Integrated Analysis of POL Family Gene Mutations in AML: Clinical Features, Prognosis, and Bioinformatics Insights

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This study investigates the role of DNA polymerases in AML to offer new insights for diagnosis and treatment. Methods A retrospective study on pediatric AML patients with POL gene family mutations from 2021 to 2024 was conducted. Patients were categorized based on risk stratification criteria, and the DAH regimen was used for induction chemotherapy. Bioinformatics analysis integrated data from various databases to identify key genes and develop survival analysis plots and AUC curves. Results The study included 59 pediatric AML patients, revealing no significant differences in demographic or clinical characteristics between those with and without POL family gene mutations. However, patients with POL gene mutations showed higher complete remission rates after initial DAH chemotherapy (91.67% vs. 59.57%, P = 0.03607), indicating a potential treatment benefit. High expression of four POL genes (POLD1, POLE, POLG, POLQ) in bone marrow and immune cells suggests their crucial role in hematopoiesis and immune response. Survival analysis across different datasets indicated that AML patients with overexpressed POL family genes had significantly worse outcomes, proposing these genes as potential prognostic biomarkers for AML. Discussion This study on pediatric AML demonstrates that POL gene family mutations are associated with higher remission rates post-chemotherapy, indicating their potential as prognostic markers. Bioinformatics analysis emphasizes the significance of these mutations in AML, highlighting their impact on disease prognosis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Acute myeloid leukemia (AML) is a clonal malignancy characterized by abnormal hematopoiesis, leading to impaired differentiation and uncontrolled proliferation of immature blasts.[ 1 ] Based on MICM identification of abnormal hematopoietic cells in the bone marrow, a relatively comprehensive treatment and follow-up mechanism has been established.[ 2 ] However, in pediatric leukemia, unlike acute lymphoblastic leukemia, the long-term prognosis of AML remains a significant challenge.[ 3 ] One reason for this is our insufficient understanding of molecular markers involved in the disease's development and progression. DNA polymerases are a class of enzymes that catalyze the polymerization of deoxyribonucleotide triphosphates (dNTPs) into progeny DNA, using parental DNA or RNA as templates. Within mammalian cells, there are predominantly five types of DNA polymerases, designated as DNA polymerase alpha (α), beta (β), gamma (γ), delta (δ), and epsilon (ε).[ 4 ] In recent years, an increasing number of studies have found that DNA polymerase is closely related to the occurrence, development, treatment, and prognosis of various tumors. Research on gastrointestinal tumors has revealed that individuals with POLE mutations are prone to developing colorectal cancer (CRC) and other gastrointestinal tumors. This mutation is likely an initiating event that upregulates the expression of immune checkpoints. Targeting this process could enhance the efficacy of immune checkpoint inhibitors in treatment.[ 5 – 8 ] In research on endometrial cancer, POLE mutations account for 7–12% of all gene mutations in endometrial cancer. In a study specifically targeting FIGO stage 3 endometrial cancer, the mutation rate can be as high as 20%.[ 9 ] Endometrial cancer patients with POLE mutations have a better prognosis, with significantly lower rates of recurrence and mortality compared to those with wild-type POLE. In high-grade endometrial cancer, patients with POLE mutations rarely experience recurrence, while the recurrence rate in wild-type POLE patients can be as high as 30.9%.[ 9 – 13 ] In bladder cancer research, it has been found that POLD1 is highly expressed in bladder cancer tissues compared to adjacent tissues. It is also more highly expressed in muscle-invasive bladder cancer than in non-muscle-invasive bladder cancer and is associated with prognosis. Further experiments conducted in vitro and in vivo have demonstrated that POLD1 promotes the proliferation and metastasis of bladder cancer by stabilizing MYC.[ 14 , 15 ] Similarly, DNA polymerase θ encoded by POLQ is closely related to the occurrence and development of various tumors. Compared to normal tissues, POLQ expression is significantly upregulated in breast cancer tissues, and high expression of POLQ is associated with poor clinical prognosis, with a 4.3-fold increased risk of death in patients with high POLQ expression.[ 16 ] Another study on breast cancer has confirmed that high POLQ expression can serve as an independent prognostic factor for breast cancer, indicating a poorer prognosis.[ 17 ] Additionally, research has found that POLQ expression in lung adenocarcinoma tissues is higher than in normal tissues.[ 18 ] Research on the POL gene family in leukemia treatment is still in its early stages. One study found that DNA polymerase theta (POLθ) can protect leukemia cells from DNA damage caused by chemotherapy drugs. In addition, the study showed that inhibitors of oncogenic tyrosine kinases (OTK) or DNA-protein crosslinks (dpc)-inducing drugs, such as etoposide, enhanced the anti-leukemia effects of POLθ inhibitors both in vitro and in vivo.[ 18 ] Furthermore, research has shown that DNA polymerase beta (POL β) exhibits increased activity in chronic myeloid leukemia (CML). During the evolution of CML from the chronic phase to the accelerated phase, an excess of POL β may contribute to the observation of genetic instability.[ 19 ] Currently, the diagnosis of AML relies mainly on the MICM classification, with particular emphasis on immunophenotyping and molecular biology examinations. Due to technological limitations, many potential abnormal genes have not yet been discovered. In recent years, with the advancement of whole transcriptome sequencing, we have been able to identify more potential gene mutations. However, due to insufficient clinical data reporting, the clinical benefits and potential mechanisms of these newly discovered gene mutations remain unclear. In this study, we have compiled information on AML patients with DNA polymerase mutations, described their basic clinical characteristics, and utilized public databases to explore the potential role of DNA polymerase in the occurrence and development of AML. Methods Clinical Data Collection We conducted a retrospective study on 12 pediatric patients diagnosed with acute myeloid leukemia (AML) carrying mutations in the POL gene family from June 1, 2021, to March 1, 2024. The control group consisted of 47 pediatric AML patients diagnosed between October 1, 2015, and December 31, 2019. Acute promyelocytic leukemia was excluded due to differences in treatment approaches. All patients provided written consent for the use of their clinical data. This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of West China Second University Hospital. Diagnostic Criteria All suspected cases must undergo morphology-immunology cytogenetics-molecular biology (MICM) diagnosis and classification, and must meet one of the following criteria: (1) Bone marrow with primitive or immature granulocytes ≥ 20%; (2) If bone marrow has < 20% primitive immature granulocytes but has specific genetic abnormalities characteristic of primary AML, such as t (8;21), inv (16); (3) Myeloid sarcoma (extramedullary myeloid tumor, granulocytic sarcoma, or chloroma), regardless of evidence of bone marrow or peripheral blood leukemia cell infiltration, must have evidence of myeloid differentiation. For myeloid sarcoma without infiltration of leukemia cells in bone marrow or peripheral blood, there must be a pathological diagnosis. Risk Stratification Newly diagnosed patients are classified into low-risk/intermediate-risk/high-risk categories based on examination results, as follows: (1) Low-risk: meeting all four criteria simultaneously: ① having one of the following favorable genetic markers: t(8;21) / AML1-ETO or RUNX1-T1RUNX1, inv(16) or t(16;16) / CBFβ-MYH11, normal karyotype with NPM1 mutation or CEBPα double mutation; ② white blood cell count (WBC) ≤ 100×10^9/L at diagnosis; ③ excluding myeloid sarcoma, central nervous system leukemia, testicular leukemia; ④ bone marrow minimal residual disease (MRD) < 10^-3 on Day 28 after the first course of induction therapy, or complete remission in bone marrow (i.e., < 5% blast cells) if MRD testing is unavailable. (2) High-risk: having one of the following factors: ① having one of the following unfavorable genetic markers: monosomy 