The ER stress related gene panel guide the prognosis and chemosensitivity in acute myeloid leukemia

The ER stress related gene panel guide the prognosis and chemosensitivity in acute myeloid leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The ER stress related gene panel guide the prognosis and chemosensitivity in acute myeloid leukemia Simei Ren, Hongwei Peng, Luyao Long, Jie Guo, Qi Dai, Li Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4088362/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute myeloid leukemia possess high heterogeneity and current European Leukemia Net (ELN) risk stratification system cannot be applicable to all AML patients and needs about 3 weeks testing cycle. The aim of this study was to develop a applicable prognostic tool that may overcome the above shortcomings. Methods We used AML patients collected in clinic and TCGA database to explore the role of ER stress in response to chemotherapy. Patients from the TCGA database were used as the training cohort, and two GEO datasets were used as external validation cohorts. Univariate /multivariate COX and LASSO regression was exemplified to establish the prognostic model. Kaplan-Meier and time-dependent ROC were used to assess and compare the efficiency of the model with ELN stratification and other models. R package "pRRophetic" was utilized to assess drug sensitivity. Results In the training cohort, we selected 5 ER stress-related genes to predict chemosensitivity and establish the ERS-5 prognostic model. The model successfully predicted the overall survival of patients; p < 0.0001, HR = 4.86 (2.79–8.44); AUC = 0.83. The model was verified in validation cohorts and could further stratify the risk of various AML subgroups. It also complemented the ability of ELN to predict the response of patients with AML to main chemotherapeutic drugs. Finally, a “ERS-5” risk score was construced by the nomogram based on the ERS-5 model and age. Conclusions The ERS-5 model allowed more rapid (about 3 hours) and accurate risk stratification and complemented the ability of ELN to assess chemosensitivity. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Acute myeloid leukemia (AML) is a highly heterogeneous and rapidly progressing hematological malignancy [ 1 ]. Therefore, a risk stratification model is urgently needed to provide more precise treatment options. European Leukemia Net (ELN) risk stratification model is a widely adopted prognostic tool for AML, which divides patients with AML into favorable, intermediate, and adverse groups based on cytogenetic abnormalities and gene mutations [ 2 , 3 ]. A comprehensive set of tests is needed to conduct ELN risk stratification for AML patients, including second-generation sequencing (NGS), karyotype analysis, whole-exome sequencing (WES), and bulk RNA sequencing (RNAseq). The entire process typically demands approximately three weeks to complete [ 4 ]. Moreover, the application of ELN stratification for all patients with AML is challenging. Even though ELN categorizes patients with AML into an intermediate-risk group, this classification is accompanied by high heterogeneity. Particularly, 40–45% of patients with AML who fall into the CN-AML category are still stratified as intermediate risk by ELN [ 5 , 6 ]. Notably, cytogenetic abnormalities and gene mutations manifest in a patient-specific manner. A study revealed that the ELN prognostic system cannot be applied to 20.9% of all patients with AML due to the lack of risk-defining cytogenetic abnormalities [ 7 ]. Thus, ELN stratification is an intricate system that needs improvement to provide more accurate results. Besides, the main cause of adverse outcomes in AML is poor chemotherapeutic response, recurrence, or relapse [ 8 ]. Prognosis assessment alone cannot predict chemosensitivity. At the same time, some patients with good prognosis exhibit poor responses to chemotherapy [ 9 ]. Hence, it is crucial to develop a prognostic model that can accurately predict therapeutic response and can be universally applied as a powerful tool to guide individualized treatment of patients with AML. Previously, we reviewed the effect of various mechanisms and pathways, including multidrug resistance, tumor microenvironment, and cellular metabolism, on chemoresistance in AML [ 10 ]. Interestingly, we noticed that endoplasmic reticulum (ER) stress is involved in all these mechanisms [ 11 – 13 ]. ER stress regulates cellular proteostasis in response to external or internal stimuli and determines cell fate through various pro-survival or pro-death mechanisms, such as autophagy, apoptosis, and cell cycle arrest [ 14 , 15 ]. Studies have shown that activated ER stress can enhance the sensitivity of AML cells to chemotherapeutic drugs [ 16 – 20 ]. ER stress is associated with favorable outcomes for patients with AML [ 21 ]. Therefore, ER stress may play a central regulatory role in determining chemoresistance in AML. Given these findings, we selected ER stress-related genes associated with response to chemotherapy to construct a prognostic model, aiming to provide a more efficient model to predict the prognosis as well as the drug response for AML patients. Methods 1. Patients, data sources, and processing method We included AML patients from the clinic and TCGA database for gene enrichment analyses. From 2019 to 2022, mononuclear cells from bone marrow (BM) specimens of 41 patients with AML, including 15 sensitive and 26 refractory and relapsed (R/R) patients, were collected before treatment at the First Affiliated Hospital of Nanchang University and the second hospital of Hebei Medical University. Another 10 patients from the second hospital of Hebei Medical University were also sequenced to verify the model verification. The protocol of this study was approved by the Research Ethics Committee of the second hospital of Hebei Medical University (Approval letter No. 2019-R259). All patients with AML received the recommended treatment based on the 2017 ELN recommendations [ 3 ]. The response to treatment was assessed after two cycles of treatment, and patients were divided into the sensitive or refractory/relapsed (R/R) groups. For the RNA-seq experiment, raw reads were obtained with paired-end 150-base sequencing (Illumina) after constructing the library. Hisat2 software was used to map the raw reads to reference genome sequences. Transcripts were then assembled and quantified using Stringtie software. Library preparation, RNA-Seq experiment, and data processing were performed using Novogene. Differentially expressed genes (DEGs) analysis was performed using the R package “DESeq2”. We then collected the clinical and gene microarray data of new AML patients from bone marrow specimens. The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/legacy-archive/ ) and Gene Expression Omnibus Database (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) were used in this regard. We excluded AML samples without information on overall survival (OS) or French-American-British (FAB) M6/M7 subtypes. In total, 115 specimens from TCGA were utilized as the training cohort due to relatively abundant clinical information. Two GEO datasets were applied for external validation, including 90 samples from GES10358 and 373 samples from GSE37642. In addition, 160 samples from GSE12417 were used for validation among patients with cytogenetically normal AML (CN-AML). All microarray data from TCGA and GEO datasets were used as CEL files and normalized by the Robust Multichip Average (RMA) method (R package “affy”). 2. Functional enrichment analysis Fold changes in gene expression between favorable and adverse groups were obtained by R package “limma”. The Gene Ontology (GO) enrichment and the Gene Set Enrichment Analysis (GSEA) were conducted using R package “ClusterProfiler”. We filtered out significantly enriched GO items with a threshold of adjusted p value 1, p value < 0.05, and q- value < 0.25 were considered significantly enriched in GSEA analysis. 3. Identification of ER stress regulator and construction of prognostic risk signature In total, 723 ER stress-related genes were retrieved from the Gene Ontology database ( http://geneontology.org/ accessed on August 2022). Univariate COX regression was conducted using data from the TCGA cohort to identify ER stress-related prognostic genes. We further conducted LASSO regression to identify candidate genes for risk signature construction (R package “glmnet”). The tuning parameter (λ) was adjusted by ten-fold cross-validation to determine the optimal fitting model. The formula for the risk score of each patient was as follows: \(risk score=\sum _{i}^{N}\beta \text{i}*exp\text{i}\) . β i indicates the coefficients in multivariate COX regression and exp i represents the expression value of the gene. 4. Nomogram construction We constructed a nomogram model using the R package "rms". The nomogram model predicted 1-, 3-, and 5-year survival rates of AML patients. Ten real-world AML patient data from the second hospital of Hebei Medical University were used to verify the nomogram model in clinical practice. 5. Drug sensitivity evaluation We utilized the “pRRophetic” package [ 22 ] (R version 4.1.3) to predict the half-maximal inhibitory concentration (IC 50 ) of doxorubicin, cytarabine, sorafenib, midostaurin, and navitoclax among AML patients. The Genomics of Drug Sensitivity in Cancer (CDSC) v2 dataset “cpg2016” was used as the training matrix to predict IC 50 . 6. Statistical analysis Wilcoxon test was used to compare continuous variables between the two groups, and Kruskal-Wallis was used to compare three groups. Kaplan–Meier and log-rank analysis were used to compare patients’ survival between different risk groups (R package “survival” and “survminer”). Time-dependent receiver operating characteristic curve (tROC) was used to assess the predictive efficacy and the area under the curve (AUC). The R package “timeROC” was also used to compare AUC between the two models. The concordance index (C-index) was calculated to assess the discriminative ability of the risk signature (R package “survival”). A two-sided p -value < 0.05 was considered statistically significant. All analyses were performed using R version 4.2.1. Results 1. Identification of ER stress-related genes associated with response to chemotherapy and prognosis of patients with AML The flowchart of the study is shown in Fig. 1 . We identified 644 DEGs between 15 sensitive and 26 R/R AML patients. Surprisingly, GO enrichment showed that “Response to endoplasmic reticulum stress” and other ER stress-related pathways were most significantly enriched in DEGs (Fig. 2 A). GSEA analysis also showed that “protein processing in endoplasmic reticulum” pathway was significantly enriched and upregulated in sensitive patients (Fig. 2 B). Using data from the TCGA cohort, we analyzed the DEGs between 26 patients in the ELN-stratified favorable risk group and 26 patients in the adverse risk group. GSEA analysis showed that the same ER stress-related KEGG pathway was significantly enriched in ELN-stratified favorable patients (Fig. 2 C). These results suggest that ER stress response-related genes are associated with response to chemotherapy and favorable outcomes in AML. We have enriched 723 ERS related genes in GO database, Univariate COX regression indicated that 81 of 723 ER stress-related genes can affect OS in the training cohort (identified as ERs uniCOX genes; p < 0.05). The Venn diagram presented 20 ERs uniCOX genes that were also DEGs between sensitive and R/R patients (Figs. 2 D and E). 