Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access November 9, 2023

Establishing a novel Fanconi anemia signaling pathway-associated prognostic model and tumor clustering for pediatric acute myeloid leukemia patients

  • Lixian Chang , Xuelian Cheng , Xingjie Gao , Yao Zou , Weiping Yuan , Li Zhang EMAIL logo and Xiaofan Zhu EMAIL logo
From the journal Open Medicine

Abstract

Considering the connection between the Fanconi anemia (FA) signaling pathway and tumor development, we aim to investigate the links between the FA gene expression and the survival prognosis of acute myeloid leukemia (AML) patients. Our study begins by identifying two distinct clusters of pediatric AML patients. Following the batch matching of the TARGET-AML, TCGA-LAML GSE71014, GSE12417, and GSE37642 cohorts, the samples were divided into a training set and an internal validation set. A Lasso regression modeling analysis was performed to identify five signatures: BRIP1, FANCC, FANCL, MAD2L2, and RFWD3. The AML samples were stratified into high- and low-risk groups by evaluating the risk scores. The AML high-risk patients showed a poorer overall survival prognosis. To predict the survival rates, we developed an FA Nomogram incorporating risk score, gender, age, and French–American–British classification. We further utilized the BEAT-AML cohort for the external validation of FA-associated prognostic models and observed good clinical validity. Additionally, we found a correlation between DNA repair, cell cycle, and peroxide-related metabolic events and FA-related high/low risk or cluster 1/2. In summary, our novel FA-associated prognostic models promise to enhance the prediction of pediatric AML prognosis.

1 Introduction

During myeloid differentiation, hematopoietic progenitor cells get blocked, resulting in acute myeloid leukemia (AML) [1,2,3]. AML is characterized by its high heterogeneity, associated with several factors, including cytogenetic abnormalities, genetic mutations, and changes in gene expression [1,2,3]. Pediatric AML constitutes approximately 15–20% of all cases of childhood leukemia [4,5]. Advancements in hematopoietic stem cell transplantation, molecular targeted therapy, immunotherapy, and genomic technology have improved prognosis and overall survival (OS) rates for pediatric AML. However, the mortality rate remains high [4,5]. Pediatric AML exhibits distinct genetic characteristics compared to adult AML [6]. Evaluating prognostic risk factors in AML is crucial for the management strategies or therapeutic advances in AML patients [1,2,3]. Several models have been reported for predicting the prognosis of AML patients, focusing on specific biological events [7,8,9]. For instance, a survival model based on 85 genes was reported to assess the AML prognosis [7]. Another study identified a 29-gene signature that could be a helpful predictor for therapy resistance in intensively treated adult AML patients [9]. However, fewer specific gene expression-based models or clusters were reported for predicting the prognosis of pediatric AML.

Fanconi anemia (FA) is characterized by autosomal or X-linked-recessive and autosomal-dominant genetic inheritance [10,11,12,13,14]. The pathogenesis of FA is associated with the FA signaling pathway, which contributes to genomic stability [10,11,12,13,14]. So far, 23 FA-related genes have been identified, such as FANCA/B/C and FANCD2 [10,11,12,13,14,15]. Extensive research has established strong connections between the FA signaling pathway and cancers [16,17,18,19]. FA patients are more susceptible to AML and other solid tumors [16,17,18,19,20,21]. For example, the FA pathway has shown potential as a treatment target for colorectal cancer [16]. Therefore, developing effective and reliable prognostic risk score models for AML is crucial by considering the expression pattern of FA signaling pathway genes.

Herein, we carried out a cluster analysis of pediatric AML cases based on the FA signaling pathway. We then identified five FA hub genes (including BRIP1, FANCC, FANCL, MAD2L2, and RFWD3). These genes were used to establish a novel FA-related prognostic model for pediatric AML patients.

2 Methods

2.1 Gene expression and clinical information

Gene expression matrix of the TPM (transcripts per million) type and a set of clinical traits were obtained from The Cancer Genome Atlas (TCGA)-LAML cohort using the “TCGAbiolinks” R package. We analyzed the expression pattern of each FA gene by combining datasets within a genotype-tissue expression (GTEx) and TCGA-LAML cohort. The data of TARGET-AML were obtained from the official website (https://ocg.cancer.gov/). We performed a statistical analysis using the wilcox.test or kruskal.test. The “GEOquery” R package was utilized to obtain the datasets of the GSE71014, GSE37642, and GSE12417 cohorts within the gene expression omnibus (GEO) database. Then, the expression matrix of TARGET-AML (n = 257), TCGA-LAML (n = 137), GSE71014 (n = 104), GSE37642 (n = 136), and GSE12417 (n = 78) was processed using the R language, and the batch correction was performed using the “sva” package. We then used the prcomp() function for the principal component analysis (PCA). The BEAT-AML cohort (n = 399) [22,23,24,25,26] was utilized as an external validation set.

