High SEC61A1 expression predicts poor outcome of acute myeloid leukemia

Abstract The malfunction of SEC61A1 has been linked to several types of cancers, but its role in acute myeloid leukemia (AML) remains poorly understood. In this study, we used a series of bioinformatics analysis techniques, including gene expression profiling and proteomic analysis. Our findings were subsequently validated through a series of in vitro experiments, such as SEC61A1 knockdown in cell lines and RT-qPCR. We discovered a significant up-regulation of SEC61A1 in AML patients compared to healthy controls. AML patients with elevated SEC61A1 expression exhibited reduced overall survival compared to those with lower expression. Moreover, SEC61A1 expression emerged as an independent risk factor for predicting the survival of AML patients undergoing allo-HSCT. Our analysis also revealed an association between high SEC61A1 expression and increased signaling pathways related to cell growth. Our study underscores the importance of SEC61A1 expression as a novel prognostic indicator for predicting survival among AML patients, while also identifying it as a promising therapeutic target.


Introduction
Acute myeloid leukemia (AML) stands as a common hematologic malignancy marked by a diverse clonal presence of immature myeloid progenitor cells in both the bone marrow and peripheral blood [1,2], accounting for 80% of newly diagnosed acute leukemia adult patients [1].Despite noteworthy progress in AML treatment, only 40% of patients below 60 years achieve long-term survival under current therapy [2,3].Among older patients, survival is significantly worse [3].Leukemia is classified by two major systems: FAB and WHO.FAB categorizes based on cell types and morphology [4], while WHO considers morphology, immunophenotype, genetics, and clinical features [5].While FAB was an important first step, WHO offers a more contemporary approach, integrating molecular and immunophenotypic data to enhance therapeutic guidance [5].
Recent advancements in clinical and molecular prognostic markers have markedly enhanced our comprehension of AML biology [5,6].In addition to traditional markers, the past few years have unveiled novel therapeutic targets and provided insights into identifying individuals who stand to gain the most from specific treatments (such as immune checkpoints [7], macrophage markers [8], chemokine receptors [9], etc.).These breakthroughs are poised to revolutionize the approach to treating individual patients with AML.Therefore, the identification of new molecular markers that can predict survival and serve as treatment targets is of paramount importance.
SEC61A1 protein belongs to SECY/SEC61-alpha family and is located in the endoplasmic reticulum (ER) membrane [10].It plays a crucial role in the insertion of membrane polypeptides into the ER and is responsible for the retro-translocation of misfolded proteins to the cytosol for degradation [11].The aberrant function of the SEC61A1 protein has been implicated in various human cancers, including head, lung, prostate, and glioblastoma [12][13][14][15], and has been targeted as a therapeutic option in multiple myeloma [10].A recent study has also reported that SEC61A1 is crucial in mycolactone-dependent apoptosis in AML cells [16].Nevertheless, the understanding of SEC61A1 expression in AML remains limited.
In this study, we observed that SEC61A1 was highly expressed in AML patients compared to healthy controls.High SEC61A1 expression was associated with poor prognosis in AML and served as a valuable molecular marker for predicting survival.

Data collection and processing
In this study, we included 151 AML patients derived from The Cancer Genome Atlas (TCGA) dataset [17], with ages ranging from 18 to 88 years.These patients were diagnosed and treated at Washington University between 2001 and 2010 [17].Following the NCCN guideline, all AML patients underwent the standard induction and consolidation regimen.As stated by the teams, written consents for the study were all available, following the Declaration of Helsinki [17].Samples of peripheral blood were derived at the time point of diagnosis.Clinical information on survival, baseline characteristics, gene mutation profile, and expression profile could all be downloaded from the TCGA.Additionally, AML-M3 patients were excluded from the study.
Other AML datasets were collected from Gene Expression Omnibus (GEO) (GEO accession numbers: GSE7186 [18], GSE13159 [19], GSE22778 [20], GSE12417 [21]).Written consent for the treatment and the study was also available, as stated by the authors.Microarray data, as well as clinical characteristics, could all be downloaded from the GEO dataset.Proteomic expression data were derived from the Proteomic Data Commons (PDC, National Cancer Institute) (PDC Study Identifier: PDC000477, Project ID: Proteogenomic Translational Research Centers [PTRC]) [22].