5 or 7, 5q-, 7q-, 12p/t(2;12) / ETV6-HOXD, excluding MLL rearrangements with t(9;11), t(6;9) / DEK-NUP214 or DEK-CAN, t(7;12) / HLXB9-ETV6, t(9;22) / BCR-ABL1, t(16;21) / TLS-ERG or FUS-ERG, complex karyotype (three or more genetic abnormalities excluding favorable karyotype), c-kit mutation, FLT3-ITD mutation, RUNX1 mutation, TP53 mutation; ② transformed AML: including therapy-related AML, induced post-chemotherapy or radiotherapy AML, a rare type of leukemia related to treatment; AML transformed from myelodysplastic syndromes (MDS); ③ myeloid sarcoma; ④ bone marrow MRD ≥ 10^-2 on Day 28 after the first course of induction therapy. If MRD testing is unavailable, bone marrow blast cells ≥ 20%. Treatment Protocol All newly diagnosed patients receive the DAH regimen as the first course of induction chemotherapy, which includes Daunorubicin 40 mg/m2 every other day for 3 doses from day 1 to day 5; Ara-C, 100 mg/m2, twice daily from day 1 to day 7; Homoharringtonine, 3 mg/m2, once daily from day 1 to day 5. Bone marrow remission + MRD assessment is conducted on Day 21 and/or Day 28 of the first course of induction chemotherapy, with Day 28 as the final assessment point. If complete remission is achieved by Day 21, no assessment on Day 28 is needed. Bioinformatics analysis Data sources and websites: HPA database( https://www.proteinatlas.org ), DICE database༈ https://dice-database.org༉ , String database༈ https://string-db.org༉ , AlphaFold Protein Structure Database༈ https://alphafold.ebi.ac.uk༉,TCGA database, Target database, GEO database༈GSE37642, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138300༉ , Reactom pathyway database( https://reactome.org ), DAVID pathyway database༈ https://david.ncifcrf.gov༉ , GSEA database༈ https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#C7༉ , hipot༈ https://hiplot.com.cn/༉ Data process Utilizing data from the HPA and DICE databases, a comprehensive understanding of the POL gene family and its impact on immune cells was acquired. Expression profile and survival data from TCGA, Target, and GSE138300 were obtained for conducting survival analysis of the POL gene family, with meta-analysis yielding integrated results from diverse database sources. Through R programming, gene clusters significantly correlated with the POL gene family were identified, followed by enrichment analysis to elucidate pathways potentially influenced by mutations in the POL gene family. Subsequently, 21 key genes were identified through further using Lasso regression, leading to the generation of survival analysis plots and AUC curves. Detailed descriptions of the databases and R code have been previously disclosed in our published work.[ 20 – 22 ] Statistical analysis The statistical analysis of clinical data utilized the chi-square test. Survival analysis results were computed using the Kaplan-Meier Plotter. Genes related to the POL gene family were analyzed through the Pearson method using R code, and enrichment analysis results were obtained using DAVID. Significance levels were denoted as follows: (* for P < 0.05, ** for P < 0.01, *** for P < 0.001). Results Clinical characteristics of pediatric AML In this study, a total of 59 AML patients were included (12 with mutations in the POL family genes, and 47 patients who had not undergone comprehensive transcriptome screening for mutated genes). There were no differences in gender composition, age distribution, immunophenotyping (M0-M7), initial white blood cell levels, or risk group classification (High, Low, medium group) between the two groups. These results suggest that the POL family genes may not manifest phenotypic differences under the current MICM classification. However, it is noteworthy that after the initial induction remission phase with DAH regimen chemotherapy, a significant difference was observed in the complete remission rate upon bone marrow smear re-examination on Day 21 or Day 28 (91.67% vs. 59.57%, P = 0.03607, Table 1 ) between the two groups. This indicates the potential benefits for AML patients carrying mutations in the POL family genes following treatment. Table 1 Patient clinical characteristics between the POL family genes positive and control group POL gene positivegroup (n = 12) control group(n = 47) P vaule N % N % Gender Gender Female 6 50.00% Female 20 42.55% 0.64288 Male 6 50.00% Male 27 57.45% FAB classification FAB classification 0.36768 M0 0 0.00% M0 0 0.00% M1 0 0.00% M1 0 0.00% M2 4 33.33% M2 29 61.70% M4 4 33.33% M4 8 17.02% M5 3 25.00% M5 8 17.02% M6 0 0.00% M6 0 0.00% M7 1 8.33% M7 1 2.13% Unknown 0 0.00% Unknown 1 2.13% AGE AGE 0.45521 < 3 4 33.33% < 3 8 17.02% 3–10 4 33.33% 3–10 19 40.43% 10–15 4 33.33% 10–15 20 42.55% 15–18 0 0.00% 15–18 0 0.00% Risk group Risk group 0.10342 LR 2 16.67% LR 11 23.40% MR 2 16.67% MR 17 36.17% HR 8 66.67% HR 19 40.43% Reached CR after cycle 1 of induction Reached CR after cycle 1 of induction 0.03607* Yes 11 91.67% Yes 28 59.57% No 1 8.33% No 19 40.43% Descriptive analysis and prognosis analysis of the POL gene family Based on the whole transcriptome results, mutations in four POL family genes, POLD1, POLE, POLG, and POLQ, were detected. Therefore, we primarily analyzed these four genes. Based on the results from the HPA and DICE databases (POLD1: Fig. 1 .A1-A6, POLE: Fig. 1 .B1-B6, POLG: Fig. 1 .C1-C6, POLQ: Fig. 1 .D1-D6), all four genes are highly expressed in the bone marrow, which may be related to active hematopoietic function. Subcellular structural results indicate that POLD1 and POLQ are expressed in both the nucleus and cytoplasm, while POLE is mainly expressed in the nucleus and POLG is primarily expressed in the mitochondria. Furthermore, results from immune cell subgroups suggest that these four genes are highly expressed in different immune cells. Combined with their gene linkage results, these findings suggest that POL family genes may play an important role in various functions of immune cells, providing evidence for the involvement of POL family gene mutations in the occurrence and development of AML. Subsequently, we analyzed the impact of POL family genes on survival events in three data sources (GSE37642, n = 552; TARGET database, n = 156; TCGA database, n = 173). The results indicate that in all significant cases (Fig. 2 : A2, A3, B2, C2, C3, D1, D2), high expression of POL family genes is associated with poor prognosis, with relative hazards ranging from 1.73 to 3.26. These results suggest that POL family genes may be associated with the long-term prognosis of AML patients and can serve as potential prognostic biomarkers. Bioinformatics pathway analysis and molecular biomarker prognostic model Based on the expression profile data of GSE37642, 5135 genes are significantly associated with the POL gene family. Using TCGA expression profile data, 1275 genes are significantly associated with the POL gene family. Furthermore, based on TARGET expression profile data, 7962 genes are significantly associated with the POL gene family. The Venn diagram indicates a significant correlation of 264 genes with the POL gene family across multiple databases (Fig. 3 A). The PPI network of these genes was obtained from the String database (Fig. 3 B). Enrichment analysis results of GOBP, KEGG, and Reactome based on these genes suggest that these enriched genes mainly affect processes such as the cell cycle, DNA, and RNA metabolism (Fig. 3 C- 3 K). Subsequently, using GSE37642 data as internal data, Lasso regression was performed (Fig. 4 A, B), with a final inclusion of 21 genes in the model analysis. The model function is represented as Risk = 0.10422040PCID2 + 0.07340848EIF4A3 + 0.07215384SLC39A14 + 0.03649681PDHA1 + 0.03099477YARS + 0.01937387PLCG2 + 0.01297111SDHA + 0.01045514SCD + 0.00582637C8orf33–0.138781901RNH1–0.107139014MTMR4–0.067400210LARP1–0.058022633LIG3–0.043670449PRPF8–0.042635170E2F1–0.037351980UXS1–0.034852018NRF1–0.019641953POLR3D − 0.017298750MLEC − 0.015060838GOLGA3–0.002227719*POLR3. The AUC curve indicates an AUC value of 0.753 for the 3-year survival rate (Fig. 4 C) and an AUC value of 0.755 for the 5-year survival rate (Fig. 4 D). Survival analysis based on this Risk value suggests a significant association with survival events in AML patients (HR = 2.65 [2.14–3.29], P < 1E-16, Fig. 4 E). Furthermore, using TCGA and TARGET databases as external data for validation, the Risk value still plays a role in predicting adverse prognosis in TCGA (HR = 1.81 [1.16–2.85], P = 0.0086, Fig. 4 F) and TARGET (HR = 2.21 [1.49–3.29], P = 5.9E-05, Fig. 4 G) databases. Based on the above results, mutations in the POL family genes may lead to a downregulation of the expression levels of POL family genes, consequently resulting in