2.2 Tumor clustering

We used the “ConsensusClusterPlus” R package [27] to cluster TARGET-AML samples according to the expression characteristics of FA signatures. Then, PCA was conducted. Additionally, we utilized the “survival” R package to perform the cluster-related prognostic analysis.

2.3 Lasso regression modeling construction

We performed Lasso regression modeling analysis using the datasets of TARGET-AML, TCGA-LAML GSE71014, GSE12417, and GSE37642 cohorts. The cases were divided into a training set (75%) and an internal validation set (25%). We used the “glmnet” R package to conduct a Lasso regression modeling analysis of the training set. The resulting model provided gene coefficients and risk score values. Risk scores were grouped according to their median values to determine high- and low-risk groups. The OS risk values for the internal validation set were also obtained. We plotted the risk profile, survival curves, and the survival status map. The hub gene expression profile was visualized as a heat map. Furthermore, we used the BEAT-AML cohort to externally validate the above FA-associated prognostic model.

2.4 Gene enrichment

Differentially expressed genes in two tumor clusters or risk groups were identified using a “limma” R package with a false discovery rate of 0.05 and a log2 fold change (log2FC) of 1. A “ggplot2” R package was used to generate an MA plot. Also, a heat map of FA genes and the clinical traits of age, gender, French–American–British (FAB), and clustering status were created. Gene enrichment analysis was performed, and the results were visualized using the “GOplot” R package [28]. Furthermore, gene set enrichment analysis (GSEA) was conducted by a “clusterProfiler” R package [29,30], and the results were visualized using the “gseaplot()” function of the enrichplot R package. Common members of differential genes between the risk and clustering groups were obtained, and the related gene ontology (GO)-Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed.

2.5 Cox regression

To assess the OS prognosis, the univariate/multivariate Cox regression analyses were conducted using a “survival” R package, considering the factors of prognostic risk score, FAB, gender, and age. Additionally, we performed external validation of the BEAT-AML cohort based on the multivariate Cox regression model associated with FA, using the predict() function. The forest plots were generated using the plot() R function. To evaluate the receiver operating characteristic (ROC) results for different survival times, we utilized the “survivalROC” R package and provided the value of area under the curve (AUC).

2.6 Nomogram prediction model

A cph () modeling analysis was performed using the “regplot” R package, and a Nomogram was obtained. The calibration curves of AML cases were plotted using the calibrate() function of the “ggstatsplot” R package. The C index value was obtained using the “rms” R package. In addition, the external validation dataset (BEAT-AML) was used to perform calibration curve analysis based on the FA Nomogram. The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses were conducted using the “survIDINRI” R package. Finally, the decision curve analysis (DCA) was performed using the “stdca.R” package from www.decisioncurveanalysis.org.

3 Results

3.1 Analytic strategy

The schematic diagram of our analytic strategy is presented in Figure 1. We initially analyzed the expression profile of 23 FA genes and the clinical traits. Next, using these FA signatures, we identified two tumor subtypes (cluster 1/2) in AML samples by utilizing the clustering function of the “ConsensusClusterPlus” R package. Additionally, we extracted and performed batch correction on the expression matrix of TARGET-AML, TCGA-LAML GSE71014, GSE12417, and GSE37642 cohorts. This was done with FA-related genes, the FA expression matrix, and corresponding clinical traits. An internal validation and training set were randomly selected from the samples. The training set-based Lasso regression modeling analysis was then conducted. We conducted multivariate Cox regression analyses. Nomograms and related calibrate curves were plotted as well. We assessed the clinical effectiveness of FA models by DCA. Additionally, survival, differential, and enrichment analyses of GO, KEGG, and GSEA were performed after risk or cluster grouping. FA-related models were validated in both the internal and external validation cohorts.

Figure 1 
                  A schematic diagram of the study design.
Figure 1

A schematic diagram of the study design.