2. Construction of the ERS-5 prognostic model based on data from the training cohort To screen the optimal candidate genes for model construction, we applied LASSO regression on the above-mentioned 20 genes to minimize the risk of overfitting. Finally, 5 ER stress-related genes ( RTN4R , PDIA6 , CYP2E1 , CALCRL , and AREG ) were identified with the optimum λ (Fig. 3 A). We then conducted multivariate COX regression to obtain the coefficient of each gene and established a 5-gene prognostic model (ERS-5) (Fig. 3 B). The formula of the ERS-5 risk score model for each patient was as follows: Risk score = (-0.1540) * RTN4R + (-0.6358) * PDIA6 + (-0.5071) * CYP2E1 + (0.3349) * CALCRL +(0.0897) * AREG Patients were divided into low- and high-risk groups based on the median of the ERS-5 risk score. Compared with the high-risk group, the low-risk group exhibited significantly lower expression of RTN4R , PDIA6 , and CYP2E1 and higher expression of CALCRL and AREG (Fig. 3 C). We applied Kaplan-Meier and time-dependent ROC analysis in the training cohort to evaluate the prognostic ability of the ERS-5 model. Kaplan-Meier analysis showed that the low-risk group experienced a significantly longer OS than the high-risk group (Hazard ratio [HR] = 4.86; 95% confidence interval [CI]: 2.79–8.44; p < 0.0001, Fig. 3 D). Time-dependent ROC analysis revealed the AUC for 3- and 5-year OS prediction were 0.83 and 0.89, respectively (Fig. 3 E). These results suggest that the ERS-5 model has a good performance for evaluating AML prognosis in the training cohort. 3. Evaluation of prognostic efficacy of the ERS-5 model We then employed 90 samples from GES10358 and 373 samples from GSE37642 for external validation (Table 1 .). The Kaplan-Meier plot showed a significant survival difference between the low- and high-risk groups in the GSE10358 (HR = 2.57 95%CI: 1.37–4.80; p = 0.0031) and GSE37642 (HR = 1.7195%CI: 1.34–2.19; p < 0.0001) datasets (Fig. 4 A and B). The AUC for 3-year OS was 0.75 in the GSE10358 cohort and 0.66 in the GSE37642 cohort (Fig. 4 C and D). Table 1 Clinical characteristics of the training cohort and validation cohorts. Characteristics Training Validation TCGA n = 115 GSE10358 n = 90 GSE37642 n = 373 Age, years, No. (%) < 60 64 (55.6) 53 (58.9) 211 (56.6) ≥ 60 51 (44.4) 37 (41.1) 162 (43.4) Gender, No. (%) Male 63 (54.8) 40 (44.4) N/A. Female 52 (45.2) 50 (55.6) N/A. ELN Risk stratification, No. (%) Favorable 26 (22.6) N/A. N/A. Intermediate 63 (54.8) N/A. N/A. Adverse 26 (22.6) N/A. N/A. Genetic Mutations and fusions, No. (%) FLT3 positive 26 (22.6) 23 (20.0) N/A. IDH positive 21 (18.3) 17 (14.8) N/A. NPM1 positive 23 (20.0) 23 (20.0) N/A. DNMT3A positive N/A. 24 (20.9) N/A. RAS positive 4 (3.5) N/A. N/A. PML::RARA positive 57 (49.6) N/A. N/A. BCR::ABL positive 21 (18.3) N/A. N/A. CBFβ positive 39 (33.9) N/A. N/A. KMT2A positive 30 (26.1) N/A. N/A. RUNX1::RUNX1T1 positive 25 (21.7) N/A. N/A. ETV6::RUNX1 positive 6 (5.2) N/A. N/A. Abbreviation: N/A., not applicable. Many studies developed prognostic tools for AML. We compared the prognostic efficacy of the ERS-5 model with that of several published models, including Zeng-121-gene [ 23 ], Yang-10-gene [ 24 ], Li-24-gene [ 25 ], Ng-17-gene [ 26 ], and Pan-13-gene [ 27 ]. Time-dependent ROC analysis demonstrated that the ERS-5 model achieved the highest AUC for 1- to 5-year survival. The C-index of the ER stress-related signature was the highest, suggesting that the ERS-5 model had the highest accuracy among the six models (Fig. 4 E). 4. The ERS-5 model further stratified various genetic subgroups of AML Nevertheless, though AML has been divided into various genetic subgroups, the prognosis and heterogeneity differed within the same subgroup. Thus, more precise stratification methods are urgently needed to tailor the individualized therapy. Thus, in the training cohort, we examined the efficacy of the ERS-5 model in several subgroups using the Kaplan-Meier curve. In both younger (< 60 years) and older (≥ 60 years) patients, the ERS-5 model-defined low-risk group achieved better OS than the high-risk group ( p < 0.0001 and p = 0.0027, respectively, Fig. 5 A). Furthermore, the ERS-5 model significantly discerned the survival difference in patients with specific genetic fusions like RUNX1::RUNX1T1 , PML::RARA , BCR::ABL , KMT2A and CBFβ rearrangement or mutations of NPM1 , FLT3 , and IDH ( p < 0.05, Fig. 5 B). As CN-AML accounted for 40–45% of patients [ 6 ], we employed 160 samples from the GSE12417 dataset to assess the predictive capacity of the ERS-5 model in CN-AML. The low-risk group showed significantly better survival than the high-risk group in CN-AML (HR = 1.54, 95%CI: 1.04–2.27; p = 0.0311, Fig. 5 C). Taken together, these results suggested that the ERS-5 model can be universally applied to diverse AML subgroups with different ages or specific molecular features. 5. Real-world application of the AML prognostic prediction model We assessed the prognostic value of the ERS-5 model, age (cut off at 60), gender, and ELN stratification model by univariate COX regression. The ERS-5 model and age were significantly associated with OS (Fig. 6 A). To provide a more quantitative tool for predicting the prognosis of AML patients, we constructed a nomogram based on the ERS-5 model and age. For each patient, the "ERS-5" risk score was obtained based on the formula of the ERS-5 risk score model: Risk score = (-0.1540) * RTN4R + (-0.6358) * PDIA6 + (-0.5071) * CYP2E1 + (0.3349) * CALCRL +(0.0897) * AREG . The corresponding scores for age were as follows: 12 points for ≥ 60 years and 0 points for < 60 years. The sum of the ERS-5 score and age score was considered the total point to determine the 1-, 3-, and 5-year survival of AML patients (Fig. 6 B). We employed and analyzed 10 AML specimens using the ER5-5 model and compared the calculated survival rates with patients’ actual overall survival (Table 2 ). Afterward, we constructed a fitting curve, which showed a close correlation between the total score and the patients’ actual survival (r = 0.71), suggesting the high accuracy of the nomogram model in predicting patients’ survival rates (Fig. 6 C). Table 2 Nomogram clinical application based on “ERS-5” model and patients’ age Patients No. Scores Calculated survival rates Actual survival time (years) ERS-5 model Age Total* 1 y 3 y 5 y 1 68 12 80 0.55 0.25 0.15 2.00 2 78 0 78 0.62 0.32 0.16 2.20 3 30 12 42 0.89 0.75 0.68 1.56 4 52 12 64 0.75 0.50 0.38 0.81 5 41 12 53 0.82 0.65 0.55 1.08 6 2 12 14 1.00 1.00 0.88 4.48 7 19 12 31 1 0.84 0.78 5.01 8 70 12 82 0.51 0.2 0.1 1.84 9 60 12 72 0.65 0.38 0.24 0.93 10 65 0 65 0.75 0.5 0.42 0.91 Calculated survival rates 1.00 indicates it exceed the predicted values of nomogram (survival rates > 0.9). *Total score indicates ERS-5 model score plus age score according to the nomogram calculation. 6. The ERS-5 model surpassed the ELN risk stratification system in predicting the therapeutic response of AML patients As the ELN risk stratification system has been internationally adopted for predicting AML prognosis, we explored the efficiency of the ELN risk stratification system in the training cohort. Kaplan-Meier analysis showed that the intermediate group experienced a significantly shorter OS compared with the favorable group (HR = 0.25, 95%CI 0.11–0.59; p = 0.0016). The OS of the intermediate group was similar to that of the adverse group (HR = 1.52, 0.89–2.23; p = 0.1281, Fig. 7 A), suggesting that the ELN risk stratification failed to distinguish the OS between intermediate and adverse groups. However, the ERS-5 model successfully divided patients into 3 risk groups (favorable vs. intermediate, HR = 0.30, 95%CI: 0.14–0.64, p = 0.0016; adverse vs. intermediate, HR = 2.02, 95%CI: 1.19–3.45, p < 0.0098, Fig. 7 B). Since the intermediate group was heterogeneous, we utilized the ERS-5 model to further divide the intermediate group into low- and high-risk subgroups, with significantly different survival (HR = 2.81, 95%CI 1.47–5.36; p = 0.0018, Fig. 7 C). These findings suggest that the ERS model was superior to the ELN stratification system by further classifying the intermediate group. Then, we attempted to refine the ELN stratification with the ERS-5 model (“ERS-5 + ELN”). By re-stratifying patients, we investigated whether the ERS-5 model can improve the ELN stratification. Kaplan-Meier analysis indicated that the ERS-5 + ELN model showed a more discernible survival difference (favorable vs. intermediate, HR = 0.20, 95%CI: 0.06–0.67, p = 0.0091; adverse vs. intermediate, HR = 2.69, 95%CI: 1.55–4.66, p < 0.0001, Fig. 7 D). As shown in Fig. 7 E, 3 patients in the favorable group were recognized as having intermediate risk, 36 patients with intermediate-risk (Int-β) were regrouped to the adverse group, and 7 patients from the adverse group (Adv-α) were re-assigned to the intermediate group. Thus, these results demonstrate that ELN stratification system was limited in predicting prognosis, therefore, it was combined with ERS-5 model to improve its predictive performance. According to our results, the ERS-5 model combined with ELN stratification system was superior in predicting the therapeutic response. Since 5 genes of the ERS-5 model were related to therapeutic response, we performed drug response analyses to determine whether the ERS-5 model surpassed the ELN stratification system in predicting the therapeutic response of patients. We compared the IC 50 of doxorubicin, cytarabine, FLT3 inhibitors, sorafenib, midostaurin, and BCL2 inhibitor navitoclax between the risk groups of ELN stratification system and “ERS-5 + ELN” model. The response to navitoclax significantly differed between the ELN-stratified groups. The IC 50 of the favorable group to navitoclax was higher than that of the adverse group, suggesting the ELN stratification failed to predict the therapeutic response to all 5 drugs and thus failed to predict the OS. According to Fig. 7 F, the Int-β group was significantly more resistant than the Adv-α group, suggesting the ERS-5 model combined with the ELN stratification system could more accurately predict the response to chemotherapy. These results demonstrated that the ERS-5 model can improve the predictive ability of the ELN stratification system. As we compared the accuracy of the ERS-5 model, ELN stratification model, and “ERS-5 + ELN” model, time-dependent ROC analysis showed the 2-, 3-, and 5-year survival AUC of the “ERS-5 + ELN” model was significantly higher than that of the ELN stratification model. There was no significant difference between the ERS-5 model and the “ERS-5 + ELN” model in this regard (Fig. 7 G). Given the relatively long turnaround time of ELN stratification, the ERS-5 model also surpassed the “ERS-5 + ELN” model. The results suggested that the ERS-5 model was superior to the ELN stratification system and improved the predictive ability of the ELN stratification system. Discussion Poor outcomes of AML patients are largely due to drug resistance, which occurs through various mechanisms [ 10 , 15 ]. Among these mechanisms, ER stress seems to play a pivotal role in determining cell fate and drug resistance[ 11 , 14 , 29 ]. In this study, we found that ER stress can significantly differentiate between