a relatively favorable prognosis. Discussion In this comprehensive study, we explore the intricate relationship between DNA polymerase mutations within the POL gene family and their impact on pediatric Acute Myeloid Leukemia (AML), a formidable malignancy characterized by the aberrant proliferation of immature blood cells. Despite advancements in diagnostic and treatment strategies, the prognosis of pediatric AML remains bleak, highlighting the urgent need for a deeper molecular understanding and identification of novel prognostic markers. Our investigation commenced with the enrollment of 59 pediatric AML patients, among which 12 cases exhibited definitive POL family gene mutations. This selection was guided by burgeoning evidence indicating a significant role for DNA polymerases in the development of cancer, attributed to their essential functions in DNA replication and repair. Notably, mutations in these enzymes have been associated with various cancers, including gastrointestinal and endometrial cancers, impacting disease onset, progression, and therapeutic response. Our methodology was twofold: a detailed clinical analysis to compare demographic, clinical characteristics, and treatment outcomes between the two cohorts, and a rigorous bioinformatics approach utilizing data from public databases such as GSE, TCGA and TARGET. This allowed us to dissect the expression patterns, subcellular localization, and potential functional implications of POL gene mutations in AML. The results were revealing. Clinically, there were no significant differences in gender distribution, age at diagnosis, or initial white blood cell count between patients with and without POL gene mutations. However, a striking divergence emerged in treatment outcomes. Patients harboring POL gene mutations demonstrated a markedly higher complete remission rate following the initial induction chemotherapy phase compared to their mutation-negative counterparts (91.67% vs. 59.57%, P = 0.03607), suggesting a potential therapeutic advantage for this subgroup. Bioinformatics analyses have significantly deepened our understanding. Mutations were identified in four pivotal POL genes: POLD1, POLE, POLG, and POLQ. These genes were found to have elevated expression levels in the bone marrow, suggesting their critical involvement in hematopoiesis. Furthermore, their expression across various immune cell types indicated a more expansive role in immune regulation. Intriguingly, survival analysis leveraging data from GSE, TCGA, and TARGET datasets demonstrated that AML patients with high expression of POL family genes experienced notably poorer outcomes compared to those without such mutations, highlighting the prognostic significance of these genetic alterations. This implies that the presence of POL family gene mutations might impair the expression levels of POL family genes, thereby exerting a protective effect. The potential underlying mechanisms could involve pathways related to the cell cycle and the metabolism of DNA and RNA. In our study, although we have unveiled the potential role of DNA polymerase family genes in pediatric Acute Myeloid Leukemia (AML), there are several limitations to our research: 1) Due to the typically longer follow-up periods required for pediatric AML patients compared to adults, our single-center study is currently unable to provide comprehensive long-term prognostic data. Thus, a longer follow-up period is necessary to obtain more complete prognostic information, which is crucial for understanding the impact of POL family gene mutations on the prognosis of pediatric AML patients. 2) Our study relies on bioinformatics analysis to explore the role of the POL gene family in AML, but lacks further experimental validation. Although this is the first analysis of the potential impact of POL family genes in AML research, and we have proposed a prognostic model for risk assessment, the accuracy of bioinformatics results may be questioned due to the absence of experimental data. This highlights the importance of future laboratory validation work to ensure that our findings are biologically reliable. 3) Our study is based on a relatively small cohort, which may limit the generalizability and statistical power of our results. Especially when exploring the impact of rare gene mutations on disease, a larger sample size would help enhance the credibility and… In summary, our study highlights the critical role of POL gene mutations in pediatric AML, not only influencing treatment response but also bearing significant prognostic implications. These findings pave the way for future research into targeted therapies that could exploit these molecular vulnerabilities, offering hope for improved management strategies in this challenging pediatric malignancy. Declarations Ethics declaration: this study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of West China Second University Hospital. All subjects gave their informed consent for inclusion before they participated in the study. Funding: Not applicable. Conflicts of interest: All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare. Author contributions: Chaoban Wang, Ju Gao, Xia Guo: Conception and design, Collection and assembly of data, Data analysis and interpretation, Administrative support; Jianrong Wu，Wenhao Tang: Collection and assembly of data; Data analysis and interpretation; All authors: Manuscript writing and final approval of the manuscript. # Jianrong Wu and Chaoban Wang have equal contributions on this paper. References Prada-Arismendy J, Arroyave JC, Röthlisberger S: Molecular biomarkers in acute myeloid leukemia . Blood Reviews 2017, 31 (1):63-76. Creutzig U, van den Heuvel-Eibrink MM, Gibson B, Dworzak MN, Adachi S, de Bont E, Harbott J, Hasle H, Johnston D, Kinoshita A et al : Diagnosis and management of acute myeloid leukemia in children and adolescents: recommendations from an international expert panel . Blood 2012, 120 (16):3187-3205. Ksiazek T, Czogala M, Kaczowka P, Sadowska B, Pawinska-Wasikowska K, Bik-Multanowski M, Sikorska-Fic B, Matysiak M, Skalska-Sadowska J, Wachowiak J et al : High Frequency of Fusion Gene Transcript Resulting From t(10;11)(p12;q23) Translocation in Pediatric Acute Myeloid Leukemia in Poland . Front Pediatr 2020, 8 :278. Loeb LA, Monnat RJ, Jr.: DNA polymerases and human disease . Nat Rev Genet 2008, 9 (8):594-604. Temko D, Van Gool IC, Rayner E, Glaire M, Makino S, Brown M, Chegwidden L, Palles C, Depreeuw J, Beggs A et al : Somatic POLE exonuclease domain mutations are early events in sporadic endometrial and colorectal carcinogenesis, determining driver mutational landscape, clonal neoantigen burden and immune response . J Pathol 2018, 245 (3):283-296. Rosner G, Gluck N, Carmi S, Bercovich D, Fliss-Issakov N, Ben-Yehoyada M, Aharon-Caspi S, Kellerman E, Strul H, Shibolet O et al : POLD1 and POLE Gene Mutations in Jewish Cohorts of Early-Onset Colorectal Cancer and of Multiple Colorectal Adenomas . Dis Colon Rectum 2018, 61 (9):1073-1079. Vande Perre P, Siegfried A, Corsini C, Bonnet D, Toulas C, Hamzaoui N, Selves J, Chipoulet E, Hoffmann JS, Uro-Coste E et al : Germline mutation p.N363K in POLE is associated with an increased risk of colorectal cancer and giant cell glioblastoma . Fam Cancer 2019, 18 (2):173-178. Hino H, Shiomi A, Kusuhara M, Kagawa H, Yamakawa Y, Hatakeyama K, Kawabata T, Oishi T, Urakami K, Nagashima T et al : Clinicopathological and mutational analyses of colorectal cancer with mutations in the POLE gene . Cancer Med 2019, 8 (10):4587-4597. Stasenko M, Tunnage I, Ashley CW, Rubinstein MM, Latham AJ, Da Cruz Paula A, Mueller JJ, Leitao MM, Jr., Friedman CF, Makker V et al : Clinical outcomes of patients with POLE mutated endometrioid endometrial cancer . Gynecol Oncol 2020, 156 (1):194-202. Meng B, Hoang LN, McIntyre JB, Duggan MA, Nelson GS, Lee CH, Köbel M: POLE exonuclease domain mutation predicts long progression-free survival in grade 3 endometrioid carcinoma of the endometrium . Gynecol Oncol 2014, 134 (1):15-19. Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC et al : Integrated genomic characterization of endometrial carcinoma . Nature 2013, 497 (7447):67-73. Church DN, Stelloo E, Nout RA, Valtcheva N, Depreeuw J, ter Haar N, Noske A, Amant F, Tomlinson IP, Wild PJ et al : Prognostic significance of POLE proofreading mutations in endometrial cancer . J Natl Cancer Inst 2015, 107 (1):402. Hussein YR, Weigelt B, Levine DA, Schoolmeester JK, Dao LN, Balzer BL, Liles G, Karlan B, Köbel M, Lee CH et al : Clinicopathological analysis of endometrial carcinomas harboring somatic POLE exonuclease domain mutations . Mod Pathol 2015, 28 (4):505-514. Wang Y, Ju L, Wang G, Qian K, Jin W, Li M, Yu J, Shi Y, Wang Y, Zhang Y et al : DNA polymerase POLD1 promotes proliferation and metastasis of bladder cancer by stabilizing MYC . Nat Commun 2023, 14 (1):2421. Wang Y, Ju L, Guo Z, Qian K, Chen H, Zhang Y, Xiao Y, Wang X: Pedigree analysis of a POLD1 germline mutation in urothelial carcinoma shows a close association between different mutation burdens and overall survival . Cell Mol Immunol 2021, 18 (3):767-769. Lemée F, Bergoglio V, Fernandez-Vidal A, Machado-Silva A, Pillaire MJ, Bieth A, Gentil C, Baker L, Martin AL, Leduc C et al : DNA polymerase theta up-regulation is associated with poor survival in breast cancer, perturbs DNA replication, and promotes genetic instability . Proc Natl Acad Sci U S A 2010, 107 (30):13390-13395. Higgins GS, Harris AL, Prevo R, Helleday T, McKenna WG, Buffa FM: Overexpression of POLQ confers a poor prognosis in early breast cancer patients . Oncotarget 2010, 1 (3):175-184. Shinmura K, Kato H, Kawanishi Y, Yoshimura K, Tsuchiya K, Takahara Y, Hosokawa S, Kawase A, Funai K, Sugimura H: POLQ Overexpression Is Associated with an Increased Somatic Mutation Load and PLK4 Overexpression in Lung Adenocarcinoma . Cancers (Basel) 2019, 11 (5). Canitrot Y, Laurent G, Astarie-Dequeker C, Bordier C, Cazaux C, Hoffmann JS: Enhanced expression and activity of DNA polymerase beta in chronic myelogenous leukemia . Anticancer Res 2006, 26 (1b):523-525. Wu H, Mo Y, Yu S, Ye X, Lu Y, Wang C, Shan X: Novel homozygous mutations in AIRE leading to APS-1 and potential mechanisms based on bioinformatics analysis . Heliyon 2024, 10 (6):e28037. Wu H, Wang C, Yu S, Ye X, Jiang Y, He P, Shan X: Downregulation of ACAN is Associated with the Growth hormone pathway and Induces short stature . J Clin Lab Anal 2023, 37 (2):e24830. Wang C, Yu S, Qian R, Chen S, Dai C, Shan X: Prognostic and immunological significance of peroxisome proliferator-activated receptor gamma in hepatocellular carcinoma based on multiple databases . Transl Cancer Res 2022, 11 (7):1938-1953. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4737536","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":333754722,"identity":"cde05ea4-4d87-417d-8999-446601f14cfa","order_by":0,"name":"Jianrong Wu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jianrong","middleName":"","lastName":"Wu","suffix":""},{"id":333754723,"identity":"62642abf-5c6b-437b-a014-251c57722c9f","order_by":1,"name":"chaoban wang","email":"","orcid":"","institution":"Sichuan University, Ministry of Education","correspondingAuthor":false,"prefix":"","firstName":"chaoban","middleName":"","lastName":"wang","suffix":""},{"id":333754724,"identity":"d63dcbb4-a736-42a5-a3e2-4445721423f2","order_by":2,"name":"Wenhao Tang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Tang","suffix":""},{"id":333754725,"identity":"a49903fe-8bc7-43dd-a0b7-c732167fd5db","order_by":3,"name":"Ju Gao","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Ju","middleName":"","lastName":"Gao","suffix":""},{"id":333754726,"identity":"381526d6-f013-43a7-b7e4-756cf7ec5c30","order_by":4,"name":"Xia Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYLCCBAYbOQiLjXgtacYkamFgOJzYQLQW+YjkYxIPapjT57efMWD4UHaYgX92A34thjfSkg0SjrHlbjiTY8A449xhBok7BwhomZFj+CCBjSd3gwSPATNv22EGA4kEgloMDiT8k0iXnwHU8pcYLfISQFsS2wwSGG4AtTASo8WA51myQWJfguGGM2kFB3vOpfNI3CBkS3vyMckf3/7Ly7cf3vjgR5m1HP8MQrYcQOKA2Dz41YNsaSCoZBSMglEwCkY8AADZYkCuk/L4LgAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xia","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-07-14 08:02:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4737536/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4737536/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62651296,"identity":"895e5c23-5145-4ca9-99e7-0b611ef61bb2","added_by":"auto","created_at":"2024-08-17 00:49:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3639488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBasic features of the POL gene family. \u003c/strong\u003e1) POLD1: A1, human body distribution map; A2, tissue organ expression levels; A3, immunofluorescence localization map of POLD1 (red cytoplasm, blue nucleus, green POLD1); A4, subcellular structure map of POLD1; A5, expression levels of POLD1 in various types of immune cells; A6, network map of POLD1 and associated genes. 2) POLE (B1-B6). 3) POLG (C1-C6). 4) POLQ (D1-D6).\u003c/p\u003e","description":"","filename":"FIgure11.png","url":"https://assets-eu.researchsquare.com/files/rs-4737536/v1/6666f069b31977c1480c60ca.png"},{"id":62651295,"identity":"73382fb7-7b87-4834-b839-6d487e23d1b6","added_by":"auto","created_at":"2024-08-17 00:49:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":464692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis of the POL gene family in the GSE37642, TARGET, and TCGA databases\u003c/strong\u003e: 1) POLD1 (A1: GSE37642, A2: TARGET, A3: TCGA); 2) POLE (B1: GSE37642, B2: TARGET, B3: TCGA); 3) POLG (C1: GSE37642, C2: TARGET, C3: TCGA); 4) POLQ (D1: GSE37642, D2: TARGET, D3: TCGA).\u003c/p\u003e","description":"","filename":"FIgure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4737536/v1/8140afb98f54e46ed8083f49.png"},{"id":62651918,"identity":"04e1db19-7928-4968-81d0-2ecb87b2ac3b","added_by":"auto","created_at":"2024-08-17 00:57:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4747713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBioinformatics analysis of the POL gene family. \u003c/strong\u003e1) A, Venn diagram, 264 common genes; 2) B, PPI network diagram; 3) C, Reactome enrichment analysis; 4) GOBP enrichment analysis (D, bubble chart, E, pathway-gene chart, F, gene network diagram, J, dendrogram network diagram); 5) KEGG enrichment analysis (G, bubble chart, H, pathway-gene chart, I, gene network diagram, K, dendrogram network diagram).\u003c/p\u003e","description":"","filename":"FIgure31.png","url":"https://assets-eu.researchsquare.com/files/rs-4737536/v1/5e99052c9d88a204a68aa825.png"},{"id":62651297,"identity":"043f9598-e8de-4783-b5ae-51a15a4ff05b","added_by":"auto","created_at":"2024-08-17 00:49:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":845184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLasso regression and risk-survival analysis plot.\u003c/strong\u003e 1) Lasso regression plot (A, B); 2) AUC curve (3-year expected survival period C, 5-year expected survival period D); 3) Survival curve based on Risk value (E: GSE37642, F: TARGET, G: TCGA).\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-4737536/v1/8bbe34dbac4e399010595934.png"},{"id":62653546,"identity":"b7315fac-dbb9-4413-820c-3dc99079057e","added_by":"auto","created_at":"2024-08-17 01:13:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11353139,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4737536/v1/53ff8cd5-1cab-4cfd-a373-cb2a4c806300.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Analysis of POL Family Gene Mutations in AML: Clinical Features, Prognosis, and Bioinformatics Insights","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a clonal malignancy characterized by abnormal hematopoiesis, leading to impaired differentiation and uncontrolled proliferation of immature blasts.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Based on MICM identification of abnormal hematopoietic cells in the bone marrow, a relatively comprehensive treatment and follow-up mechanism has been established.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] However, in pediatric leukemia, unlike acute lymphoblastic leukemia, the long-term prognosis of AML remains a significant challenge.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] One reason for this is our insufficient understanding of molecular markers involved in the disease's development and progression.\u003c/p\u003e \u003cp\u003eDNA polymerases are a class of enzymes that catalyze the polymerization of deoxyribonucleotide triphosphates (dNTPs) into progeny DNA, using parental DNA or RNA as templates. Within mammalian cells, there are predominantly five types of DNA polymerases, designated as DNA polymerase alpha (α), beta (β), gamma (γ), delta (δ), and epsilon (ε).[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] In recent years, an increasing number of studies have found that DNA polymerase is closely related to the occurrence, development, treatment, and prognosis of various tumors. Research on gastrointestinal tumors has revealed that individuals with POLE mutations are prone to developing colorectal cancer (CRC) and other gastrointestinal tumors. This mutation is likely an initiating event that upregulates the expression of immune checkpoints. Targeting this process could enhance the efficacy of immune checkpoint inhibitors in treatment.