3.2 Expression profile

We analyzed the expression features of FA genes based on the combined datasets from the GTEx and TCGA-LAML cohorts.

As shown in Figure S1, we observed increased expression levels in the tumor group for FANCA, BRCA2, FANCG, FANCM, SLX4, ERCC4, and FAAP100 (all p < 0.05), while a decreased level was observed for FANCB, FANCC, FANCD2, FANCE, FANCI, FANCL, RAD51C, RAD51, BRCA1, UBE2T, XRCC2, MAD2L2, and RFWD3 (all p < 0.05) compared with the normal group. Additionally, there were expression differences for all FA genes, except FANCG, among different FAB types (Figure 2, all p < 0.05). We found a slightly higher expression of RAD51 (Figure S2, p < 0.05) and UBE2T (p < 0.05) in the male group. Furthermore, as shown in Figure S3, we observed positive correlations between the age factor and expression levels of FANCC and FANCE (p < 0.001).

Figure 2 
                  Correlation between FA gene expression and FAB. Box plots display each FA gene’s expression difference among different FAB types for the TARGET-AML cohort. A kruskal.test() was performed. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 2

Correlation between FA gene expression and FAB. Box plots display each FA gene’s expression difference among different FAB types for the TARGET-AML cohort. A kruskal.test() was performed. *p < 0.05, **p < 0.01, ***p < 0.001.

3.3 Tumor clustering analysis

Next, we conducted a tumor clustering analysis based on the expression profile of FA genes within TCGA-LAML (Figure 3a) and TARGET-AML (Figure 3b) datasets. We separated these AML clusters using a PCA approach for both cohorts. However, we found a statistical difference in OS survival between the two clusters only for the TARGET-AML (Figure 3b, p = 1.153 × 10−3) but not for the TCGA-LAML (Figure 3a). Specifically, the pediatric AML patients of TARGET in cluster 2 showed a worse OS prognosis compared to those in cluster 1 (Figure 3b). We provided heat map data in Figure 3c, showing the association between FA gene expression and clinical information. Furthermore, a significant difference was observed in all FA gene expressions between clusters 1 and 2 (Figure S4, all p < 0.05).

Figure 3 
                  Tumor clustering analysis of FA genes. (a) AML patients from the TCGA-LAML were clustered according to the expression characteristics of FA-related genes. A PCA was performed to evaluate the classification effect on tumor clusters. The FA-specific prognostic curve of OS is also provided. (b) Similar analyses were conducted for the TARGET-AML cohort. (c) The correlation between FA gene expression and various clinical traits, including age, gender, FAB, and clusters, was analyzed. A heat map is provided. (d and e) The differential gene identification of the two clusters was completed, and a volcano plot is provided (d). The GO/KEGG (e) and GSEA (f) gene enrichment analyses were then conducted.
Figure 3

Tumor clustering analysis of FA genes. (a) AML patients from the TCGA-LAML were clustered according to the expression characteristics of FA-related genes. A PCA was performed to evaluate the classification effect on tumor clusters. The FA-specific prognostic curve of OS is also provided. (b) Similar analyses were conducted for the TARGET-AML cohort. (c) The correlation between FA gene expression and various clinical traits, including age, gender, FAB, and clusters, was analyzed. A heat map is provided. (d and e) The differential gene identification of the two clusters was completed, and a volcano plot is provided (d). The GO/KEGG (e) and GSEA (f) gene enrichment analyses were then conducted.

The differential genes of the two clusters were obtained, and a volcano plot is shown in Figure 3d. The GO/KEGG enrichment analysis data (Figure 3e) revealed that these genes were associated with the events related to the “cell cycle,” “DNA replication,” and peroxide-related events such as “hydrogen peroxide metabolic process,” and “peroxidase activity.” The GSEA result further showed a series of genes linked to DNA repair events, such as “nucleotide excision repair” and “homology-directed repair,” as well as cell cycle events, such as “M_phase” and “cell cycle checkpoints” (Figure 3f). These findings suggest a connection between the pediatric AML FA subtypes and DNA repair, cell cycle, and peroxide-related metabolic events.