sensitive and R/R patients and also between patients with favorable prognosis and patients with adverse prognosis. These findings suggested that ER stress-related genes are promising prognostic indicators for response to chemotherapy and outcomes of AML patients. Thus, we developed a prognostic model, ERS-5, based on the ER stress-related genes and verified the model in both training and validation cohorts. According to our results, the ERS-5 model showed high accuracy in evaluating the OS of AML patients in both training and validation cohorts. Although there are already some prognostic models [ 23 – 27 ], we proved that the ERS-5 model can surpass these models with the highest AUC and C-index and a streamlined set of genes. More importantly, our model is simpler and more efficient, reducing the measurement time from about 3 weeks to about 3 hours by detecting nucleic acid expression levels of RTN4R, PDIA6, CYP2E1, CALCRL, and AREG genes. The current ELN stratification system has some limitations. It was known that 40–45% of AML patients are CN-AML who will be stratified as intermediate risk by ELN stratification, but show marked heterogeneity [ 5 , 6 ]. Interestingly, the ERS-5 model can significantly stratify the OS of CN-AML patients and the intermediate group, suggesting the superiority of the ERS-5 model compared with the ELN stratification model, especially for the intermediate-risk group. Moreover, though AML has been divided into different cytogenetic subgroups, still significant heterogeneity exists. The same subgroup has different outcomes after standard treatment. Interestingly, the ERS-5 model successfully distinguished OS based on age, and varied genetic fusions and some common mutations ( NPM1 , FLT3 and IDH ),[ 28 – 31 ](Fig. 5 ), suggesting that different groups of patients can benefit from the model. Consequently, based on the preferable results of the ERS-5 model, we constructed a nomogram that integrated demographic characteristics (age) with the ERS-5 model to provide a more accurate final score and guide treatment. The incidence of AML was much higher in the elderly population, nevertheless, the poor prognosis was more correlated with age [ 38 , 39 ].Older patients receive lower-intensity treatment [ 40 , 41 ], but some of them would still benefit from intensive treatment [ 42 ]. Thus, it is critical to stratify the prognosis of older patients [ 39 , 43 ]. According to Fig. 6 , the ERS-5 model could distinguish between the low- and high-risk groups among older patients, suggesting that low-risk older patients might be candidates for more intensive treatments. The nomogram provides a visual representation of the probabilities of 1-, 3-, and 5-year survival, aiding clinicians in making therapeutic decisions based on individualized risk scores (calculated by the ERS-5 model) and patient’s age. This integrated approach aims to optimize treatment strategies, particularly for older patients, contributing to more personalized and effective patient care. Furthermore, the ERS-5 model could evaluate the therapeutic response of AML patients. The ERS-5 model surpassed the ELN stratification system in predicting the therapeutic response of patients to several drugs, such as chemotherapy drug cytarabine, FLT3 inhibitors sorafenib, and BCL2 inhibitor navitoclax (Fig. 7 ). Cytarabine is a chemotherapy agent that is used for intensive and consolidation therapy [ 32 ]. However, due to unpredictable response rate, the dose and duration of cytarabine in consolidation therapy remain controversial [ 33 , 34 ]. The ERS-5 model can identify patients who are fit for consolidation therapy with high-dose cytarabine (Fig. 7 F). BCL2 is also a well-known downstream target of ER stress response [ 14 , 35 ]. Venetoclax, a BCL2 inhibitor, has drawn a lot of attention in the treatment of AML especially among R/R AML patients [ 32 , 36 ]. The combination of venetoclax and HMAs (hypomethylating agents, HMAs) has been recommended by NCCN guidelines for AML patients which is unfit for high-dose chemotherapies[ 37 ]. However, approximately 65% complete response rate and 35% poor response of venetoclax have been reported. The ERS-5 model also predicted the chemotherapeutic response in patients who were relatively sensitive or resistant to BCL2 inhibitor navitoclax, thus assisting the individualized application of BCL2 inhibitors for patients with AML. Given its efficacy and extensive application, the ERS-5 model may be a potential indicator for predicting the response to venetoclax. Above all, this study provided a novel prognostic model and demonstrated its accuracy and potential application in AML. Although the ERS-5 model performed well, the present study had some limitations. The nomogram constructed by ERS-5 model, combined with age, was only successfully used for 10 AML patients. Therefore, it is necessary to verify the efficacy of the ERS-5 model with more clinical data. Besides, the ERS-5 model can predict chemo-response by measuring the copy numbers of the five genes, the function and mechanism of these 5 genes should be elucidated in future studies. Our findings underscore the potential of the ERS-5 model as a robust tool in predicting the prognosis of AML patients, although future studies and validation efforts are crucial. Conclusions In summary, as an efficient and practical tool, the ERS-5 model surpasses the ELN stratification system and can be independently utilized to evaluate the prognosis and guide the treatment of AML patients. Abbreviations AML Acute myeloid leukemia ELN European Leukemia Net ER endoplasmic reticulum NGS second-generation sequencing WES whole-exome sequencing RNAsea RNA sequencing BM bone marrow R/R refractory and relapsed DEGs Differentially expressed genes TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus Database OS overall survival FAB French-American-British CN-AML cytogenetically normal AML RMA Robust Multichip Average GO Gene Ontology GSEA Gene Set Enrichment Analysis KEGG Kyoto Encyclopedia of Genes and Genomes IC50 half-maximal inhibitory concentration CDSC Genomics of Drug Sensitivity in Cancer T- ROC U- Time-dependent receiver operating characteristic curve AUC area under the curve C- index D- concordance index ERS-5 5-gene prognostic model Declarations Ethics approval and consent to participate The protocol of this study was approved by the Research Ethics Committee of the second hospital of Hebei Medical University (Approval letter No. 2019-R259). Consent for publication All authors approved the final manuscript and the submission to this journal. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author Simei Ren. on reasonable request. Competing interests The authors declare no conflict of interest. Funding This work was supported by grants from the Beijing Natural Science Foundation (No. J230022, L222125), National Natural Science Foundation of China (No.81670161). Authors’ contributions Ren SM designed and supervised this study, analyzed data, wrote and edited the manuscript. Peng HW edited and revised the manuscript. Long LY analyzed data and generated the figures and tables. Guo J collected and analyzed data, made the statistical comparation. Dai Q revised the figures as well as made the data proofreading. Yang L and Sun L collected specimens and data, as well as patients follow-up. 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Cancer Sci 108:1793–1802 Nishimura N, Radwan MO, Amano M, Endo S, Fujii E, Hayashi H et al (2019) Novel p97/ VCP inhibitor induces endoplasmic reticulum stress and apoptosis in both bortezomib-sensitive and ‐resistant multiple myeloma cells. Cancer Sci 110:3275–3287 Schardt JA, Weber D, Eyholzer M, Mueller BU, Pabst T (2009) Activation of the Unfolded Protein Response Is Associated with Favorable Prognosis in Acute Myeloid Leukemia. Clin Cancer Res 15:3834–3841 Geeleher P, Cox N, Huang RS, pRRophetic (2014) An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. Barbour JD, editor. PLoS ONE. ;9:e107468 Zeng T, Cui L, Huang W, Liu Y, Si C, Qian T et al (2021) The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia. BMC Med 19:176 Yang Z, Shang J, Li N, Zhang L, Tang T, Tian G et al (2020) Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia. J Cell Mol Med 24:4510–4523 Li Z, Herold T, He C, Valk PJM, Chen P, Jurinovic V et al (2013) Identification of a 24-Gene Prognostic Signature That Improves the European LeukemiaNet Risk Classification of Acute Myeloid Leukemia: An International Collaborative Study. JCO 31:1172–1181 Ng SWK, Mitchell A, Kennedy JA, Chen WC, McLeod J, Ibrahimova N et al (2016) A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540:433–437 Pan M, Zhou J, Jiao C, Ge J (2023) Bioinformatics analysis of the endoplasmic reticulum stress-related prognostic model and immune cell infiltration in acute myeloid leukemia. Hematology 28:2221101 Shoukier M, Kadia T, Konopleva M, Alotaibi AS, Alfayez M, Loghavi S et al (2021) Clinical characteristics and outcomes in patients with acute myeloid leukemia with concurrent FLT3 -ITD and IDH mutations. Cancer 127:381–390 Martelli MP, Sportoletti P, Tiacci E, Martelli MF, Falini B (2013) Mutational landscape of AML with normal cytogenetics: Biological and clinical implications. Blood Rev 27:13–22 Wang M, Yang C, Zhang L, Schaar DG (2017) Molecular Mutations and Their Cooccurrences in Cytogenetically Normal Acute Myeloid Leukemia. Stem Cells Int 2017:6962379 Kiyoi H, Kawashima N, Ishikawa Y (2020) FLT3 mutations in acute myeloid leukemia: Therapeutic paradigm beyond inhibitor development. Cancer Sci 111:312–322 Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H et al (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 140:1345–1377 Wei H, Wang Y, Gale RP, Lin D, Zhou C, Liu B et al (2020) Randomized Trial of Intermediate-dose Cytarabine in Induction and Consolidation Therapy in Adults with Acute Myeloid Leukemia. Clin Cancer Res 26:3154–3161 Weigert N, Rowe JM, Lazarus HM, Salman MY (2022) Consolidation in AML: Abundant opinion and much unknown. Blood Rev 51:100873 Radha G, Raghavan SC (2017) BCL2: A promising cancer therapeutic target. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1868:309–314 Wei Y, Xiong X, Li X, Lu W, He X, Jin X et al (2021) Low-dose decitabine plus venetoclax is safe and effective as post‐transplant maintenance therapy for high‐risk acute myeloid leukemia and myelodysplastic syndrome. Cancer Sci 112:3636–3644 Mistry JJ, Hellmich C, Lambert A, Moore JA, Jibril A, Collins A et al (2021) Venetoclax and Daratumumab combination treatment demonstrates pre-clinical efficacy in mouse models of Acute Myeloid Leukemia. Biomark Res 9:35 Short NJ, Rytting ME, Cortes JE (2018) Acute myeloid leukaemia. Lancet 392:593–606 Arellano M, Carlisle JW (2018) How I treat older patients with acute myeloid leukemia: Treating Older Patients with AML. Cancer 124:2472–2483 Medeiros BC, Pandya BJ, Hadfield A, Pike J, Wilson S, Mueller C et al (2019) Treatment patterns in patients with acute myeloid leukemia in the United States: a cross-sectional, real-world survey. Curr Med Res Opin 35:927–935 Russell NH, Hills RK, Thomas A, Thomas I, Kjeldsen L, Dennis M et al (2021) Outcomes of older patients aged 60 to 70 years undergoing reduced intensity transplant for acute myeloblastic leukemia: results of the NCRI acute myeloid leukemia 16 trial. haematol 107:1518–1527 Vey N (2020) Low-intensity regimens versus standard-intensity induction strategies in acute myeloid leukemia. Therapeutic Adv Hematol 11:204062072091301 Pastore F, Pastore A, Rothenberg-Thurley M, Metzeler KH, Ksienzyk B, Schneider S et al (2022) Molecular profiling of patients with cytogenetically normal acute myeloid leukemia and hyperleukocytosis. Cancer 128:4213–4222 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4088362","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280741844,"identity":"c9b35ebe-8dbc-48bf-9c3a-78bd038d8db9","order_by":0,"name":"Simei Ren","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYLCCBAYGHsZm5oMPPjawgdhEamFuZ0s2nEm0FhBg7+dRk+ZtYCCsxby99/CHhzk2MrzNPAzStjv48nTbew8w/NyBW4vMmXNpEonb0ngkm3kPGOeeYSs2O3MugbH3DG4tEhI5ZgyJ2w7zGDbzJSTntrElbruRY8DM2IZHi/wb4w+J2/7z2B/mMThsSZQWCR4DoMMOAAMZaBEjUVp4csyAWpKBWtiSgV4AajlzxuBgLz4t7GeMP/7cZmfP2H/4+I+fO44lbjveY/jgJx4t6OAYmDxAvAYGhhpSFI+CUTAKRsEIAQAu1VKAoPm2qwAAAABJRU5ErkJggg==","orcid":"","institution":"National Center for Clinical Laboratories, Chinese Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Simei","middleName":"","lastName":"Ren","suffix":""},{"id":280741846,"identity":"5c90472b-ad99-44c7-9ea3-0ecf92163406","order_by":1,"name":"Hongwei Peng","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Peng","suffix":""},{"id":280741848,"identity":"c0f5c95d-81fb-46b9-92cd-1bc418047ecc","order_by":2,"name":"Luyao Long","email":"","orcid":"","institution":"National Center for Clinical Laboratories, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Luyao","middleName":"","lastName":"Long","suffix":""},{"id":280741850,"identity":"eaa29e1d-817d-4c78-9dd0-a2e7d2cd81f4","order_by":3,"name":"Jie Guo","email":"","orcid":"","institution":"National Center for Clinical Laboratories, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Guo","suffix":""},{"id":280741852,"identity":"a6e2d408-f51e-4d1d-bd08-abb24f82af54","order_by":4,"name":"Qi Dai","email":"","orcid":"","institution":"National Center for Clinical Laboratories, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Dai","suffix":""},{"id":280741853,"identity":"171e6656-3554-4c28-98d3-b9f138ea7691","order_by":5,"name":"Li Sun","email":"","orcid":"","institution":"The second hospital of Hebei medical university","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Sun","suffix":""},{"id":280741856,"identity":"982424d3-edca-4049-afca-15aa8c35c857","order_by":6,"name":"Lin Yang","email":"","orcid":"","institution":"The second hospital of Hebei medical university","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-03-13 01:55:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4088362/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4088362/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53183853,"identity":"44376386-0a0b-46fb-a49f-98cb107296a5","added_by":"auto","created_at":"2024-03-21 16:03:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study. \u003c/strong\u003eR/R, refractory/relapsed; CN-AML, cytogenetic normal AML.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/2c94490c534214d739d931b6.png"},{"id":53183859,"identity":"2e7ff624-3690-4827-97ec-ebf2349cd443","added_by":"auto","created_at":"2024-03-21 16:03:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":378609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of ER stress-related genes associated with chemotherapy responses and prognoses of AML patients.\u003c/strong\u003e (A) GO enrichment analysis of DEGs between sensitive and R/R AML patients. (B and C) GSEA enrichment between sensitive and R/R patients (B) and between AML patients with favorable- and adverse-risk by the ELN risk stratification in the training cohort (C). (D) Filtration of candidate genes. (E) Forest plot of univariate COX regression of the 20 prognostic ER stress-related genes. DEGs, differentially expressed genes; p.adjust, adjusted \u003cem\u003ep\u003c/em\u003e value. R/R, refractory/relapsed; ERs_uniCOX genes, ER stress-related genes with \u003cem\u003ep\u003c/em\u003evalues \u0026lt;0.05 by COX regression.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/da4d7978d4921e51288b657c.png"},{"id":53183854,"identity":"d6f37dc9-5758-4bb1-843c-b5ed823f7e6a","added_by":"auto","created_at":"2024-03-21 16:03:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164985,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of ERS-5 prognostic model in the training cohort. \u003c/strong\u003e(A) LASSO-COX regression of the 20 survival-related genes (left) and cross-validation for the tuning parameter (λ) selection in the LASSO regression (right). (B) The coefficients of the final 5 genes which were analyzed by multivariate COX regression. (C) The expression comparisons of the 5 genes between the low- and high-risk groups. (**, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001; ****, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001) (D) Kaplan-Meier survival\u003cstrong\u003e \u003c/strong\u003ecurves for the low- and high-risk groups with log-rank \u003cem\u003ep\u003c/em\u003e value and HR in training cohort. (E) Time-dependent ROC curve of the ERS-5 model. HR, hazard ratio.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/8157d08c174be6bc40bb32c0.png"},{"id":53183858,"identity":"d7d90417-9349-4547-ae8a-86e8b4d885f8","added_by":"auto","created_at":"2024-03-21 16:03:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of prognostic efficacy of the ERS-5 model\u003c/strong\u003e. (A-D) Kaplan-Meier analysis (above) for the low- and high-risk groups with log-rank \u003cem\u003ep\u003c/em\u003evalue and HR and time-dependent ROC curve with AUC values(below) in GSE10358 and GSE37642 datasets. The low- and high-risk groups were cut off at median of the ERS-5 model. (E) The time-dependent AUC values (left) and C-indexes (right) of five models. HR: hazard ratio.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/04004479de96f88d0e3f1a83.png"},{"id":53185703,"identity":"04e947b6-0922-467e-8770-ce0701c66515","added_by":"auto","created_at":"2024-03-21 16:11:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":290389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eERS-5 model further stratified AML subgroups. \u003c/strong\u003e(A-C) Kaplan-Meier survival\u003cstrong\u003e \u003c/strong\u003ecurves for the subgroups of different age (A), genetic fusions and mutations (B) and CN-AML (C) with log-rank \u003cem\u003ep\u003c/em\u003e values and HR. The low- and high-risk groups were cut off at the median of ERS-5 model. CN-AML: cytogenetic normal AML. HR: hazard ratio.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/c2910e70bba20f7f3ca8e915.png"},{"id":53183856,"identity":"4216548c-705a-4b1e-963d-97e3373e1775","added_by":"auto","created_at":"2024-03-21 16:03:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":150491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of nomogram with ERS-5 and age. \u003c/strong\u003e(A) Univariate COX regression of ERS-5 model, age, gender and ELN risk stratification. (B) Nomogram to predict 1, 3, and 5 years OS in AML patients. (C) The fitting curve of the total score and the actual survival time(r=0.705). The total score calculated by the risk score and age score.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/4053bfa82f7b9ba7e19dadee.png"},{"id":53183855,"identity":"da2a99e5-e3b5-4888-af83-e3fac6e34d0e","added_by":"auto","created_at":"2024-03-21 16:03:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":266639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eERS-5 model surpassed the ELN risk stratification system by evaluating drug responses of AML patients. \u003c/strong\u003e(A) Kaplan-Meier survival\u003cstrong\u003e \u003c/strong\u003ecurve for the ELN risk stratification with log-rank \u003cem\u003ep\u003c/em\u003e value (favorable/adverse vs. intermediate group). (B) Kaplan-Meier survival\u003cstrong\u003e \u003c/strong\u003ecurve for the ERS-5 model with log-rank \u003cem\u003ep\u003c/em\u003e value (favorable/adverse vs. intermediate group). (C) Kaplan-Meier survival\u003cstrong\u003e \u003c/strong\u003ecurve for the high-risk and low-risk groups cut off at median of ERS-5 model in ELN intermediate group. (D) Kaplan-Meier survival\u003cstrong\u003e \u003c/strong\u003ecurve for the reclassified groups of the “ERS-5 + ELN” model. (E) Reclassification of patients from the three ELN risk groups to the newly generated “ERS-5 + ELN” model. (F) The predicted IC\u003csub\u003e50\u003c/sub\u003e of five drugs between risk groups of ELN stratification (above, left), low- and high-risk group of ERS-5 model (above, right) and Int-β and Adv-α group of “ERS-5 + ELN” model (below). (G) The time-dependent AUC values of ERS-5, ELN stratification and “ERS-5 + ELN” model.\u0026nbsp; (*, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; NS., no significance)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/3d26fab398dac0591a783e26.png"},{"id":55537051,"identity":"7a081175-0f1c-4494-b5ed-2415705aa3b6","added_by":"auto","created_at":"2024-04-29 16:40:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1793330,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4088362/v1/201fe370-0bba-4a48-809e-f670dc31b26e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The ER stress related gene panel guide the prognosis and chemosensitivity in acute myeloid leukemia","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a highly heterogeneous and rapidly progressing hematological malignancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, a risk stratification model is urgently needed to provide more precise treatment options. European Leukemia Net (ELN) risk stratification model is a widely adopted prognostic tool for AML, which divides patients with AML into favorable, intermediate, and adverse groups based on cytogenetic abnormalities and gene mutations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A comprehensive set of tests is needed to conduct ELN risk stratification for AML patients, including second-generation sequencing (NGS), karyotype analysis, whole-exome sequencing (WES), and bulk RNA sequencing (RNAseq). The entire process typically demands approximately three weeks to complete [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, the application of ELN stratification for all patients with AML is challenging. Even though ELN categorizes patients with AML into an intermediate-risk group, this classification is accompanied by high heterogeneity. Particularly, 40\u0026ndash;45% of patients with AML who fall into the CN-AML category are still stratified as intermediate risk by ELN [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, cytogenetic abnormalities and gene mutations manifest in a patient-specific manner. A study revealed that the ELN prognostic system cannot be applied to 20.9% of all patients with AML due to the lack of risk-defining cytogenetic abnormalities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, ELN stratification is an intricate system that needs improvement to provide more accurate results. Besides, the main cause of adverse outcomes in AML is poor chemotherapeutic response, recurrence, or relapse [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Prognosis assessment alone cannot predict chemosensitivity. At the same time, some patients with good prognosis exhibit poor responses to chemotherapy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Hence, it is crucial to develop a prognostic model that can accurately predict therapeutic response and can be universally applied as a powerful tool to guide individualized treatment of patients with AML.