Reverse transcription quantitative PCR (RT-qPCR)
To extract the total RNA from AML cell lines, we used the Trizol agent (Vazyme Biotech Co., Ltd) following the manufacturer's protocol.Then HiScript 1st Strand cDNA Synthesis Kit (Vazyme Biotech Co., Ltd) was used to reverse transcribe the extracted mRNA into cDNA.The real-time PCR was performed on a CFX96 TOUCH analysis system (Bio-rad, USA), using ChamQ Universal SYBR qPCR 1Master Mix (Vazyme Biotech Co., Ltd).In this study, we used the following amplification conditions of qPCR: 95°C of pre-denaturation for 30 s, 40 cycles of 95°C of denaturation for 5 s, and 60°C of extension for 30 s.The primer sequences were as follows: SEC61A1 Forward: 5′-GAAGGAGCAGCAGATGGTGAT GAG-3′, Reverse: 5′-GGAAGTCAGCCAGGACCGAGAGAG-3′.
The relative expression levels of the target genes were calculated by the 2 −ΔΔCt method.

Plasmid construction, lentiviral transduction, and target gene knockdown
Following the manufacturer's instruction, we constructed lentiviral shRNA plasmids for SEC61A1 by subcloning the shRNA oligos into the lentiviral shRNA vector with an IRES GFP (pLV3ltr-ZsGreen-Puro-U6) (Corues Biotechnology, China).The shRNA oligos design for SEC61A1 is listed in Table S1.
Following the manufacturer's instructions, 293T cells were transfected with the indicated viral plasmid and psPAX2 and pMD2.G (packaging plasmids) (Corues Biotechnology, China), via Exfect Transfection Reagent (Vazyme Biotech Co., Ltd, China, No. T101-01).After 48 and 72 h post-transfection, viral supernatant was collected and filtered with a 45 μM filter.AML cells were therefore transduced with the resulting lentivirus.HitransG P (KeyGen BioTECH, China) was added to increase the transduction efficiency.After 48 h transduction, 1 µg/mL of puromycin (InvivoGen, CAS.58-58-2) was added into the infected cells for stable expressing cell selection.Via flow cytometry, the efficiency of transduction was monitored by GFP (+) cells, more than 95% of GFP (+) cells were used for knockdown efficiency and further in vitro experiments.

Bioinformatic analysis of RNA sequencing data
In this study, all bioinformatic analysis was conducted on the R platform and Perl script, R package SVA was used to remove the batch effects and recover the biological signal in the data when integrating different GPL platforms from a common GEO series (GSE22778 contains nine platforms, including GPL8650, GPL8651, GPL8652, GPL8653, GPL8654, GPL10105, GPL10106, GPL10107, GPL10108, n = 185; GSE12417 contains three platforms, including GPL96, GPL97, GPL570, n = 405).R package edgeR was used to conduct the differential expression analysis of the two cohorts (SEC61A1 high and SEC61A1 low cohort).All R scripts used for data analysis in this study are publicly available from GitHub (https:// github.com).We used Benjamini and Hochberg's method to control the false discovery rate.Genes with a fold change over 1.5 (adjusted P-value <0.01) found by edgeR were considered as differentially expressed.Analysis of specific signaling pathways was conducted on Gene Set Enrichment Analysis (GSEA) platform.This study utilized publicly accessible data from TCGA and GEO databases.These databases contain genomic and expression data from patients worldwide and are anonymized, devoid of directly identifiable personal information.