[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] In research on endometrial cancer, POLE mutations account for 7\u0026ndash;12% of all gene mutations in endometrial cancer. In a study specifically targeting FIGO stage 3 endometrial cancer, the mutation rate can be as high as 20%.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Endometrial cancer patients with POLE mutations have a better prognosis, with significantly lower rates of recurrence and mortality compared to those with wild-type POLE. In high-grade endometrial cancer, patients with POLE mutations rarely experience recurrence, while the recurrence rate in wild-type POLE patients can be as high as 30.9%.[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] In bladder cancer research, it has been found that POLD1 is highly expressed in bladder cancer tissues compared to adjacent tissues. It is also more highly expressed in muscle-invasive bladder cancer than in non-muscle-invasive bladder cancer and is associated with prognosis. Further experiments conducted in vitro and in vivo have demonstrated that POLD1 promotes the proliferation and metastasis of bladder cancer by stabilizing MYC.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSimilarly, DNA polymerase θ encoded by POLQ is closely related to the occurrence and development of various tumors. Compared to normal tissues, POLQ expression is significantly upregulated in breast cancer tissues, and high expression of POLQ is associated with poor clinical prognosis, with a 4.3-fold increased risk of death in patients with high POLQ expression.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Another study on breast cancer has confirmed that high POLQ expression can serve as an independent prognostic factor for breast cancer, indicating a poorer prognosis.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Additionally, research has found that POLQ expression in lung adenocarcinoma tissues is higher than in normal tissues.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eResearch on the POL gene family in leukemia treatment is still in its early stages. One study found that DNA polymerase theta (POLθ) can protect leukemia cells from DNA damage caused by chemotherapy drugs. In addition, the study showed that inhibitors of oncogenic tyrosine kinases (OTK) or DNA-protein crosslinks (dpc)-inducing drugs, such as etoposide, enhanced the anti-leukemia effects of POLθ inhibitors both in vitro and in vivo.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Furthermore, research has shown that DNA polymerase beta (POL β) exhibits increased activity in chronic myeloid leukemia (CML). During the evolution of CML from the chronic phase to the accelerated phase, an excess of POL β may contribute to the observation of genetic instability.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eCurrently, the diagnosis of AML relies mainly on the MICM classification, with particular emphasis on immunophenotyping and molecular biology examinations. Due to technological limitations, many potential abnormal genes have not yet been discovered. In recent years, with the advancement of whole transcriptome sequencing, we have been able to identify more potential gene mutations. However, due to insufficient clinical data reporting, the clinical benefits and potential mechanisms of these newly discovered gene mutations remain unclear.\u003c/p\u003e \u003cp\u003eIn this study, we have compiled information on AML patients with DNA polymerase mutations, described their basic clinical characteristics, and utilized public databases to explore the potential role of DNA polymerase in the occurrence and development of AML.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical Data Collection\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective study on 12 pediatric patients diagnosed with acute myeloid leukemia (AML) carrying mutations in the POL gene family from June 1, 2021, to March 1, 2024. The control group consisted of 47 pediatric AML patients diagnosed between October 1, 2015, and December 31, 2019. Acute promyelocytic leukemia was excluded due to differences in treatment approaches. All patients provided written consent for the use of their clinical data. This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of West China Second University Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Criteria\u003c/h2\u003e \u003cp\u003eAll suspected cases must undergo morphology-immunology cytogenetics-molecular biology (MICM) diagnosis and classification, and must meet one of the following criteria:\u003c/p\u003e \u003cp\u003e(1) Bone marrow with primitive or immature granulocytes\u0026thinsp;\u0026ge;\u0026thinsp;20%;\u003c/p\u003e \u003cp\u003e(2) If bone marrow has \u0026lt;\u0026thinsp;20% primitive immature granulocytes but has specific genetic abnormalities characteristic of primary AML, such as t (8;21), inv (16);\u003c/p\u003e \u003cp\u003e(3) Myeloid sarcoma (extramedullary myeloid tumor, granulocytic sarcoma, or chloroma), regardless of evidence of bone marrow or peripheral blood leukemia cell infiltration, must have evidence of myeloid differentiation. For myeloid sarcoma without infiltration of leukemia cells in bone marrow or peripheral blood, there must be a pathological diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRisk Stratification\u003c/h2\u003e \u003cp\u003eNewly diagnosed patients are classified into low-risk/intermediate-risk/high-risk categories based on examination results, as follows:\u003c/p\u003e \u003cp\u003e(1) Low-risk: meeting all four criteria simultaneously: ① having one of the following favorable genetic markers: t(8;21) / AML1-ETO or RUNX1-T1RUNX1, inv(16) or t(16;16) / CBFβ-MYH11, normal karyotype with NPM1 mutation or CEBPα double mutation; ② white blood cell count (WBC)\u0026thinsp;\u0026le;\u0026thinsp;100\u0026times;10^9/L at diagnosis; ③ excluding myeloid sarcoma, central nervous system leukemia, testicular leukemia; ④ bone marrow minimal residual disease (MRD)\u0026thinsp;\u0026lt;\u0026thinsp;10^-3 on Day 28 after the first course of induction therapy, or complete remission in bone marrow (i.e., \u0026lt;\u0026thinsp;5% blast cells) if MRD testing is unavailable.\u003c/p\u003e \u003cp\u003e(2) High-risk: having one of the following factors: ① having one of the following unfavorable genetic markers: monosomy 5 or 7, 5q-, 7q-, 12p/t(2;12) / ETV6-HOXD, excluding MLL rearrangements with t(9;11), t(6;9) / DEK-NUP214 or DEK-CAN, t(7;12) / HLXB9-ETV6, t(9;22) / BCR-ABL1, t(16;21) / TLS-ERG or FUS-ERG, complex karyotype (three or more genetic abnormalities excluding favorable karyotype), c-kit mutation, FLT3-ITD mutation, RUNX1 mutation, TP53 mutation; ② transformed AML: including therapy-related AML, induced post-chemotherapy or radiotherapy AML, a rare type of leukemia related to treatment; AML transformed from myelodysplastic syndromes (MDS); ③ myeloid sarcoma; ④ bone marrow MRD\u0026thinsp;\u0026ge;\u0026thinsp;10^-2 on Day 28 after the first course of induction therapy. If MRD testing is unavailable, bone marrow blast cells\u0026thinsp;\u0026ge;\u0026thinsp;20%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTreatment Protocol\u003c/h2\u003e \u003cp\u003eAll newly diagnosed patients receive the DAH regimen as the first course of induction chemotherapy, which includes Daunorubicin 40 mg/m2 every other day for 3 doses from day 1 to day 5; Ara-C, 100 mg/m2, twice daily from day 1 to day 7; Homoharringtonine, 3 mg/m2, once daily from day 1 to day 5. Bone marrow remission\u0026thinsp;+\u0026thinsp;MRD assessment is conducted on Day 21 and/or Day 28 of the first course of induction chemotherapy, with Day 28 as the final assessment point. If complete remission is achieved by Day 21, no assessment on Day 28 is needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eData sources and websites:\u003c/h2\u003e \u003cp\u003eHPA database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), DICE database༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dice-database.org༉\u003c/span\u003e\u003cspan address=\"https://dice-database.org༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, String database༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org༉\u003c/span\u003e\u003cspan address=\"https://string-db.org༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, AlphaFold Protein Structure Database༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk༉,TCGA\u003c/span\u003e\u003cspan address=\"https://alphafold.ebi.ac.uk༉,TCGA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e database, Target database, GEO database༈GSE37642, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138300༉\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138300༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Reactom