\u003c/p\u003e \u003cp\u003ePreviously, we reviewed the effect of various mechanisms and pathways, including multidrug resistance, tumor microenvironment, and cellular metabolism, on chemoresistance in AML [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Interestingly, we noticed that endoplasmic reticulum (ER) stress is involved in all these mechanisms [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. ER stress regulates cellular proteostasis in response to external or internal stimuli and determines cell fate through various pro-survival or pro-death mechanisms, such as autophagy, apoptosis, and cell cycle arrest [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have shown that activated ER stress can enhance the sensitivity of AML cells to chemotherapeutic drugs [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. ER stress is associated with favorable outcomes for patients with AML [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, ER stress may play a central regulatory role in determining chemoresistance in AML. Given these findings, we selected ER stress-related genes associated with response to chemotherapy to construct a prognostic model, aiming to provide a more efficient model to predict the prognosis as well as the drug response for AML patients.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003e1. Patients, data sources, and processing method\u003c/p\u003e \u003cp\u003eWe included AML patients from the clinic and TCGA database for gene enrichment analyses. From 2019 to 2022, mononuclear cells from bone marrow (BM) specimens of 41 patients with AML, including 15 sensitive and 26 refractory and relapsed (R/R) patients, were collected before treatment at the First Affiliated Hospital of Nanchang University and the second hospital of Hebei Medical University. Another 10 patients from the second hospital of Hebei Medical University were also sequenced to verify the model verification. The protocol of this study was approved by the Research Ethics Committee of the second hospital of Hebei Medical University (Approval letter No. 2019-R259). All patients with AML received the recommended treatment based on the 2017 ELN recommendations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The response to treatment was assessed after two cycles of treatment, and patients were divided into the sensitive or refractory/relapsed (R/R) groups. For the RNA-seq experiment, raw reads were obtained with paired-end 150-base sequencing (Illumina) after constructing the library. Hisat2 software was used to map the raw reads to reference genome sequences. Transcripts were then assembled and quantified using Stringtie software. Library preparation, RNA-Seq experiment, and data processing were performed using Novogene. Differentially expressed genes (DEGs) analysis was performed using the R package \u0026ldquo;DESeq2\u0026rdquo;.\u003c/p\u003e \u003cp\u003eWe then collected the clinical and gene microarray data of new AML patients from bone marrow specimens. The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/legacy-archive/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/legacy-archive/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Gene Expression Omnibus Database (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e were used in this regard. We excluded AML samples without information on overall survival (OS) or French-American-British (FAB) M6/M7 subtypes. In total, 115 specimens from TCGA were utilized as the training cohort due to relatively abundant clinical information. Two GEO datasets were applied for external validation, including 90 samples from GES10358 and 373 samples from GSE37642. In addition, 160 samples from GSE12417 were used for validation among patients with cytogenetically normal AML (CN-AML). All microarray data from TCGA and GEO datasets were used as CEL files and normalized by the Robust Multichip Average (RMA) method (R package \u0026ldquo;affy\u0026rdquo;).\u003c/p\u003e \u003cp\u003e2. Functional enrichment analysis\u003c/p\u003e \u003cp\u003eFold changes in gene expression between favorable and adverse groups were obtained by R package \u0026ldquo;limma\u0026rdquo;. The Gene Ontology (GO) enrichment and the Gene Set Enrichment Analysis (GSEA) were conducted using R package \u0026ldquo;ClusterProfiler\u0026rdquo;. We filtered out significantly enriched GO items with a threshold of adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The Benjamini-Hochberg method was used for multiple hypothesis testing. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathways with the absolute value of normalized enrichment score (|NES|)\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and \u003cem\u003eq-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered significantly enriched in GSEA analysis.\u003c/p\u003e \u003cp\u003e3. Identification of ER stress regulator and construction of prognostic risk signature\u003c/p\u003e \u003cp\u003eIn total, 723 ER stress-related genes were retrieved from the Gene Ontology database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003cspan address=\"http://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed on August 2022). Univariate COX regression was conducted using data from the TCGA cohort to identify ER stress-related prognostic genes. We further conducted LASSO regression to identify candidate genes for risk signature construction (R package \u0026ldquo;glmnet\u0026rdquo;). The tuning parameter (λ) was adjusted by ten-fold cross-validation to determine the optimal fitting model. The formula for the risk score of each patient was as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(risk score=\\sum _{i}^{N}\\beta \\text{i}*exp\\text{i}\\)\u003c/span\u003e\u003c/span\u003e. β\u003csub\u003ei\u003c/sub\u003e indicates the coefficients in multivariate COX regression and exp\u003csub\u003ei\u003c/sub\u003e represents the expression value of the gene.\u003c/p\u003e \u003cp\u003e4. Nomogram construction\u003c/p\u003e \u003cp\u003eWe constructed a nomogram model using the R package \"rms\". The nomogram model predicted 1-, 3-, and 5-year survival rates of AML patients. Ten real-world AML patient data from the second hospital of Hebei Medical University were used to verify the nomogram model in clinical practice.\u003c/p\u003e \u003cp\u003e5. Drug sensitivity evaluation\u003c/p\u003e \u003cp\u003eWe utilized the \u0026ldquo;pRRophetic\u0026rdquo; package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (R version 4.1.3) to predict the half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) of doxorubicin, cytarabine, sorafenib, midostaurin, and navitoclax among AML patients. The Genomics of Drug Sensitivity in Cancer (CDSC) v2 dataset \u0026ldquo;cpg2016\u0026rdquo; was used as the training matrix to predict IC\u003csub\u003e50\u003c/sub\u003e.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e6. Statistical analysis\u003c/h2\u003e \u003cp\u003eWilcoxon test was used to compare continuous variables between the two groups, and Kruskal-Wallis was used to compare three groups. Kaplan\u0026ndash;Meier and log-rank analysis were used to compare patients\u0026rsquo; survival between different risk groups (R package \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer\u0026rdquo;). Time-dependent receiver operating characteristic curve (tROC) was used to assess the predictive efficacy and the area under the curve (AUC). The R package \u0026ldquo;timeROC\u0026rdquo; was also used to compare AUC between the two models. The concordance index (C-index) was calculated to assess the discriminative ability of the risk signature (R package \u0026ldquo;survival\u0026rdquo;). A two-sided \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed using R version 4.2.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e1. Identification of ER stress-related genes associated with response to chemotherapy and prognosis of patients with AML\u003c/p\u003e \u003cp\u003eThe flowchart of the study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We identified 644 DEGs between 15 sensitive and 26 R/R AML patients. Surprisingly, GO enrichment showed that \u0026ldquo;Response to endoplasmic reticulum stress\u0026rdquo; and other ER stress-related pathways were most significantly enriched in DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). GSEA analysis also showed that \u0026ldquo;protein processing in endoplasmic reticulum\u0026rdquo; pathway was significantly enriched and upregulated in sensitive patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Using data from the TCGA cohort, we analyzed the DEGs between 26 patients in the ELN-stratified favorable risk group and 26 patients in the adverse risk group. GSEA analysis showed that the same ER stress-related KEGG pathway was significantly enriched in ELN-stratified favorable patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). These results suggest that ER stress response-related genes are associated with response to chemotherapy and favorable outcomes in AML. We have enriched 723 ERS related genes in GO database, Univariate COX regression indicated that 81 of 723 ER stress-related genes can affect OS in the training cohort (identified as ERs uniCOX genes; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Venn diagram presented 20 ERs uniCOX genes that were also DEGs between sensitive and R/R patients (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e2. Construction of the ERS-5 prognostic model based on data from the training cohort\u003c/p\u003e \u003cp\u003eTo screen the optimal candidate genes for model construction, we applied LASSO regression on the above-mentioned 20 genes to minimize the risk of overfitting. Finally, 5 ER stress-related genes (\u003cem\u003eRTN4R\u003c/em\u003e, \u003cem\u003ePDIA6\u003c/em\u003e, \u003cem\u003eCYP2E1\u003c/em\u003e, \u003cem\u003eCALCRL\u003c/em\u003e, and \u003cem\u003eAREG\u003c/em\u003e) were identified with the optimum λ (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We then conducted multivariate COX regression to obtain the coefficient of each gene and established a 5-gene prognostic model (ERS-5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The formula of the ERS-5 risk score model for each patient was as follows:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRisk score = (-0.1540) * \u003cem\u003eRTN4R\u003c/em\u003e+ (-0.6358) * \u003cem\u003ePDIA6\u003c/em\u003e+ (-0.5071) * \u003cem\u003eCYP2E1\u003c/em\u003e+ (0.3349) * \u003cem\u003eCALCRL\u003c/em\u003e+(0.0897) *\u003cem\u003eAREG\u003c/em\u003e\u003c/p\u003e \u003cp\u003ePatients were divided into low- and high-risk groups based on the median of the ERS-5 risk score. Compared with the high-risk group, the low-risk group exhibited significantly lower expression of \u003cem\u003eRTN4R\u003c/em\u003e, \u003cem\u003ePDIA6\u003c/em\u003e, and \u003cem\u003eCYP2E1\u003c/em\u003e and higher expression of \u003cem\u003eCALCRL\u003c/em\u003e and \u003cem\u003eAREG\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We applied Kaplan-Meier and time-dependent ROC analysis in the training cohort to evaluate the prognostic ability of the ERS-5 model. Kaplan-Meier analysis showed that the low-risk group experienced a significantly longer OS than the high-risk group (Hazard ratio [HR]\u0026thinsp;=\u0026thinsp;4.86; 95% confidence interval [CI]: 2.79\u0026ndash;8.44; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Time-dependent ROC analysis revealed the AUC for 3- and 5-year OS prediction were 0.83 and 0.89, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). These results suggest that the ERS-5 model has a good performance for evaluating AML prognosis in the training cohort.