Statistical analysis
Data were visualized and analyzed using STATA 16.0 (StataCorp 2019) and Prism 8.0 (GraphPad Prism software).In this study, data were presented as the mean ± SD (normalized distribution) or median (IQR) (skewed distribution).t-test was used to compare two groups, using Prism 8.0 software.Kaplan-Meier method was used to conduct the survival analysis.
Ethical statement: The collection and sharing of data in TCGA and GEO databases adhere to stringent ethical guidelines and regulations, encompassing patient informed consent, privacy protection, and data security.The original data collection received approval from the respective ethics committees, and during the sharing process, any potentially identifying information was removed.The use of data in this study aligns with the data usage policies of TCGA and GEO, posing no potential risks to the privacy and rights of participants.It is explicitly stated that this study does not involve direct experimentation on human or animal subjects; rather, it analyzes publicly available, lawfully obtained genomic and expression data.

Overexpression of SEC61A1 accompanied by the poor prognosis of AML
Two datasets derived from GEO datasets (GSE7186 and GSE13159) were used to compare the expression level of SEC61A1 between AML and healthy cohorts.The results denoted that the expression of SEC61A1 was significantly increased in AML patients, compared to controls (GSE7186, P = 0.0435; GSE13159, P < 0.0001) (Figure 1a and b).We further hypothesized that high SEC61A1 expression is associated with poor prognosis in AML patients.Hence, we used three datasets to confirm this finding.The results denoted that high SEC61A1 expression predicts unfavorable prognosis in overall survival in AML (SEC61A1 high vs SEC61A1 low AML cohort, GSE22778, P = 0.0198; GSE12417, P = 0.0177; TCGA [excluding AML-M3], P = 0.0025; TCGA CN-AML, P = 0.0038) (Figure 1c-f).
In addition, the expression of SEC61A1 serves as an indicator for poor OS in non-FLT3 mutant AML patients (TCGA-AML, P = 0.028) (Figure 2a), with SEC61A1 demonstrating notably dismal survival outcomes in the intermediate to high-risk groups (Figure 2b-d).Intriguingly, upon downregulating the expression of SEC61A1 in AML cell lines (Figures S1a, b and S2), we observed a simultaneous reduction in FLT3 expression (Figure S1a and b).Furthermore, we identified a positive correlation between the expression of SEC61A1 and FLT3 in AML (P = 0.0068, R = 0.21) (Figure S3).Further categorizing AML patients based on the expression levels of SEC61A1 and FLT3, we classified them into three groups (SEC61A1 low with FLT3 low , SEC61A1 low and FLT3 high or SEC61A1 high with FLT3 low , and SEC61A1 high with FLT3 high ).Notably, the SEC61A1 low with FLT3 low group exhibited the most favorable survival trend among the three, while the SEC61A1 high with FLT3 high group demonstrated the poorest survival outcome (Figure S3).

Baseline characters of SEC61A1 high and SEC61A1 low AML cohort of TCGA dataset
We conducted a comparison of baseline clinical characteristics and gene mutations between SEC61A1 high and SEC61A1 low AML cohorts using the TCGA dataset.However, no significant differences were observed in age, gender, race, WBC count, platelet count, BM blast percentage, and gene mutations (Table 1).Nevertheless, we discovered that the SEC61A1 high cohort exhibited a higher proportion of FLT3 mutation (P = 0.005) (Table 1).

