Lactylation Modulation Identifies Key Biomarkers and Therapeutic Targets in KMT2A- Rearranged AML

doi:10.21203/rs.3.rs-5218493/v1

Download PDF

Article

Lactylation Modulation Identifies Key Biomarkers and Therapeutic Targets in KMT2A- Rearranged AML

https://doi.org/10.21203/rs.3.rs-5218493/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Acute Myeloid Leukemia (AML) with KMT2A rearrangements (KMT2Ar), found on chromosome 11q23, is often called KMT2A-rearranged AML (KMT2Ar-AML). This variant is highly aggressive, characterized by rapid disease progression and poor outcomes. Growing knowledge of epigenetic changes, especially lactylation, has opened new avenues for investigation and management of this subtype. Lactylation plays a significant role in cancer, inflammation, and tissue regeneration, but the underlying mechanisms are not yet fully understood. This research examined the influence of lactylation on gene expression within KMT2Ar-AML, initially identifying twelve notable lactylation-dependent differentially expressed genes (DEGs). Using advanced machine learning techniques, six key lactylation-associated genes (PFN1, S100A6, CBR1, LDHB, LGALS1, PRDX1) were identified as essential for prognostic evaluation and linked to relevant disease pathways. The study also suggested PI3K inhibitors and Pevonedistat as possible therapeutic options to modulate immune cell infiltration. Our findings confirm the critical role of lactylation in KMT2Ar-AML and identify six key genes that may serve as biomarkers for diagnosis and treatment. In addition to highlighting the need for further validation in clinical settings, these findings contribute to our understanding of KMT2Ar-AML's molecular mechanisms.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

Health sciences/Oncology

Acute Myeloid Leukemia

KMT2A rearrangements (KMT2Ar)

lactylation

epigenetics

machine learning

Acute Myeloid Leukemia (AML) is an aggressive hematologic malignancy characterized by the rapid proliferation of immature leukocytes in the bone marrow, disrupting normal hematopoiesis[1–3]. Among the various subtypes of AML, the KMT2A-rearranged subtype (KMT2Ar-AML) is notable for its poor prognosis and rapid disease progression[4–7]. Rearrangements of KMT2A on chromosome 11q23 have been identified in 5–10% of pediatric AML cases and are considered adverse prognostic factors[4, 8]. To better understand the molecular basis of KMT2Ar-AML, we used high-throughput microarray platforms. These platforms identified significant changes in gene expression and epigenetic modifications that may serve as potential diagnostic and prognostic biomarkers[9]. In recent years, the role of epigenetic modifications, particularly lactylation, has gained increasing attention as a crucial factor in cancer biology[10–12].

In recent studies, it has been demonstrated that histone lactylation regulates gene expression and advances cancer. Researchers have found that lactylation-related enzymes, such as lactate dehydrogenase, are associated with poor outcomes in various cancers, including non-small cell lung cancer, ocular melanoma, and clear cell renal cancer[13, 14]. This suggests that lactylation may represent a new therapeutic target for enhancing patient outcomes. Furthermore, research highlights how lactylation influences immune responses and shows promise in overcoming immunosuppression within the tumor microenvironment[15–17]. Notably, histone lactylation has been implicated in the regulation of key oncogenic pathways, such as the PI3K/AKT/HIF-1α signaling pathway, further emphasizing its relevance in cancer therapy[18, 19]. These findings position lactylation as a promising focal point for developing innovative cancer treatments.

This study investigates lactylation's role in KMT2A-rearranged AML by analyzing lactylation-dependent DEGs, identifying key epigenetic regulators as potential therapeutic targets, and showing their impact on AML progression, prognosis, and treatment response, thus enhancing understanding of KMT2Ar-AML mechanisms for personalized strategies and improved diagnostics.