pathyway database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org\u003c/span\u003e\u003cspan address=\"https://reactome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), DAVID pathyway database༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov༉\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, GSEA database༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#C7༉\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#C7༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, hipot༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hiplot.com.cn/༉\u003c/span\u003e\u003cspan address=\"https://hiplot.com.cn/༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData process\u003c/h2\u003e \u003cp\u003eUtilizing data from the HPA and DICE databases, a comprehensive understanding of the POL gene family and its impact on immune cells was acquired. Expression profile and survival data from TCGA, Target, and GSE138300 were obtained for conducting survival analysis of the POL gene family, with meta-analysis yielding integrated results from diverse database sources. Through R programming, gene clusters significantly correlated with the POL gene family were identified, followed by enrichment analysis to elucidate pathways potentially influenced by mutations in the POL gene family. Subsequently, 21 key genes were identified through further using Lasso regression, leading to the generation of survival analysis plots and AUC curves.\u003c/p\u003e \u003cp\u003eDetailed descriptions of the databases and R code have been previously disclosed in our published work.[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis of clinical data utilized the chi-square test. Survival analysis results were computed using the Kaplan-Meier Plotter. Genes related to the POL gene family were analyzed through the Pearson method using R code, and enrichment analysis results were obtained using DAVID. Significance levels were denoted as follows: (* for P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** for P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** for P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of pediatric AML\u003c/h2\u003e \u003cp\u003eIn this study, a total of 59 AML patients were included (12 with mutations in the POL family genes, and 47 patients who had not undergone comprehensive transcriptome screening for mutated genes). There were no differences in gender composition, age distribution, immunophenotyping (M0-M7), initial white blood cell levels, or risk group classification (High, Low, medium group) between the two groups. These results suggest that the POL family genes may not manifest phenotypic differences under the current MICM classification. However, it is noteworthy that after the initial induction remission phase with DAH regimen chemotherapy, a significant difference was observed in the complete remission rate upon bone marrow smear re-examination on Day 21 or Day 28 (91.67% vs. 59.57%, P\u0026thinsp;=\u0026thinsp;0.03607, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) between the two groups. This indicates the potential benefits for AML patients carrying mutations in the POL family genes following treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient clinical characteristics between the POL family genes positive and control group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePOL gene positivegroup (n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003econtrol group(n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP vaule\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFAB classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFAB classification\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAGE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.45521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRisk group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReached CR after cycle 1 of induction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eReached CR after cycle 1 of induction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.03607*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis and prognosis analysis of the POL gene family\u003c/h2\u003e \u003cp\u003eBased on the whole transcriptome results, mutations in four POL family genes, POLD1, POLE, POLG, and POLQ, were detected. Therefore, we primarily analyzed these four genes. Based on the results from the HPA and DICE databases (POLD1: Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A1-A6, POLE: Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.B1-B6, POLG: Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.C1-C6, POLQ: Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.D1-D6), all four genes are highly expressed in the bone marrow, which may be related to active hematopoietic function. Subcellular structural results indicate that POLD1 and POLQ are expressed in both the nucleus and cytoplasm, while POLE is mainly expressed in the nucleus and POLG is primarily expressed in the mitochondria. Furthermore, results from immune cell subgroups suggest that these four genes are highly expressed in different immune cells. Combined with their gene linkage results, these findings suggest that POL family genes may play an important role in various functions of immune cells, providing evidence for the involvement of POL family gene mutations in the occurrence and development of AML.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, we analyzed the impact of POL family genes on survival events in three data sources (GSE37642, n\u0026thinsp;=\u0026thinsp;552; TARGET database, n\u0026thinsp;=\u0026thinsp;156; TCGA database, n\u0026thinsp;=\u0026thinsp;173). The results indicate that in all significant cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: A2, A3, B2, C2, C3, D1, D2), high expression of POL family genes is associated with poor prognosis, with relative hazards ranging from 1.73 to 3.26. These results suggest that POL family genes may be associated with the long-term prognosis of AML patients and can serve as potential prognostic biomarkers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics pathway analysis and molecular biomarker prognostic model\u003c/h2\u003e \u003cp\u003eBased on the expression profile data of GSE37642, 5135 genes are significantly associated with the POL gene family. Using TCGA expression profile data, 1275 genes are significantly associated with the POL gene family. Furthermore, based on TARGET expression profile data, 7962 genes are significantly associated with the POL gene family. The Venn diagram indicates a significant correlation of 264 genes with the POL gene family across multiple databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The PPI network of these genes was obtained from the String database (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Enrichment analysis results of GOBP, KEGG, and Reactome based on these genes suggest that these enriched genes mainly affect processes such as the cell cycle, DNA, and RNA metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, using GSE37642 data as internal data, Lasso regression was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), with a final inclusion of 21 genes in the model analysis. The model function is represented as Risk\u0026thinsp;=\u0026thinsp;0.10422040PCID2\u0026thinsp;+\u0026thinsp;0.07340848EIF4A3\u0026thinsp;+\u0026thinsp;0.07215384SLC39A14\u0026thinsp;+\u0026thinsp;0.03649681PDHA1\u0026thinsp;+\u0026thinsp;0.03099477YARS\u0026thinsp;+\u0026thinsp;0.01937387PLCG2\u0026thinsp;+\u0026thinsp;0.01297111SDHA\u0026thinsp;+\u0026thinsp;0.01045514SCD\u0026thinsp;+\u0026thinsp;0.00582637C8orf33\u0026ndash;0.138781901RNH1\u0026ndash;0.107139014MTMR4\u0026ndash;0.067400210LARP1\u0026ndash;0.058022633LIG3\u0026ndash;0.043670449PRPF8\u0026ndash;0.042635170E2F1\u0026ndash;0.037351980UXS1\u0026ndash;0.034852018NRF1\u0026ndash;0.019641953POLR3D \u0026minus;\u0026thinsp;0.017298750MLEC \u0026minus;\u0026thinsp;0.015060838GOLGA3\u0026ndash;0.002227719*POLR3. The AUC curve indicates an AUC value of 0.753 for the 3-year survival rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and an AUC value of 0.755 for the 5-year survival rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Survival analysis based on this Risk value suggests a significant association with survival events in AML patients (HR\u0026thinsp;=\u0026thinsp;2.65 [2.14\u0026ndash;3.29], P\u0026thinsp;\u0026lt;\u0026thinsp;1E-16, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Furthermore, using TCGA and TARGET databases as external data for validation, the Risk value still plays a role in predicting adverse prognosis in TCGA (HR\u0026thinsp;=\u0026thinsp;1.81 [1.16\u0026ndash;2.85], P\u0026thinsp;=\u0026thinsp;0.0086, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) and TARGET (HR\u0026thinsp;=\u0026thinsp;2.21 [1.49\u0026ndash;3.29], P\u0026thinsp;=\u0026thinsp;5.9E-05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) databases. Based on the above results, mutations in the POL family genes may lead to a downregulation of the expression levels of POL family genes, consequently resulting in a relatively favorable prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this comprehensive study, we explore the intricate relationship between DNA polymerase mutations within the POL gene family and their impact on pediatric Acute Myeloid Leukemia (AML), a formidable malignancy characterized by the aberrant proliferation of immature blood cells. Despite advancements in diagnostic and treatment strategies, the prognosis of pediatric AML remains bleak, highlighting the urgent need for a deeper molecular understanding and identification of novel prognostic markers.