Cox proportional hazard model analysis of SEC61A1 expression in CN-AML patients
To gain a better understanding of the correlations between SEC61A1 expression and overall survival, we conducted univariate Cox hazard analysis for TCGA AML patients (excluding M3-AML) based on whether they received allo-HSCT.The included variables were SEC61A1 expression, age, WBC count, BM-blast, hemoglobin, platelet count, cytogenetic risk, and gene mutations (detailed list in Tables S2  and S3, mutation vs. wild type).In patients who did not receive allo-HSCT, the results unveiled that age (HR = 1.029,P = 0.004), intermediate cytogenetic risk (HR = 4.247, P = 0.016), poor cytogenetic risk (HR = 20.555,P < 0.001), TP53 mutation (HR = 3.907, P < 0.001), and DNMT3A mutation (HR = 1.939,P = 0.033) were identified as risk factors.However, other variables showed no statistical significance (Table S2).Meanwhile, for patients who received allo- HSCT, the results indicated that SEC61A1 expression was a significant risk factor (HR = 16.635,P = 0.006) (Table S3).
We further conducted a multivariate Cox hazard analysis for overall survival.Parameters with P values less than 0.2 during univariate Cox hazard analysis were included in the hazard model.The results revealed that poor cytogenetic risk (HR = 18.885,P < 0.001) and FLT3 mutation (HR = 3.0, P = 0.01) were independent risk factors for patients who did not undergo allo-HSCT (Figure 3a) (P < 0.05).Among patients who received allo-HSCT, SEC61A1 expression (HR = 14.787,P = 0.01), JAK3 mutation (HR = 2.293, P = 0.043), CEBPA mutation (HR = 6.611,P = 0.022), and U2AF1 mutation (HR = 2.824, P = 0.013) were identified as independent risk factors.Furthermore, high SEC61A1 expression was found to be the highest risk factor among patients who received allo-HSCT (Figure 3b).These findings indicate that high SEC61A1 expression is an independent risk factor for AML patients udnergoing allo-HSCT.

Genome-wide expression profile associated with SEC61A1 expression
To illustrate the underlying molecular mechanism associated with high SEC61A1 expression, we further explored the gene expression profile from the TCGA database.By utilizing R software, we identified 162 differentially expressed genes (DEGs) (SEC61A1 high vs SEC61A1 low AML cohort, |log 2 FC| > 2, P-value <0.05), including 83 upregulated and 79 downregulated genes (Figure 4a).Notably, EPHA3, MMP7, BIRC7, and ROS1 were found to be upregulated (Figure 4a).Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant enrichment in the cGMP-PKG signaling pathway and JAK-STAT signaling pathway (Figure 4b).We also performed GSEA analysis to investigate the signaling pathways associated with high SEC61A1 expression.Interestingly, several cell cycle-associated signaling pathways, such as Reactome cell cycle mitotic, Reactome Mphase, and Reactome cell cycle checkpoints, were found to be enriched in the high SEC61A1 expression cohort (Figure 5a-i).
To validate our aforementioned findings, we conducted further investigation into proteomic expression patterns associated with SEC61A1 expression in AML patients using data sourced from the PDC database.A cohort comprising 189 AML patients was included in our analysis.As illustrated in Figure 6a and b, EPHA3, MMP7, BIRC7, and ROS1 exhibited significant upregulation in the SEC61A1 high (n = 95) compared to the SEC61A1 low (n = 94) AML cohort, with |log 2 FC| > 2 and a P-value <0.05.Additionally, our GO and KEGG analysis revealed pronounced enrichment in the JAK-STAT signaling pathway.Notably, we observed significant enrichment in "leukocyte mediated immunity" and "myeloid leukocyte activation" associated with SEC61A1 expression.