2.1 Examination of Lactylation-Dependent Differentially Expressed Genes (Lac-DEGs) in Acute Myeloid Leukemia (AML) with KMT2A Rearrangement

The study design and key findings are summarized in Fig. 1.. After addressing batch effects in the GEO dataset (Figure S1), our study included 70 samples of KMT2Ar-AML, as well as 52 control specimens. Comparative analysis identified 646 DEGs, with 360 upregulated and 286 downregulated. This observation highlights substantial molecular variances specific to KMT2Ar-AML, as illustrated in Fig. 2A.

Our analysis subsequently focused on DEGs related to lactylation, leading to the identification of 12 crucial lactylation-dependent DEGs, with 7 showing upregulation and 5 showing downregulation. This characterization highlights the significant role of these genes in the lactylation pathways associated with AML, as depicted in Figs. 2B-C. To provide a more comprehensive understanding of the expression patterns of the Lac-DEGs, we utilized heatmaps and histograms to offer a detailed representation of their differential expression between KMT2Ar-AML and CN-AML cohorts, as illustrated in Figs. 2D-E.

Moreover, a functional clustering analysis of DEGs and Lac-DEGs was conducted, revealing insights into their role and association within the lactylation mechanisms pertinent to AML, as evidenced in Figure S2-3.

2.2 Identification and Validation of Diagnostic Markers

To uncover potential diagnostic markers, a multifaceted machine-learning strategy was implemented. Initially, LASSO regression analysis identified 9 significant genes from the statistically relevant univariate variables (Fig. 3A). Following this, Random Forest analysis (Fig. 3B) highlighted the top 10 genes based on their importance. Furthermore, SVM-RFE (Fig. 3C) was employed to refine the identification of significant genes. The intersection of these methodologies culminated in the identification of 6 core genes, designated as LactoKey Genes: PFN1, S100A6, CBR1, LDHB, LGALS1, and PRDX1 (Fig. 3D). We investigated the interrelationships among these specific genes, utilizing red to signify positive correlations and green to indicate negative correlations, as illustrated in Fig. 3E. In order to assess the diagnostic relevance of these genes, a receiver operating characteristic (ROC) curve analysis was performed (Fig. 3F). This analysis highlighted the predictive efficacy of the LactoKey Genes concerning disease manifestation, yielding the following area under the curve (AUC) values: PFN1: AUC = 0.77, S100A6: AUC = 0.855, CBR1: AUC = 0.791, LDHB: AUC = 0.806, LGALS1: AUC = 0.863, and PRDX1: AUC = 0.74. The AUC values suggest that the LactoKey genes hold considerable promise for the diagnosis of KMT2Ar-AML. Notably, LGALS1 and S100A6 demonstrate the most significant diagnostic precision, with AUC values recorded at 0.863 and 0.855, respectively. These findings suggest that the LactoKey genes are not only essential for elucidating the biological mechanisms underlying KMT2Ar-AML but also serve as valuable biomarkers for its diagnosis.

2.3 Immune Infiltration Analysis of LactoKey Genes

The examination of immune cell infiltration indicated a positive association among diverse immune cell populations. Nevertheless, specific subsets, particularly Type 2 T helper cells, exhibited negative correlations with other immune cell types, notably Activated B cells, Type 17 helper cells, CD56dim natural killer cells, Natural killer cells, and Immature dendritic cells (refer to Fig. 4A). In samples from patients with KMT2Ar-acute myeloid leukemia (AML), there was a marked increase in the proportions of several immune cell populations, which included CD56dim natural killer cells, Eosinophils, Gamma delta T cells, Myeloid-derived suppressor cells (MDSCs), Macrophages, Monocytes, Natural killer cells, and Neutrophils, when compared to control AML samples. Notably, MDSCs exhibited elevated levels in both KMT2Ar-AML and control AML, demonstrating distinct immune cell subset profiles (Fig. 4B). Correlation analysis between the LactoKey Genes (CBR1, PFN1, S100A6, LGALS1, LDHB, and PRDX1) and immune cell infiltration indicated that CBR1, PFN1, S100A6, and LGALS1 generally exhibited positive correlations with most immune cells, whereas LDHB and PRDX1 tended to show negative correlations, as illustrated in Fig. 4C-H.