\u003c/p\u003e \u003cp\u003e3. Evaluation of prognostic efficacy of the ERS-5 model\u003c/p\u003e \u003cp\u003eWe then employed 90 samples from GES10358 and 373 samples from GSE37642 for external validation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.). The Kaplan-Meier plot showed a significant survival difference between the low- and high-risk groups in the GSE10358 (HR\u0026thinsp;=\u0026thinsp;2.57 95%CI: 1.37\u0026ndash;4.80; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0031) and GSE37642 (HR\u0026thinsp;=\u0026thinsp;1.7195%CI: 1.34\u0026ndash;2.19; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B). The AUC for 3-year OS was 0.75 in the GSE10358 cohort and 0.66 in the GSE37642 cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and D).\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\u003eClinical characteristics of the training cohort and validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCGA\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;115\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSE10358\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;90\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGSE37642\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;373\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, No. (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211 (56.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162 (43.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, No. (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELN Risk stratification, No. (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic Mutations and fusions, No. (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFLT3\u003c/em\u003e positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIDH\u003c/em\u003e positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPM1\u003c/em\u003e positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNMT3A positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRAS\u003c/em\u003e positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePML::RARA positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCR::ABL positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCBFβ\u003c/em\u003e positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRUNX1::RUNX1T1 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETV6::RUNX1 positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviation: N/A., not applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMany studies developed prognostic tools for AML. We compared the prognostic efficacy of the ERS-5 model with that of several published models, including Zeng-121-gene [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Yang-10-gene [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Li-24-gene [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Ng-17-gene [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and Pan-13-gene [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Time-dependent ROC analysis demonstrated that the ERS-5 model achieved the highest AUC for 1- to 5-year survival. The C-index of the ER stress-related signature was the highest, suggesting that the ERS-5 model had the highest accuracy among the six models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e4. The ERS-5 model further stratified various genetic subgroups of AML\u003c/p\u003e \u003cp\u003eNevertheless, though AML has been divided into various genetic subgroups, the prognosis and heterogeneity differed within the same subgroup. Thus, more precise stratification methods are urgently needed to tailor the individualized therapy. Thus, in the training cohort, we examined the efficacy of the ERS-5 model in several subgroups using the Kaplan-Meier curve. In both younger (\u0026lt;\u0026thinsp;60 years) and older (\u0026ge;\u0026thinsp;60 years) patients, the ERS-5 model-defined low-risk group achieved better OS than the high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0027, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Furthermore, the ERS-5 model significantly discerned the survival difference in patients with specific genetic fusions like \u003cem\u003eRUNX1::RUNX1T1\u003c/em\u003e, \u003cem\u003ePML::RARA\u003c/em\u003e, \u003cem\u003eBCR::ABL\u003c/em\u003e, \u003cem\u003eKMT2A\u003c/em\u003e and \u003cem\u003eCBFβ\u003c/em\u003e rearrangement or mutations of \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, and \u003cem\u003eIDH\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). As CN-AML accounted for 40\u0026ndash;45% of patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], we employed 160 samples from the GSE12417 dataset to assess the predictive capacity of the ERS-5 model in CN-AML. The low-risk group showed significantly better survival than the high-risk group in CN-AML (HR\u0026thinsp;=\u0026thinsp;1.54, 95%CI: 1.04\u0026ndash;2.27; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0311, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Taken together, these results suggested that the ERS-5 model can be universally applied to diverse AML subgroups with different ages or specific molecular features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e5. Real-world application of the AML prognostic prediction model\u003c/p\u003e \u003cp\u003eWe assessed the prognostic value of the ERS-5 model, age (cut off at 60), gender, and ELN stratification model by univariate COX regression. The ERS-5 model and age were significantly associated with OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To provide a more quantitative tool for predicting the prognosis of AML patients, we constructed a nomogram based on the ERS-5 model and age. For each patient, the \"ERS-5\" risk score was obtained based on the formula of the ERS-5 risk score model: Risk score = (-0.1540) * \u003cem\u003eRTN4R\u003c/em\u003e+ (-0.6358) * \u003cem\u003ePDIA6\u003c/em\u003e+ (-0.5071) * \u003cem\u003eCYP2E1\u003c/em\u003e+ (0.3349) * \u003cem\u003eCALCRL\u003c/em\u003e+(0.0897) *\u003cem\u003eAREG\u003c/em\u003e. The corresponding scores for age were as follows: 12 points for \u0026ge;\u0026thinsp;60 years and 0 points for \u0026lt;\u0026thinsp;60 years. The sum of the ERS-5 score and age score was considered the total point to determine the 1-, 3-, and 5-year survival of AML patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe employed and analyzed 10 AML specimens using the ER5-5 model and compared the calculated survival rates with patients\u0026rsquo; actual overall survival (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Afterward, we constructed a fitting curve, which showed a close correlation between the total score and the patients\u0026rsquo; actual survival (r\u0026thinsp;=\u0026thinsp;0.71), suggesting the high accuracy of the nomogram model in predicting patients\u0026rsquo; survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNomogram clinical application based on \u0026ldquo;ERS-5\u0026rdquo; model and patients\u0026rsquo; age\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=\"char\" char=\".\" 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\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eScores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCalculated survival rates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eActual survival time (years)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERS-5 model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5 y\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\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\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCalculated survival rates 1.00 indicates it exceed the predicted values of nomogram (survival rates\u0026thinsp;\u0026gt;\u0026thinsp;0.9). *Total score indicates ERS-5 model score plus age score according to the nomogram calculation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e6. The ERS-5 model surpassed the ELN risk stratification system in predicting the therapeutic response of AML patients\u003c/p\u003e \u003cp\u003eAs the ELN risk stratification system has been internationally adopted for predicting AML prognosis, we explored the efficiency of the ELN risk stratification system in the training cohort. Kaplan-Meier analysis showed that the intermediate group experienced a significantly shorter OS compared with the favorable group (HR\u0026thinsp;=\u0026thinsp;0.25, 95%CI 0.11\u0026ndash;0.59; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016). The OS of the intermediate group was similar to that of the adverse group (HR\u0026thinsp;=\u0026thinsp;1.52, 0.89\u0026ndash;2.23; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1281, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), suggesting that the ELN risk stratification failed to distinguish the OS between intermediate and adverse groups. However, the ERS-5 model successfully divided patients into 3 risk groups (favorable vs. intermediate, HR\u0026thinsp;=\u0026thinsp;0.30, 95%CI: 0.14\u0026ndash;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016; adverse vs. intermediate, HR\u0026thinsp;=\u0026thinsp;2.02, 95%CI: 1.19\u0026ndash;3.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0098, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Since the intermediate group was heterogeneous, we utilized the ERS-5 model to further divide the intermediate group into low- and high-risk subgroups, with significantly different survival (HR\u0026thinsp;=\u0026thinsp;2.81, 95%CI 1.47\u0026ndash;5.36; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0018, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). These findings suggest that the ERS model was superior to the ELN stratification system by further classifying the intermediate group. Then, we attempted to refine the ELN stratification with the ERS-5 model (\u0026ldquo;ERS-5\u0026thinsp;+\u0026thinsp;ELN\u0026rdquo;). By re-stratifying patients, we investigated whether the ERS-5 model can improve the ELN stratification. Kaplan-Meier analysis indicated that the ERS-5\u0026thinsp;+\u0026thinsp;ELN model showed a more discernible survival difference (favorable vs. intermediate, HR\u0026thinsp;=\u0026thinsp;0.20, 95%CI: 0.06\u0026ndash;0.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0091; adverse vs. intermediate, HR\u0026thinsp;=\u0026thinsp;2.69, 95%CI: 1.55\u0026ndash;4.