Discussion
SEC61A1 plays a crucial role in the biogenesis of most secreted and transmembrane proteins [11,23].This process is fundamental to the proper folding, modification, and transport of proteins destined for secretion or integration into cellular membranes [11,23].Previous studies have indicated that targeting SEC61A1 enhances sensitivity to anticancer drugs in various hematological malignancies [24][25][26].The primary mechanism underlying this effect involves the augmentation of ER stress [26].Moreover, SEC61A1 has been shown to sensitize AML cells to other drugs by inducing apoptosis [10].Therefore, investigating the significance of SEC61A1 expression in AML is of great importance.
Traditionally, disease classification relied heavily on pathological and cytological features.However, with a deeper understanding of cancer genetics and proteins, researchers have identified unique molecular markers that aid in the precise categorization of disease subtypes [6].Furthermore, the emergence of new drugs based on these molecular markers signifies a shift toward individualized therapy.The principles of personalized treatment involve tailoring therapeutic approaches based on a patient's specific molecular and genetic characteristics [6].This customization enhances treatment efficacy while minimizing adverse effects on patients.
In this study, we have identified SEC61A1 expression as a novel prognostic marker for AML.Our analysis unveiled a significantly upregulation of SEC61A1 in AML patients compared to healthy controls.Furthermore, higher SEC61A1 expression was found to be associated with shorter overall survival for non-M3 AML patients, especially among those with intermediate/poor risk.Importantly, we demonstrated that SEC61A1 expression was an independent risk factor for OS among AML patients receiving allo-HSCT, suggesting that targeting SEC61A1 may be a valuable strategy for preventing disease relapse after allo-HSCT.Another interesting finding is that we also observed a higher incidence of FLT3 mutations in the SEC61A1 high-expression cohort.FLT3 mutations are relatively common in AML patients, accounting for approximately 30%, and are associated with a poorer prognosis [27].Most FLT3 proteins undergo unfolding within the ER lumen [28], leading to increased ER stress [29].Given that SEC61A1 serves as a crucial regulator of ER stress [23], its potential role in maintaining ER homeostasis in AML cells with FLT3 mutations could be significant.Further research is warranted to delve into the intricacies of this relationship and explore additional details in future studies.Above findings have significant implications for the management and treatment of AML patients.Furthermore, we identified SEC61A1 as a novel prognostic marker for AML and investigated its potential molecular mechanisms.We found that high expression of SEC61A1 was associated with upregulation of EPHA3, MMP7, BIRC7, and ROS1, all of which have been reported as prognostic markers or therapeutic targets in hematological malignancies [30][31][32][33].Furthermore, we explored the correlation between high SEC61A1 expression and several oncogenic signaling pathways.The results revealed that the high SEC61A1 expression was linked to enrichment of the cGMP-PKG and JAK-STAT signaling pathways.It is noteworthy that an overactive cGMP-PKG signaling pathway was reported with anti-apoptotic activity [34], whereas JAK-STAT signaling pathway was reported to be implicated in cell growth and cycle arrest in AML [35].In addition, our GSEA analysis also supported the association between SEC61A1 expression and cell cycle activity in AML, including "cell cycle M phase, cell cycle G2/M phase, cell cycle checkpoint," which are indicative of the cell growth dynamics of leukemic cells.Consequently, our proteomic analysis revealed enrichment of the JAK-STAT signaling pathway and cell cycle pathways associated with SEC61A1 expression.These findings serve to affirm the correlation between elevated SEC61A1 expression and activation of oncogenic signaling pathways.

Conclusions
In summary, our findings propose SEC61A1 as a promising prognostic marker for assessing the survival outcomes of AML patients.The preliminary insights gleaned from our study regarding this novel marker hold potential for clinical application in the future.We anticipate that our results will catalyze further investigation in this domain, facilitating advancements in AML prognostication and treatment strategies.

Figure 2 :
Figure 2: High SEC61A1 expression predicts adverse clinical outcome among AML patients.(a) OS analysis of SEC61A1 expression in non-FLT3 mutant AML patients, high expression of SEC61A1 predicts poor prognosis.(b)-(d) OS analysis of SEC61A1 expression in favorable cytogenetic risk (b), intermediate risk (c), and poor risk (d).

Figure 3 :
Figure 3: Forest plot of multivariate COX hazard regression model (TCGA AML-non M3).(a) Forest plot of multivariate COX hazard regression analysis among AML patients without receiving allo-HSCT.(b) Forest plot of multivariate COX hazard regression analysis among AML patients receiving allo-HSCT.

Figure 5 :
Figure 5: GSEA analysis result of signaling pathways associated with SEC61A1 expression.(a)-(i) Top eight enriched signaling pathways by GSEA analysis are listed.

Table 1 :
Baseline clinical characteristics of non-M3-AML patients from the TCGA