3.4 Lasso regression model

We initially conducted batch matching on the TARGET-AML, TCGA-LAML GSE71014, GSE12417, and GSE37642 cohorts (Figure 4a). Subsequently, we used 75% of the samples as a training set. Using the expression feature of FA genes, we performed a Lasso regression modeling analysis (Figure 4b). Our model identified five hub genes with correlation coefficients: BRIP1 (0.284), FANCC (−0.031), FANCL (0.022), MAD2L2 (0.270), and RFWD3 (−0.068). The results, including the expression/risk profile, survival curve, and survival status of the training and internal validation sets, are presented in Figure 4c–d. We observed an increased mortality status in AML cases as the risk value increased. Furthermore, the high-risk group exhibited a worse OS prognosis than the low-risk group (Figure 4c, p = 7.358 × 10−5; Figure 4d, p = 7.249 × 10−3).

Figure 4 
                  Lasso regression modeling analysis of FA. (a) We performed the Lasso regression modeling analysis based on the expression matrix of FA-related genes in TARGET-AML, TCGA-LAML GSE71014, GSE12417, and GSE37642 cohorts. A PCA plot shows the batch correction of the expression matrix. (b) Based on the training set, the Lasso regression modeling analysis was performed. (c and d) The data of the hub gene expression profile, risk profile, survival status map, and survival curve are provided in the training set (c) and internal validation set (d).
Figure 4

Lasso regression modeling analysis of FA. (a) We performed the Lasso regression modeling analysis based on the expression matrix of FA-related genes in TARGET-AML, TCGA-LAML GSE71014, GSE12417, and GSE37642 cohorts. A PCA plot shows the batch correction of the expression matrix. (b) Based on the training set, the Lasso regression modeling analysis was performed. (c and d) The data of the hub gene expression profile, risk profile, survival status map, and survival curve are provided in the training set (c) and internal validation set (d).

3.5 Prognostic analysis

In the Cox regression analyses of TCGA-LAML, the age factor was found to be correlated with worse clinical OS prognosis in adult AML patients (Figure 5a, HR > 1, p < 0.001). For the pediatric AML cases in the TARGET-AML cohort, we observed a relationship between higher risk scores and worse clinical OS prognosis (Figure 5b, all HR > 10, p < 0.001). The relationship between hub gene expression and clinical traits was visualized as a heatmap in Figure 5c. Furthermore, we performed ROC analysis to assess the OS predictive value of our model. Figure 5d shows that the AUC values were greater than 0.62 for 1-, 3-, 5-year survival time. These suggested that our FA model exhibits good predictive value for OS in pediatric AML patients.

Figure 5 
                  Prognostic analyses of TCGA-LAML and TARGET-AML. Targeting the factors (prognostic risk score, FAB, gender, and age), we performed the univariate/multivariate Cox regression analyses to evaluate the prognosis of OS for the TCGA-LAML (a) and TARGET-AML (b). We created a heatmap that combines the expression of hub genes with related clinical traits (c). Based on the risk score, we conducted the ROC analyses to assess the OS prognosis of TARGET-AML for different survival time points (d).
Figure 5

Prognostic analyses of TCGA-LAML and TARGET-AML. Targeting the factors (prognostic risk score, FAB, gender, and age), we performed the univariate/multivariate Cox regression analyses to evaluate the prognosis of OS for the TCGA-LAML (a) and TARGET-AML (b). We created a heatmap that combines the expression of hub genes with related clinical traits (c). Based on the risk score, we conducted the ROC analyses to assess the OS prognosis of TARGET-AML for different survival time points (d).

3.6 Nomogram

We conducted the Nomogram and related assessments. Among the hub genes, there were higher expression levels of BRIP1 (Figure 6a, p < 0.001), FANCL (p < 0.05), and MAD2L2 (p < 0.001) in the high-risk group, compared with the low-risk group. By combining the risk score, gender, age, and FAB, we established a Nomogram to predict the OS rates of a pediatric AML case. The Nomogram, shown in Figure 6b, predicted the 1-, 3-, and 5-year survival rates at 0.34, 0.292, and 0.0881, respectively. The calibration plot curve in Figure 6c showed a high overlap between the predicted and observed value lines. The C index value of 0.66 was obtained (Figure 6c). Furthermore, NRI and IDI results suggested that the predictive function of the model improved after incorporating the risk score factor (Figure 6d, all IDI > 0, NRI > 0, p < 0.05). The DCA results (Figure 6e) further showed a better clinical validity of the “age + gender + FAB + risk score” group than other groups.