\u003c/p\u003e \u003cp\u003eOur investigation commenced with the enrollment of 59 pediatric AML patients, among which 12 cases exhibited definitive POL family gene mutations. This selection was guided by burgeoning evidence indicating a significant role for DNA polymerases in the development of cancer, attributed to their essential functions in DNA replication and repair. Notably, mutations in these enzymes have been associated with various cancers, including gastrointestinal and endometrial cancers, impacting disease onset, progression, and therapeutic response.\u003c/p\u003e \u003cp\u003eOur methodology was twofold: a detailed clinical analysis to compare demographic, clinical characteristics, and treatment outcomes between the two cohorts, and a rigorous bioinformatics approach utilizing data from public databases such as GSE, TCGA and TARGET. This allowed us to dissect the expression patterns, subcellular localization, and potential functional implications of POL gene mutations in AML.\u003c/p\u003e \u003cp\u003eThe results were revealing. Clinically, there were no significant differences in gender distribution, age at diagnosis, or initial white blood cell count between patients with and without POL gene mutations. However, a striking divergence emerged in treatment outcomes. Patients harboring POL gene mutations demonstrated a markedly higher complete remission rate following the initial induction chemotherapy phase compared to their mutation-negative counterparts (91.67% vs. 59.57%, P = 0.03607), suggesting a potential therapeutic advantage for this subgroup.\u003c/p\u003e \u003cp\u003eBioinformatics analyses have significantly deepened our understanding. Mutations were identified in four pivotal POL genes: POLD1, POLE, POLG, and POLQ. These genes were found to have elevated expression levels in the bone marrow, suggesting their critical involvement in hematopoiesis. Furthermore, their expression across various immune cell types indicated a more expansive role in immune regulation. Intriguingly, survival analysis leveraging data from GSE, TCGA, and TARGET datasets demonstrated that AML patients with high expression of POL family genes experienced notably poorer outcomes compared to those without such mutations, highlighting the prognostic significance of these genetic alterations. This implies that the presence of POL family gene mutations might impair the expression levels of POL family genes, thereby exerting a protective effect. The potential underlying mechanisms could involve pathways related to the cell cycle and the metabolism of DNA and RNA.\u003c/p\u003e \u003cp\u003eIn our study, although we have unveiled the potential role of DNA polymerase family genes in pediatric Acute Myeloid Leukemia (AML), there are several limitations to our research: 1) Due to the typically longer follow-up periods required for pediatric AML patients compared to adults, our single-center study is currently unable to provide comprehensive long-term prognostic data. Thus, a longer follow-up period is necessary to obtain more complete prognostic information, which is crucial for understanding the impact of POL family gene mutations on the prognosis of pediatric AML patients. 2) Our study relies on bioinformatics analysis to explore the role of the POL gene family in AML, but lacks further experimental validation. Although this is the first analysis of the potential impact of POL family genes in AML research, and we have proposed a prognostic model for risk assessment, the accuracy of bioinformatics results may be questioned due to the absence of experimental data. This highlights the importance of future laboratory validation work to ensure that our findings are biologically reliable. 3) Our study is based on a relatively small cohort, which may limit the generalizability and statistical power of our results. Especially when exploring the impact of rare gene mutations on disease, a larger sample size would help enhance the credibility and…\u003c/p\u003e \u003cp\u003eIn summary, our study highlights the critical role of POL gene mutations in pediatric AML, not only influencing treatment response but also bearing significant prognostic implications. These findings pave the way for future research into targeted therapies that could exploit these molecular vulnerabilities, offering hope for improved management strategies in this challenging pediatric malignancy.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eEthics declaration: this study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of West China Second University Hospital. All subjects gave their informed consent for inclusion before they participated in the study.\u003c/p\u003e\n\u003cp\u003eFunding: Not applicable.\u003c/p\u003e\n\u003cp\u003eConflicts of interest: All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003eAuthor contributions: Chaoban Wang, Ju Gao, Xia Guo: Conception and design, Collection and assembly of data, Data analysis and interpretation, Administrative support; Jianrong Wu，Wenhao Tang: Collection and assembly of data; Data analysis and interpretation; All authors: Manuscript writing and final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e# Jianrong Wu and Chaoban Wang have equal contributions on this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrada-Arismendy J, Arroyave JC, R\u0026ouml;thlisberger S: \u003cstrong\u003eMolecular biomarkers in acute myeloid leukemia\u003c/strong\u003e. \u003cem\u003eBlood Reviews \u003c/em\u003e2017, \u003cstrong\u003e31\u003c/strong\u003e(1):63-76.\u003c/li\u003e\n\u003cli\u003eCreutzig U, van den Heuvel-Eibrink MM, Gibson B, Dworzak MN, Adachi S, de Bont E, Harbott J, Hasle H, Johnston D, Kinoshita A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDiagnosis and management of acute myeloid leukemia in children and adolescents: recommendations from an international expert panel\u003c/strong\u003e. \u003cem\u003eBlood \u003c/em\u003e2012, \u003cstrong\u003e120\u003c/strong\u003e(16):3187-3205.\u003c/li\u003e\n\u003cli\u003eKsiazek T, Czogala M, Kaczowka P, Sadowska B, Pawinska-Wasikowska K, Bik-Multanowski M, Sikorska-Fic B, Matysiak M, Skalska-Sadowska J, Wachowiak J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eHigh Frequency of Fusion Gene Transcript Resulting From t(10;11)(p12;q23) Translocation in Pediatric Acute Myeloid Leukemia in Poland\u003c/strong\u003e. \u003cem\u003eFront Pediatr \u003c/em\u003e2020, \u003cstrong\u003e8\u003c/strong\u003e:278.\u003c/li\u003e\n\u003cli\u003eLoeb LA, Monnat RJ, Jr.: \u003cstrong\u003eDNA polymerases and human disease\u003c/strong\u003e. \u003cem\u003eNat Rev Genet \u003c/em\u003e2008, \u003cstrong\u003e9\u003c/strong\u003e(8):594-604.\u003c/li\u003e\n\u003cli\u003eTemko D, Van Gool IC, Rayner E, Glaire M, Makino S, Brown M, Chegwidden L, Palles C, Depreeuw J, Beggs A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eSomatic POLE exonuclease domain mutations are early events in sporadic endometrial and colorectal carcinogenesis, determining driver mutational landscape, clonal neoantigen burden and immune response\u003c/strong\u003e. \u003cem\u003eJ Pathol \u003c/em\u003e2018, \u003cstrong\u003e245\u003c/strong\u003e(3):283-296.\u003c/li\u003e\n\u003cli\u003eRosner G, Gluck N, Carmi S, Bercovich D, Fliss-Issakov N, Ben-Yehoyada M, Aharon-Caspi S, Kellerman E, Strul H, Shibolet O\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePOLD1 and POLE Gene Mutations in Jewish Cohorts of Early-Onset Colorectal Cancer and of Multiple Colorectal Adenomas\u003c/strong\u003e. \u003cem\u003eDis Colon Rectum \u003c/em\u003e2018, \u003cstrong\u003e61\u003c/strong\u003e(9):1073-1079.