2.4 Biological Functions of LactoKey Genes

Through the execution of correlation analysis and Gene Set Enrichment Analysis (GSEA) of Reactome pathways pertaining to LactoKey genes, facilitated by the clusterProfiler package in R, we uncovered distinct functional relationships for each gene. This underscores their contributions to the pathogenesis of KMT2Ar-AML. The detailed functional associations are as follows: CBR1 is primarily linked to mitochondrial functions, energy metabolism, and oxidative phosphorylation, underscoring its involvement in energy metabolism. LDHB shows strong correlations with glycolysis/gluconeogenesis, pyruvate metabolism, and protein synthesis, highlighting its role in carbohydrate metabolism. PRDX1 exhibits strong associations with ROS detoxification, antioxidant activity, and cell cycle regulation, suggesting its role in cellular protection and regulation. PFN1 is linked to actin cytoskeleton organization, cell migration, and signal transduction, implicating it in cytoskeleton dynamics and signaling. S100A6 demonstrates associations with calcium signaling, inflammatory responses, and the regulation of apoptosis, underscoring its significance in both calcium signaling and inflammatory processes. Conversely, LGALS1 is implicated in the modulation of immune responses, cell adhesion, and the regulation of apoptosis, thereby highlighting its crucial function in immune regulation and cellular attachment. These in-depth characterizations enhance our comprehension of the molecular mechanisms implicated in KMT2Ar-AML and underscore the pivotal contributions of each LactoKey gene to disease advancement and prospective therapeutic interventions. This thorough examination elucidates the manner in which these genes partake in the pathogenesis of KMT2Ar-AML, as depicted in Fig. 5.

2.5 Examination of Gene-miRNA and Gene-TF Interaction Networks

The utilization of network analysis facilitated the construction of interaction networks involving gene-miRNA and gene-transcription factor (TF) associations for the LactoKey Genes, as illustrated in Fig. 5. The gene-miRNA network encompassed a total of 103 interactions, highlighting specific miRNAs such as hsa-miR-361-3p, hsa-miR-548b-5p, hsa-miR-548c-5p, hsa-miR-548d-5p, and hsa-miR-96 in relation to these genes. Furthermore, several common regulators were identified, including CLEC5A, E2F4, MAX, MYC, TP53, USF1, and YY1. A subsequent correlation analysis encompassing all genes was performed, which identified the top 50 genes that exhibited positive correlations with each of the key genes. Notably, LDHB demonstrated the most robust positive correlation with NPM1, as depicted in Fig. 6.

2.6 Lactylation Subtypes in KMT2Ar-AML: Divergent Expression Profiles and Pathway Associations

Our analysis identified two distinct lactylation subtypes in KMT2Ar-AML, referred to as Cluster A and Cluster B. Cluster A is distinguished by increased expression of PFN1 and S100A6, suggesting a unique lactylation profile. In contrast, Cluster B is defined by elevated expression of LDHB, LGALS1, and PRDX1, indicating a contrasting gene expression pattern across clusters, highlighting the heterogeneity within KMT2Ar-AML(Fig. 7A-C).

The biological differences between these lactylation subtypes were investigated through pathway analyses. Cluster A showed significant associations with pathways such as KEGG endocytosis and Notch signaling, while Cluster B demonstrated positive correlations with pathways related to proteasome activity, Parkinson's disease, protein export, folate-mediated one carbon metabolism, and pyruvate metabolism. These findings offer valuable insights into the unique biological processes and disease associations of each subtype (Fig. 7D).