Figure 6 
                  Nomogram and related assessment analyses. (a) The expression difference of FA hub genes between high- and low-risk groups is presented. (b) A Nomogram is constructed to forecast the 1-, 3-, and 5-year survival rates of a given AML case. Furthermore, the outcomes of the calibration plot curve (c), NRI/IDI assessment (d), and DCA (e) are provided.
Figure 6

Nomogram and related assessment analyses. (a) The expression difference of FA hub genes between high- and low-risk groups is presented. (b) A Nomogram is constructed to forecast the 1-, 3-, and 5-year survival rates of a given AML case. Furthermore, the outcomes of the calibration plot curve (c), NRI/IDI assessment (d), and DCA (e) are provided.

3.7 External validation analysis

Based on the FA-associated prognostic models mentioned above, we conducted external validation using the BEAT-AML cohort. As shown in Figure 7a, it can be observed that AML cases with a high FA-associated risk score showed a poorer prognosis (p = 3.612 × 10−2). Furthermore, an external validation of the multivariate Cox regression model associated with FA was conducted, revealing a favorable predictive value for AML patients, particularly for the 1-year survival time (AUC = 0.71), as depicted in Figure 7b. A calibration plot curve was also generated using the external validation set, illustrating a substantial concurrence between the predicted and observed value lines (Figure 7c). These results serve to substantiate the good predictive capability of our FA-associated prognostic models.

Figure 7 
                  External validation of FA-associated prognostic models. (a) Based on the Lasso regression modeling of FA genes, we performed the survival curve analysis using the external validation cohorts (BEAT-AML). (b) The ROC results of the external validation set for 1-, 2-, and 3-year survival times were obtained based on the FA-associated multivariate Cox regression model. (c) Based on the FA Nomogram, we also performed the calibration curve analysis of the external validation set.
Figure 7

External validation of FA-associated prognostic models. (a) Based on the Lasso regression modeling of FA genes, we performed the survival curve analysis using the external validation cohorts (BEAT-AML). (b) The ROC results of the external validation set for 1-, 2-, and 3-year survival times were obtained based on the FA-associated multivariate Cox regression model. (c) Based on the FA Nomogram, we also performed the calibration curve analysis of the external validation set.

3.8 Prognostic risk-related differential gene analysis

We identified a series of differential genes (n = 65) based on the high- and low-risk grouping. The results were visualized via an MA plot (Figure 8a) and a heat map (Figure 8b). Further analysis of these differential genes was conducted using GO and KEGG enrichment approaches (Figure 8c). Our findings revealed the biological metabolic processes, such as the “antibiotic metabolic process” and “cellular oxidant detoxification.” Additionally, GSEA results showed the enrichment of events, including “DNA repair,” “G2M checkpoint,” and “oxidative phosphorylation” (Figure 8d). We also obtained 24 common members of differential genes between the risk and clustering groups (Figure 8e) and performed the gene enrichment analyses of GO-KEGG (Figure 8f). This analysis highlighted peroxide-related events, including the “hydrogen peroxide catabolic process” and “peroxidase activity.”

Figure 8 
                  Prognostic risk-related differential gene analysis. (a and b) The differential gene identification between the high- and low-risk groups was performed, and the MA plot (a) and heat map (b) are provided to visualize the data. Gene enrichment analyses of GO-KEGG (c) and GSEA (d) were conducted to gain further insights. (e and f) Common members of differential genes between the risk and clustering groups were obtained, and gene enrichment analyses of GO-KEGG were performed to explore the functional significance.
Figure 8

Prognostic risk-related differential gene analysis. (a and b) The differential gene identification between the high- and low-risk groups was performed, and the MA plot (a) and heat map (b) are provided to visualize the data. Gene enrichment analyses of GO-KEGG (c) and GSEA (d) were conducted to gain further insights. (e and f) Common members of differential genes between the risk and clustering groups were obtained, and gene enrichment analyses of GO-KEGG were performed to explore the functional significance.

4 Discussion

We developed a prospective predictive model for AML by focusing on the FA signaling pathway. The consensus clustering of AML patients from the TCGA-LAML and TARGET-AML datasets was conducted using bioinformatics strategies. We identified two distinct clusters based on the expression patterns of FA signatures. Interestingly, we found a positive correlation between tumor clustering and OS prognosis only in pediatric AML cases but not in adult AML. Our novel FA-based tumor clustering approach may be helpful for the diagnosis and therapy of pediatric AML.