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e patients in the favorable group were recognized as having intermediate risk, 36 patients with intermediate-risk (Int-β) were regrouped to the adverse group, and 7 patients from the adverse group (Adv-α) were re-assigned to the intermediate group. Thus, these results demonstrate that ELN stratification system was limited in predicting prognosis, therefore, it was combined with ERS-5 model to improve its predictive performance. According to our results, the ERS-5 model combined with ELN stratification system was superior in predicting the therapeutic response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince 5 genes of the ERS-5 model were related to therapeutic response, we performed drug response analyses to determine whether the ERS-5 model surpassed the ELN stratification system in predicting the therapeutic response of patients. We compared the IC\u003csub\u003e50\u003c/sub\u003e of doxorubicin, cytarabine, \u003cem\u003eFLT3\u003c/em\u003e inhibitors, sorafenib, midostaurin, and \u003cem\u003eBCL2\u003c/em\u003e inhibitor navitoclax between the risk groups of ELN stratification system and \u0026ldquo;ERS-5\u0026thinsp;+\u0026thinsp;ELN\u0026rdquo; model. The response to navitoclax significantly differed between the ELN-stratified groups. The IC\u003csub\u003e50\u003c/sub\u003e of the favorable group to navitoclax was higher than that of the adverse group, suggesting the ELN stratification failed to predict the therapeutic response to all 5 drugs and thus failed to predict the OS. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, the Int-β group was significantly more resistant than the Adv-α group, suggesting the ERS-5 model combined with the ELN stratification system could more accurately predict the response to chemotherapy. These results demonstrated that the ERS-5 model can improve the predictive ability of the ELN stratification system.\u003c/p\u003e \u003cp\u003eAs we compared the accuracy of the ERS-5 model, ELN stratification model, and \u0026ldquo;ERS-5\u0026thinsp;+\u0026thinsp;ELN\u0026rdquo; model, time-dependent ROC analysis showed the 2-, 3-, and 5-year survival AUC of the \u0026ldquo;ERS-5\u0026thinsp;+\u0026thinsp;ELN\u0026rdquo; model was significantly higher than that of the ELN stratification model. There was no significant difference between the ERS-5 model and the \u0026ldquo;ERS-5\u0026thinsp;+\u0026thinsp;ELN\u0026rdquo; model in this regard (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Given the relatively long turnaround time of ELN stratification, the ERS-5 model also surpassed the \u0026ldquo;ERS-5\u0026thinsp;+\u0026thinsp;ELN\u0026rdquo; model. The results suggested that the ERS-5 model was superior to the ELN stratification system and improved the predictive ability of the ELN stratification system.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePoor outcomes of AML patients are largely due to drug resistance, which occurs through various mechanisms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Among these mechanisms, ER stress seems to play a pivotal role in determining cell fate and drug resistance[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, we found that ER stress can significantly differentiate between sensitive and R/R patients and also between patients with favorable prognosis and patients with adverse prognosis. These findings suggested that ER stress-related genes are promising prognostic indicators for response to chemotherapy and outcomes of AML patients. Thus, we developed a prognostic model, ERS-5, based on the ER stress-related genes and verified the model in both training and validation cohorts. According to our results, the ERS-5 model showed high accuracy in evaluating the OS of AML patients in both training and validation cohorts. Although there are already some prognostic models [\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], we proved that the ERS-5 model can surpass these models with the highest AUC and C-index and a streamlined set of genes. More importantly, our model is simpler and more efficient, reducing the measurement time from about 3 weeks to about 3 hours by detecting nucleic acid expression levels of \u003cem\u003eRTN4R, PDIA6, CYP2E1, CALCRL, and AREG\u003c/em\u003e genes.\u003c/p\u003e \u003cp\u003eThe current ELN stratification system has some limitations. It was known that 40\u0026ndash;45% of AML patients are CN-AML who will be stratified as intermediate risk by ELN stratification, but show marked heterogeneity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Interestingly, the ERS-5 model can significantly stratify the OS of CN-AML patients and the intermediate group, suggesting the superiority of the ERS-5 model compared with the ELN stratification model, especially for the intermediate-risk group. Moreover, though AML has been divided into different cytogenetic subgroups, still significant heterogeneity exists. The same subgroup has different outcomes after standard treatment. Interestingly, the ERS-5 model successfully distinguished OS based on age, and varied genetic fusions and some common mutations (\u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e and \u003cem\u003eIDH\u003c/em\u003e),[\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e](Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting that different groups of patients can benefit from the model.\u003c/p\u003e \u003cp\u003eConsequently, based on the preferable results of the ERS-5 model, we constructed a nomogram that integrated demographic characteristics (age) with the ERS-5 model to provide a more accurate final score and guide treatment. The incidence of AML was much higher in the elderly population, nevertheless, the poor prognosis was more correlated with age [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].Older patients receive lower-intensity treatment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], but some of them would still benefit from intensive treatment [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Thus, it is critical to stratify the prognosis of older patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the ERS-5 model could distinguish between the low- and high-risk groups among older patients, suggesting that low-risk older patients might be candidates for more intensive treatments. The nomogram provides a visual representation of the probabilities of 1-, 3-, and 5-year survival, aiding clinicians in making therapeutic decisions based on individualized risk scores (calculated by the ERS-5 model) and patient\u0026rsquo;s age. This integrated approach aims to optimize treatment strategies, particularly for older patients, contributing to more personalized and effective patient care.\u003c/p\u003e \u003cp\u003eFurthermore, the ERS-5 model could evaluate the therapeutic response of AML patients. The ERS-5 model surpassed the ELN stratification system in predicting the therapeutic response of patients to several drugs, such as chemotherapy drug cytarabine, \u003cem\u003eFLT3\u003c/em\u003e inhibitors sorafenib, and \u003cem\u003eBCL2\u003c/em\u003e inhibitor navitoclax (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Cytarabine is a chemotherapy agent that is used for intensive and consolidation therapy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, due to unpredictable response rate, the dose and duration of cytarabine in consolidation therapy remain controversial [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The ERS-5 model can identify patients who are fit for consolidation therapy with high-dose cytarabine (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eBCL2 is also a well-known downstream target of ER stress response [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Venetoclax, a BCL2 inhibitor, has drawn a lot of attention in the treatment of AML especially among R/R AML patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The combination of venetoclax and HMAs (hypomethylating agents, HMAs) has been recommended by NCCN guidelines for AML patients which is unfit for high-dose chemotherapies[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, approximately 65% complete response rate and 35% poor response of venetoclax have been reported. The ERS-5 model also predicted the chemotherapeutic response in patients who were relatively sensitive or resistant to BCL2 inhibitor navitoclax, thus assisting the individualized application of \u003cem\u003eBCL2\u003c/em\u003e inhibitors for patients with AML. Given its efficacy and extensive application, the ERS-5 model may be a potential indicator for predicting the response to venetoclax.\u003c/p\u003e \u003cp\u003eAbove all, this study provided a novel prognostic model and demonstrated its accuracy and potential application in AML. Although the ERS-5 model performed well, the present study had some limitations. The nomogram constructed by ERS-5 model, combined with age, was only successfully used for 10 AML patients. Therefore, it is necessary to verify the efficacy of the ERS-5 model with more clinical data. Besides, the ERS-5 model can predict chemo-response by measuring the copy numbers of the five genes, the function and mechanism of these 5 genes should be elucidated in future studies. Our findings underscore the potential of the ERS-5 model as a robust tool in predicting the prognosis of AML patients, although future studies and validation efforts are crucial.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, as an efficient and practical tool, the ERS-5 model surpasses the ELN stratification system and can be independently utilized to evaluate the prognosis and guide the treatment of AML patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute myeloid leukemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Leukemia Net\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eendoplasmic reticulum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esecond-generation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhole-exome sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNAsea\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebone marrow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eR/R\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erefractory and relapsed\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFrench-American-British\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCN-AML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytogenetically normal AML\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRobust Multichip Average\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIC50\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehalf-maximal inhibitory concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomics of Drug Sensitivity in Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT- ROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eU- Time-dependent receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC- index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eD- concordance index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eERS-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5-gene prognostic model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe protocol of this study was approved by the Research Ethics Committee of the second hospital of Hebei Medical University (Approval letter No. 2019-R259).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journal.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author Simei Ren. on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Beijing Natural Science Foundation (No. J230022, L222125), National Natural Science Foundation of China (No.81670161).