\u003c/li\u003e\n\u003cli\u003eVande Perre P, Siegfried A, Corsini C, Bonnet D, Toulas C, Hamzaoui N, Selves J, Chipoulet E, Hoffmann JS, Uro-Coste E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGermline mutation p.N363K in POLE is associated with an increased risk of colorectal cancer and giant cell glioblastoma\u003c/strong\u003e. \u003cem\u003eFam Cancer \u003c/em\u003e2019, \u003cstrong\u003e18\u003c/strong\u003e(2):173-178.\u003c/li\u003e\n\u003cli\u003eHino H, Shiomi A, Kusuhara M, Kagawa H, Yamakawa Y, Hatakeyama K, Kawabata T, Oishi T, Urakami K, Nagashima T\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eClinicopathological and mutational analyses of colorectal cancer with mutations in the POLE gene\u003c/strong\u003e. \u003cem\u003eCancer Med \u003c/em\u003e2019, \u003cstrong\u003e8\u003c/strong\u003e(10):4587-4597.\u003c/li\u003e\n\u003cli\u003eStasenko M, Tunnage I, Ashley CW, Rubinstein MM, Latham AJ, Da Cruz Paula A, Mueller JJ, Leitao MM, Jr., Friedman CF, Makker V\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eClinical outcomes of patients with POLE mutated endometrioid endometrial cancer\u003c/strong\u003e. \u003cem\u003eGynecol Oncol \u003c/em\u003e2020, \u003cstrong\u003e156\u003c/strong\u003e(1):194-202.\u003c/li\u003e\n\u003cli\u003eMeng B, Hoang LN, McIntyre JB, Duggan MA, Nelson GS, Lee CH, K\u0026ouml;bel M: \u003cstrong\u003ePOLE exonuclease domain mutation predicts long progression-free survival in grade 3 endometrioid carcinoma of the endometrium\u003c/strong\u003e. \u003cem\u003eGynecol Oncol \u003c/em\u003e2014, \u003cstrong\u003e134\u003c/strong\u003e(1):15-19.\u003c/li\u003e\n\u003cli\u003eKandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIntegrated genomic characterization of endometrial carcinoma\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2013, \u003cstrong\u003e497\u003c/strong\u003e(7447):67-73.\u003c/li\u003e\n\u003cli\u003eChurch DN, Stelloo E, Nout RA, Valtcheva N, Depreeuw J, ter Haar N, Noske A, Amant F, Tomlinson IP, Wild PJ\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrognostic significance of POLE proofreading mutations in endometrial cancer\u003c/strong\u003e. \u003cem\u003eJ Natl Cancer Inst \u003c/em\u003e2015, \u003cstrong\u003e107\u003c/strong\u003e(1):402.\u003c/li\u003e\n\u003cli\u003eHussein YR, Weigelt B, Levine DA, Schoolmeester JK, Dao LN, Balzer BL, Liles G, Karlan B, K\u0026ouml;bel M, Lee CH\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eClinicopathological analysis of endometrial carcinomas harboring somatic POLE exonuclease domain mutations\u003c/strong\u003e. \u003cem\u003eMod Pathol \u003c/em\u003e2015, \u003cstrong\u003e28\u003c/strong\u003e(4):505-514.\u003c/li\u003e\n\u003cli\u003eWang Y, Ju L, Wang G, Qian K, Jin W, Li M, Yu J, Shi Y, Wang Y, Zhang Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDNA polymerase POLD1 promotes proliferation and metastasis of bladder cancer by stabilizing MYC\u003c/strong\u003e. \u003cem\u003eNat Commun \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e(1):2421.\u003c/li\u003e\n\u003cli\u003eWang Y, Ju L, Guo Z, Qian K, Chen H, Zhang Y, Xiao Y, Wang X: \u003cstrong\u003ePedigree analysis of a POLD1 germline mutation in urothelial carcinoma shows a close association between different mutation burdens and overall survival\u003c/strong\u003e. \u003cem\u003eCell Mol Immunol \u003c/em\u003e2021, \u003cstrong\u003e18\u003c/strong\u003e(3):767-769.\u003c/li\u003e\n\u003cli\u003eLem\u0026eacute;e F, Bergoglio V, Fernandez-Vidal A, Machado-Silva A, Pillaire MJ, Bieth A, Gentil C, Baker L, Martin AL, Leduc C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDNA polymerase theta up-regulation is associated with poor survival in breast cancer, perturbs DNA replication, and promotes genetic instability\u003c/strong\u003e. \u003cem\u003eProc Natl Acad Sci U S A \u003c/em\u003e2010, \u003cstrong\u003e107\u003c/strong\u003e(30):13390-13395.\u003c/li\u003e\n\u003cli\u003eHiggins GS, Harris AL, Prevo R, Helleday T, McKenna WG, Buffa FM: \u003cstrong\u003eOverexpression of POLQ confers a poor prognosis in early breast cancer patients\u003c/strong\u003e. \u003cem\u003eOncotarget \u003c/em\u003e2010, \u003cstrong\u003e1\u003c/strong\u003e(3):175-184.\u003c/li\u003e\n\u003cli\u003eShinmura K, Kato H, Kawanishi Y, Yoshimura K, Tsuchiya K, Takahara Y, Hosokawa S, Kawase A, Funai K, Sugimura H: \u003cstrong\u003ePOLQ Overexpression Is Associated with an Increased Somatic Mutation Load and PLK4 Overexpression in Lung Adenocarcinoma\u003c/strong\u003e. \u003cem\u003eCancers (Basel) \u003c/em\u003e2019, \u003cstrong\u003e11\u003c/strong\u003e(5).\u003c/li\u003e\n\u003cli\u003eCanitrot Y, Laurent G, Astarie-Dequeker C, Bordier C, Cazaux C, Hoffmann JS: \u003cstrong\u003eEnhanced expression and activity of DNA polymerase beta in chronic myelogenous leukemia\u003c/strong\u003e. \u003cem\u003eAnticancer Res \u003c/em\u003e2006, \u003cstrong\u003e26\u003c/strong\u003e(1b):523-525.\u003c/li\u003e\n\u003cli\u003eWu H, Mo Y, Yu S, Ye X, Lu Y, Wang C, Shan X: \u003cstrong\u003eNovel homozygous mutations in AIRE leading to APS-1 and potential mechanisms based on bioinformatics analysis\u003c/strong\u003e. \u003cem\u003eHeliyon \u003c/em\u003e2024, \u003cstrong\u003e10\u003c/strong\u003e(6):e28037.\u003c/li\u003e\n\u003cli\u003eWu H, Wang C, Yu S, Ye X, Jiang Y, He P, Shan X: \u003cstrong\u003eDownregulation of ACAN is Associated with the Growth hormone pathway and Induces short stature\u003c/strong\u003e. \u003cem\u003eJ Clin Lab Anal \u003c/em\u003e2023, \u003cstrong\u003e37\u003c/strong\u003e(2):e24830.\u003c/li\u003e\n\u003cli\u003eWang C, Yu S, Qian R, Chen S, Dai C, Shan X: \u003cstrong\u003ePrognostic and immunological significance of peroxisome proliferator-activated receptor gamma in hepatocellular carcinoma based on multiple databases\u003c/strong\u003e. \u003cem\u003eTransl Cancer Res \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e(7):1938-1953.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4737536/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4737536/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThe long-term prognosis of acute myeloid leukemia (AML) is challenging due to limited understanding of the molecular markers involved in its development. This study investigates the role of DNA polymerases in AML to offer new insights for diagnosis and treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study on pediatric AML patients with POL gene family mutations from 2021 to 2024 was conducted. Patients were categorized based on risk stratification criteria, and the DAH regimen was used for induction chemotherapy. Bioinformatics analysis integrated data from various databases to identify key genes and develop survival analysis plots and AUC curves.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 59 pediatric AML patients, revealing no significant differences in demographic or clinical characteristics between those with and without POL family gene mutations. However, patients with POL gene mutations showed higher complete remission rates after initial DAH chemotherapy (91.67% vs. 59.57%, P\u0026thinsp;=\u0026thinsp;0.03607), indicating a potential treatment benefit. High expression of four POL genes (POLD1, POLE, POLG, POLQ) in bone marrow and immune cells suggests their crucial role in hematopoiesis and immune response. Survival analysis across different datasets indicated that AML patients with overexpressed POL family genes had significantly worse outcomes, proposing these genes as potential prognostic biomarkers for AML.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThis study on pediatric AML demonstrates that POL gene family mutations are associated with higher remission rates post-chemotherapy, indicating their potential as prognostic markers. Bioinformatics analysis emphasizes the significance of these mutations in AML, highlighting their impact on disease prognosis.\u003c/p\u003e","manuscriptTitle":"Integrated Analysis of POL Family Gene Mutations in AML: Clinical Features, Prognosis, and Bioinformatics Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 00:49:01","doi":"10.21203/rs.3.rs-4737536/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21f8c7c7-eed4-4697-8847-1be206681a2a","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-17T00:49:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 00:49:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4737536","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4737536","identity":"rs-4737536","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}