The close correlation between AML and mutations has led to the primary focus on correlation analysis of gene mutations in AML patients. This analysis aims to guide clinical treatment and evaluate prognosis [1,2,3,7,8,9,31]. For instance, in a previous study, we conducted a stratified prognostic analysis of different clinical subgroups in 132 children with AML-based data on gene mutations and related clinical traits [31]. Herein, we explore the potential correlation between FA-related models and the prognosis of AML patients, explicitly focusing on expression features of FA pathway genes. We developed a novel FA model for AML using the Lasso approach. Our multivariate Cox regression analysis also showed that a higher risk score is linked to a worse clinical OS prognosis, specifically in the TARGET-AML cohort rather than the TCGA-LAML cohort. These findings suggest a potential solid link between the FA signaling pathway and pediatric AML. Notably, the pediatric AML patients in cluster 2 or high-risk groups showed a worse survival prognosis.

In addition to conducting internal validation of models, we sought external validation of FA-related models by including independent external data. Due to the absence of high-throughput gene expression data for AML patients at our center, we utilized the BEAT AML data. It is worth noting that the BEAT-AML dataset had limited information on children’s AML. Thus, we refrained from filtering the BETA-AML data. The BEAT AML cohort was stratified into high- and low-risk groups based on our FA Lasso model, and the high-risk group exhibited a worse OS prognosis. Using FA-associated multivariate Cox regression analysis, we discovered that OS demonstrated favorable predictive capability for AML patients in the external validation cohort, particularly for the 1-year survival timeframe. However, we did not observe a statistically significant predictive effect for the 5-year survival period, potentially due to our analysis’s limited data on pediatric AML.

To enhance the predictive accuracy and clinical competence of survival prediction, we build an FA-associated Nomogram by combining the risk score, gender, age, and FAB. The calibration curve of both the training and external validation sets showed a significant overlap between the predicted and observed value lines, indicating strong predictive competence and accurate survival prediction. The findings of the FA model indicate that, along with mutation analysis of specific genes, it is crucial to measure the expression pattern of FA pathway genes in pediatric AML data. Standardization of FA expression detection procedures and continuous optimization of FA-related models will contribute to prognostic assessment and treatment decisions for children with AML. In addition, some patients with FA disease are known to develop tumors [21,32]. Previous studies have shown that Azacitidine effectively treats pediatric patients with FA and AML [20]. It is worth investigating the molecular mechanism underlying the transformation from FA to tumor and conducting specific chemosensitivity analysis. Once sufficient sample information is obtained, this can be achieved by studying the expression and mutation profile of FA-related genes.

The involvement of FA genes in DNA repair is closely linked to genomic maintenance within cells under stress conditions [10,33]. Despite being a rare chromosomal instability disorder [32], the functional loss of the FA signaling pathway during the DNA repair process is crucial for the occurrence and development of certain tumors [17,18,34]. Two components of the FA signaling pathway, namely BRCA1 and BRCA2, function as susceptibility genes for breast cancer [21]. Our study identified five hub prognostic genes in our model: BRIP1, FANCC, FANCL, MAD2L2, and RFWD3. As expected, clustering and risk-related gene enrichment analyses revealed a set of DNA repair-related events, including “nucleotide excision repair” and “homology-directed repair.” Besides, we observed enrichment of the cell cycle events, such as “M_phase,” “cell cycle checkpoints,” and “G2M checkpoint.” These findings suggest that the failure of timely and effective repair of damaged DNA during cell division is a critical factor in carcinogenesis when exposed to harmful external stimuli. Interestingly, we have seen the enrichment of peroxide-related biological metabolic issues, such as the “hydrogen peroxide catabolic process” and “peroxidase activity.” It has been observed that cells of individuals with FA are particularly vulnerable to oxidative stress, which is associated with the FANCG protein in mitochondria and peroxidase activity [35]. These findings suggest that the disruption in the peroxidase metabolic process contributes to the accumulation of oxidative DNA damage during tumorigenesis.

5 Conclusion

Overall, this study employed the expression data of FA-related genes to conduct a tumor clustering analysis. Also, a novel clinical predictive model was established, incorporating five FA-related signatures (BRIP1, FANCC, FANCL, MAD2L2, and RFWD3) for patients diagnosed with AML. Furthermore, a novel FA Nomogram was developed, demonstrating improved validity in predicting clinical survival outcomes. The FA-related prognostic model and tumor clustering have the potential to serve as prognostic predictors, aiding clinicians in evaluating the prognosis of pediatric AML patients.