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eRen SM designed and supervised this study, analyzed data, wrote and edited the manuscript. Peng HW edited and revised the manuscript. Long LY analyzed data and generated the figures and tables. Guo J collected and analyzed data, made the statistical comparation. Dai Q revised the figures as well as made the data proofreading. Yang L and Sun L collected specimens and data, as well as patients follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAckonwledgements\u003c/p\u003e\n\u003cp\u003eThanks to the finacial grants from\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe Beijing Natural Science Foundation (No. J230022, L222125), National Natural Science Foundation of China (No.81670161).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKantarjian HM, Kadia TM, DiNardo CD, Welch MA, Ravandi F (2021) Acute myeloid leukemia: Treatment and research outlook for 2021 and the MD Anderson approach. 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Blood Adv 6:1193\u0026ndash;1206\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalter RB, Estey EH (2020) Selection of initial therapy for newly-diagnosed adult acute myeloid leukemia: Limitations of predictive models. Blood Rev 44:100679\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCellworks Group (2020) : Cellworks CBM Biosimulation Identifies Genomic Causes for Induction Failure in AML Patients and Suggests Alternative Therapies. Paper presented at the all-virtual 62nd American Society of Hematology (ASH) Annual Meeting, SOUTH SAN FRANCISCO, Calif., 2020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong L, Assaraf YG, Lei Z-N, Peng H, Yang L, Chen Z-S et al (2020) Genetic biomarkers of drug resistance: A compass of prognosis and targeted therapy in acute myeloid leukemia. Drug Resist Updates 52:100703\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Cubillos-Ruiz JR (2021) Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat Rev Cancer 21:71\u0026ndash;88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhateb A, Ronai ZA (2020) Unfolded Protein Response in Leukemia: From Basic Understanding to Therapeutic Opportunities. Trends Cancer 6:960\u0026ndash;973\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHetz C, Zhang K, Kaufman RJ (2020) Mechanisms, regulation and functions of the unfolded protein response. Nat Rev Mol Cell Biol 21:421\u0026ndash;438\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHetz C, Papa FR (2018) The Unfolded Protein Response and Cell Fate Control. 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Blood Adv 3:4155\u0026ndash;4160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y-N, Ma Y-N, Jia X-Q, Yao Q, Chen J-P, Li H (2022) Inducement of ER Stress by PAD Inhibitor BB-Cl-Amidine to Effectively Kill AML Cells. Curr Med Sci 42:958\u0026ndash;965\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimizu T, Kamel WA, Yamaguchi-Iwai S, Fukuchi Y, Muto A, Saya H (2017) Calcitriol exerts an anti‐tumor effect in osteosarcoma by inducing the endoplasmic reticulum stress response. Cancer Sci 108:1793\u0026ndash;1802\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimura N, Radwan MO, Amano M, Endo S, Fujii E, Hayashi H et al (2019) Novel p97/ VCP inhibitor induces endoplasmic reticulum stress and apoptosis in both bortezomib-sensitive and ‐resistant multiple myeloma cells. Cancer Sci 110:3275\u0026ndash;3287\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchardt JA, Weber D, Eyholzer M, Mueller BU, Pabst T (2009) Activation of the Unfolded Protein Response Is Associated with Favorable Prognosis in Acute Myeloid Leukemia. Clin Cancer Res 15:3834\u0026ndash;3841\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeeleher P, Cox N, Huang RS, pRRophetic (2014) An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels. Barbour JD, editor. PLoS ONE. ;9:e107468\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng T, Cui L, Huang W, Liu Y, Si C, Qian T et al (2021) The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia. BMC Med 19:176\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Shang J, Li N, Zhang L, Tang T, Tian G et al (2020) Development and validation of a 10-gene prognostic signature for acute myeloid leukaemia. J Cell Mol Med 24:4510\u0026ndash;4523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Herold T, He C, Valk PJM, Chen P, Jurinovic V et al (2013) Identification of a 24-Gene Prognostic Signature That Improves the European LeukemiaNet Risk Classification of Acute Myeloid Leukemia: An International Collaborative Study. JCO 31:1172\u0026ndash;1181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg SWK, Mitchell A, Kennedy JA, Chen WC, McLeod J, Ibrahimova N et al (2016) A 17-gene stemness score for rapid determination of risk in acute leukaemia. Nature 540:433\u0026ndash;437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan M, Zhou J, Jiao C, Ge J (2023) Bioinformatics analysis of the endoplasmic reticulum stress-related prognostic model and immune cell infiltration in acute myeloid leukemia. Hematology 28:2221101\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoukier M, Kadia T, Konopleva M, Alotaibi AS, Alfayez M, Loghavi S et al (2021) Clinical characteristics and outcomes in patients with acute myeloid leukemia with concurrent \u003cem\u003eFLT3\u003c/em\u003e -ITD and \u003cem\u003eIDH\u003c/em\u003e mutations. Cancer 127:381\u0026ndash;390\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartelli MP, Sportoletti P, Tiacci E, Martelli MF, Falini B (2013) Mutational landscape of AML with normal cytogenetics: Biological and clinical implications. Blood Rev 27:13\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Yang C, Zhang L, Schaar DG (2017) Molecular Mutations and Their Cooccurrences in Cytogenetically Normal Acute Myeloid Leukemia. Stem Cells Int 2017:6962379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiyoi H, Kawashima N, Ishikawa Y (2020) \u003cem\u003eFLT3\u003c/em\u003e mutations in acute myeloid leukemia: Therapeutic paradigm beyond inhibitor development. Cancer Sci 111:312\u0026ndash;322\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026ouml;hner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H et al (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 140:1345\u0026ndash;1377\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei H, Wang Y, Gale RP, Lin D, Zhou C, Liu B et al (2020) Randomized Trial of Intermediate-dose Cytarabine in Induction and Consolidation Therapy in Adults with Acute Myeloid Leukemia. Clin Cancer Res 26:3154\u0026ndash;3161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeigert N, Rowe JM, Lazarus HM, Salman MY (2022) Consolidation in AML: Abundant opinion and much unknown. Blood Rev 51:100873\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadha G, Raghavan SC (2017) BCL2: A promising cancer therapeutic target. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1868:309\u0026ndash;314\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Y, Xiong X, Li X, Lu W, He X, Jin X et al (2021) Low-dose decitabine plus venetoclax is safe and effective as post‐transplant maintenance therapy for high‐risk acute myeloid leukemia and myelodysplastic syndrome. Cancer Sci 112:3636\u0026ndash;3644\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMistry JJ, Hellmich C, Lambert A, Moore JA, Jibril A, Collins A et al (2021) Venetoclax and Daratumumab combination treatment demonstrates pre-clinical efficacy in mouse models of Acute Myeloid Leukemia. Biomark Res 9:35\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShort NJ, Rytting ME, Cortes JE (2018) Acute myeloid leukaemia. Lancet 392:593\u0026ndash;606\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano M, Carlisle JW (2018) How I treat older patients with acute myeloid leukemia: Treating Older Patients with AML. Cancer 124:2472\u0026ndash;2483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedeiros BC, Pandya BJ, Hadfield A, Pike J, Wilson S, Mueller C et al (2019) Treatment patterns in patients with acute myeloid leukemia in the United States: a cross-sectional, real-world survey. Curr Med Res Opin 35:927\u0026ndash;935\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell NH, Hills RK, Thomas A, Thomas I, Kjeldsen L, Dennis M et al (2021) Outcomes of older patients aged 60 to 70 years undergoing reduced intensity transplant for acute myeloblastic leukemia: results of the NCRI acute myeloid leukemia 16 trial. haematol 107:1518\u0026ndash;1527\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVey N (2020) Low-intensity regimens \u003cem\u003eversus\u003c/em\u003e standard-intensity induction strategies in acute myeloid leukemia. Therapeutic Adv Hematol 11:204062072091301\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePastore F, Pastore A, Rothenberg-Thurley M, Metzeler KH, Ksienzyk B, Schneider S et al (2022) Molecular profiling of patients with cytogenetically normal acute myeloid leukemia and hyperleukocytosis. Cancer 128:4213\u0026ndash;4222\u003c/span\u003e\u003c/li\u003e\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-4088362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4088362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute myeloid leukemia possess high heterogeneity and current European Leukemia Net (ELN) risk stratification system cannot be applicable to all AML patients and needs about 3 weeks testing cycle. The aim of this study was to develop a applicable prognostic tool that may overcome the above shortcomings.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used AML patients collected in clinic and TCGA database to explore the role of ER stress in response to chemotherapy. Patients from the TCGA database were used as the training cohort, and two GEO datasets were used as external validation cohorts. Univariate /multivariate COX and LASSO regression was exemplified to establish the prognostic model. Kaplan-Meier and time-dependent ROC were used to assess and compare the efficiency of the model with ELN stratification and other models. R package \"pRRophetic\" was utilized to assess drug sensitivity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the training cohort, we selected 5 ER stress-related genes to predict chemosensitivity and establish the ERS-5 prognostic model. The model successfully predicted the overall survival of patients; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, HR\u0026thinsp;=\u0026thinsp;4.86 (2.79\u0026ndash;8.44); AUC\u0026thinsp;=\u0026thinsp;0.83. The model was verified in validation cohorts and could further stratify the risk of various AML subgroups. It also complemented the ability of ELN to predict the response of patients with AML to main chemotherapeutic drugs. Finally, a \u0026ldquo;ERS-5\u0026rdquo; risk score was construced by the nomogram based on the ERS-5 model and age.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe ERS-5 model allowed more rapid (about 3 hours) and accurate risk stratification and complemented the ability of ELN to assess chemosensitivity.\u003c/p\u003e","manuscriptTitle":"The ER stress related gene panel guide the prognosis and chemosensitivity in acute myeloid leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 16:03:33","doi":"10.21203/rs.3.rs-4088362/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":"7ba74580-22dc-4b05-a924-ec882e144407","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-29T14:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 16:03:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4088362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4088362","identity":"rs-4088362","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}