Abbreviations

AML

acute myeloid leukemia

FA

Fanconi anemia

FAB

French–American–British

OS

overall survival

TCGA

The Cancer Genome Atlas

GEO

gene expression omnibus

PCA

principal component analysis

TPM

transcripts per million

GTEx

genotype-tissue expression

FDR

false discovery rate

FC

fold change

GSEA

gene set enrichment analysis

GO

gene ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

ROC

receiver operating characteristic

AUC

area under the curve

NRI

net reclassification improvement

IDI

integrated discrimination improvement

DCA

decision curve analysis

Acknowledgments

The authors express their gratitude for the contribution of available online datasets or datasets, such as TARGET, TCGA, GEO, BEAT-AML, and GTEx.

  1. Funding information: This work was supported by grants from the Ministry of Science and Technology of China (2019YFA0110803), the National Natural Science Foundation of China (81500156, 82170135), Tianjin Natural Science Foundation Project (20JCYBJC00470), and the National Key Research and Development Program of China (2018YFA0107801).

  2. Author contributions: L. Chang, X. Cheng, and X. Zhu designed the study. L. Chang, X. Gao, and L. Zhang performed tumor clustering, Lasso regression modeling construction, Cox regression, gene enrichment, and nomogram establishment analyses. L. Chang, X. Gao, and X. Zhu interpreted the data and drafted the manuscript. W. Yuan and Y. Zou reviewed and revised the manuscript. All authors read and approved the final manuscript.

  3. Conflict of interest: The authors declare no conflict of interest.

  4. Data availability statement: The data used to support the findings of this study are available from the corresponding author upon reasonable request.

References

[1] Rubnitz JE, Kaspers GJL. How I treat pediatric acute myeloid leukemia. Blood. 2021;138(12):1009–18.Search in Google Scholar

[2] Short NJ, Rytting ME, Cortes JE. Acute myeloid leukaemia. Lancet. 2018;392(10147):593–606.Search in Google Scholar

[3] Newell LF, Cook RJ. Advances in acute myeloid leukemia. BMJ. 2021;375:n2026.Search in Google Scholar

[4] Obszański P, Kozłowska A, Wańcowiat J, Twardowska J, Lejman M, Zawitkowska J. Molecular-targeted therapy of pediatric acute myeloid leukemia. Molecules. 2022;27(12):3911.Search in Google Scholar

[5] Tosic N, Marjanovic I, Lazic J. Pediatric acute myeloid leukemia: Insight into genetic landscape and novel targeted approaches. Biochem Pharmacol. 2023;215:115705.Search in Google Scholar

[6] Shiba N. Comprehensive molecular understanding of pediatric acute myeloid leukemia. Int J Hematol. 2023;117(2):173–81.Search in Google Scholar

[7] Lai Y, Sheng L, Wang J, Zhou M, OuYang G. A novel 85-gene expression signature predicts unfavorable prognosis in acute myeloid leukemia. Technol Cancer Res Treat. 2021;20:15330338211004933.Search in Google Scholar

[8] Chen Z, Song J, Wang W, Bai J, Zhang Y, Shi J, et al. A novel 4-mRNA signature predicts the overall survival in acute myeloid leukemia. Am J Hematol. 2021;96(11):1385–95.Search in Google Scholar

[9] Herold T, Jurinovic V, Batcha AMN, Bamopoulos SA, Rothenberg-Thurley M, Ksienzyk B, et al. A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia. Haematologica. 2018;103(3):456–65.Search in Google Scholar

[10] Ceccaldi R, Sarangi P, D’Andrea AD. The Fanconi anaemia pathway: New players and new functions. Nat Rev Mol Cell Biol. 2016;17(6):337–49.Search in Google Scholar

[11] Mamrak NE, Shimamura A, Howlett NG. Recent discoveries in the molecular pathogenesis of the inherited bone marrow failure syndrome Fanconi anemia. Blood Rev. 2017;31(3):93–9.Search in Google Scholar

[12] Inano S, Sato K, Katsuki Y, Kobayashi W, Tanaka H, Nakajima K, et al. RFWD3-mediated ubiquitination promotes timely removal of both RPA and RAD51 from DNA damage sites to facilitate homologous recombination. Mol Cell. 2017;66(5):622–34.e8.Search in Google Scholar

[13] Nepal M, Che R, Ma C, Zhang J, Fei P. FANCD2 and DNA damage. Int J Mol Sci. 2017;18(8):1804.Search in Google Scholar

[14] Knies K, Inano S, Ramírez MJ, Ishiai M, Surrallés J, Takata M, et al. Biallelic mutations in the ubiquitin ligase RFWD3 cause Fanconi anemia. J Clin Invest. 2017;127(8):3013–27.Search in Google Scholar

[15] Landelouci K, Sinha S, Pepin G. Type-I interferon signaling in Fanconi anemia. Front Cell Infect Microbiol. 2022;12:820273.Search in Google Scholar

[16] Parsa FG, Nobili S, Karimpour M, Aghdaei HA, Nazemalhosseini-Mojarad E, Mini E. Fanconi anemia pathway in colorectal cancer: A novel opportunity for diagnosis, prognosis and therapy. J Pers Med. 2022;12(3):396.Search in Google Scholar

[17] Nepal M, Che R, Zhang J, Ma C, Fei P. Fanconi anemia signaling and cancer. Trends Cancer. 2017;3(12):840–56.Search in Google Scholar

[18] Nalepa G, Clapp DW. Fanconi anaemia and cancer: An intricate relationship. Nat Rev Cancer. 2018;18(3):168–85.Search in Google Scholar

[19] Savage SA, Walsh MF. Myelodysplastic syndrome, acute myeloid leukemia, and cancer surveillance in Fanconi anemia. Hematol Oncol Clin North Am. 2018;32(4):657–68.Search in Google Scholar

[20] Ding H, Hashem H, Cabral L, Rangarajan H, Abusin G, Lazarus HM, et al. Azacitidine as a bridge to allogeneic hematopoietic cell transplantation in a pediatric patient with Fanconi anemia and acute myeloid leukemia. Pediatr Transpl. 2017;21(2):e12870.Search in Google Scholar

[21] D’Andrea AD. Susceptibility pathways in Fanconi’s anemia and breast cancer. N Engl J Med. 2010;362(20):1909–19.Search in Google Scholar

[22] Tyner JW, Tognon CE, Bottomly D, Wilmot B, Kurtz SE, Savage SL, et al. Functional genomic landscape of acute myeloid leukaemia. Nature. 2018;562(7728):526–31.Search in Google Scholar

[23] Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med. 2016;374(23):2209–21.Search in Google Scholar

[24] Papaemmanuil E, Gerstung M, Malcovati L, Tauro S, Gundem G, Van Loo P, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122(22):3616–27; quiz 99.Search in Google Scholar

[25] Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.Search in Google Scholar

[26] Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269):pl1.Search in Google Scholar

[27] Wilkerson MD, Hayes DN. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572–3.Search in Google Scholar

[28] Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 2015;31(17):2912–4.Search in Google Scholar

[29] Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7.Search in Google Scholar

[30] Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.Search in Google Scholar

[31] Liu LP, Zhang AL, Ruan M, Chang LX, Liu F, Chen X, et al. Prognostic stratification of molecularly and clinically distinct subgroup in children with acute monocytic leukemia. Cancer Med. 2020;9(11):3647–55.Search in Google Scholar

[32] Gueiderikh A, Maczkowiak-Chartois F, Rosselli F. A new frontier in Fanconi anemia: From DNA repair to ribosome biogenesis. Blood Rev. 2022;52:100904.Search in Google Scholar

[33] Longerich S, Li J, Xiong Y, Sung P, Kupfer GM. Stress and DNA repair biology of the Fanconi anemia pathway. Blood. 2014;124(18):2812–9.Search in Google Scholar

[34] Niedernhofer LJ, Lalai AS, Hoeijmakers JH. Fanconi anemia (cross)linked to DNA repair. Cell. 2005;123(7):1191–8.Search in Google Scholar

[35] Mukhopadhyay SS, Leung KS, Hicks MJ, Hastings PJ, Youssoufian H, Plon SE. Defective mitochondrial peroxiredoxin-3 results in sensitivity to oxidative stress in Fanconi anemia. J Cell Biol. 2006;175(2):225–35.Search in Google Scholar

Received: 2023-06-27
Revised: 2023-10-17
Accepted: 2023-10-19
Published Online: 2023-11-09

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Downloaded on 3.3.2024 from https://www.degruyter.com/document/doi/10.1515/med-2023-0847/html
Scroll to top button