In the Reactome pathway analysis, Cluster A demonstrated significant relationships with the modulation of cell death-related genes via FOXO-mediated transcription, the innate immune response to cytosolic DNA, and interleukin signaling pathways. In contrast, the associations identified in Cluster B suggested the suppression of Notch4 signaling, the destabilization of mRNA mediated by AUF1 HNRNP D0, the stabilization of the p53 protein, the metabolism of polyamines, and the cross-presentation of soluble exogenous antigens within endosomal compartments. These observations offer valuable insights into the molecular mechanisms that distinguish the two lactylation subtypes present in KMT2Ar-AML. (Refer to Fig. 7E).

2.7 Drug Sensitivity Analysis of LactoKey Genes

In our comprehensive examination of drug sensitivity for the six LactoKey genes in the KMT2Ar-AML context, we screened 198 compounds and identified 43 with significant sensitivity profiles. To ensure a targeted and effective analysis, these compounds were chosen due to their well-characterized mechanisms of action and their potential to specifically interact with the identified LactoKey genes. Notably, among the top ten most responsive drugs were PI3K inhibitors, specifically Dactolisib and GNE-317. The top five drugs in terms of sensitivity, ranked by their inhibitory concentration values, were Sepantronium bromide, a CDK9 inhibitor, Luminespib, Dactolisib, and Epirubicin (Fig. 8A-B). KMT2Ar-AML samples with high expression of the six key LactoKey genes (PFN1, S100A6, CBR1, LDHB, LGALS1, PRDX1) exhibited increased sensitivity to Dactolisib, Epirubicin, AZD7762_1022, Trametinib_1372, and Pevonedistat_1529(Fig. 8C-H).

By pinpointing essential LactoKey genes and employing machine learning methodologies to investigate the significance of lactylation, our research has contributed to a deeper understanding of the pathogenesis associated with KMT2Ar-AML. This work establishes a foundation for subsequent molecular studies. Employing sophisticated machine learning algorithms, our research has identified six crucial LactoKey genes (PFN1, S100A6, CBR1, LDHB, LGALS1, and PRDX1) that are highly relevant to KMT2Ar-AML, indicating their involvement in lactylation and their potential utility as biomarkers for disease progression and as targets for therapeutic interventions. This is consistent with previous research demonstrating the roles of LGALS1 and BCL2A1 in promoting AML[20], as well as the potential of LGALS1 inhibitors to enhance the efficacy of chemotherapy and improve survival outcomes[21, 22]. The heightened expression levels of LGALS1 have been linked to reduced survival rates, highlighting the significance of therapeutically targeting this molecule[23]. Additionally, the relationship observed between S100A6 and the activities of CD34 + cells indicates potential pathways for the advancement of biomarkers in the treatment of acute myeloid leukemia (AML) [24]. This finding plays a significant role in enhancing our comprehension of the underlying molecular mechanisms and aids in the formulation of therapeutic strategies specifically targeting KMT2Ar-AML.

The investigation of biological functions and pathways has elucidated the involvement of LactoKey genes in fundamental biological processes, including cellular metabolism, energy generation, and protein synthesis. Dysregulation of these processes is intricately linked to the advancement of AML. Notably, CBR1 is intricately associated with energy metabolism and mitochondrial activity[25], whereas LDHB exhibits a notable positive relationship with the protein synthesis pathway[26].These results highlight the essential function of these genes in the development and advancement of KMT2Ar-AML.

The results of our investigation offer valuable perspectives on the association between LactoKey Genes and the infiltration of immune cells. This illuminates the intricate dynamics present within the tumor microenvironment and their influence on the advancement of cancer. Specifically, our research highlights the role of LactoKey genes in promoting immune cell infiltration in KMT2Ar-AML, particularly increasing levels of MDSCs and other immune cells compared to CN-AML. These results underscore the significance of lactylation in immune evasion and the advancement of disease in this context. This correlation highlights the possibility of targeting these genes for therapeutic intervention, thereby providing a fundamental approach to comprehending and controlling the tumor microenvironment for enhanced outcomes[27, 28].

This research underscores the significant impact of immune cells on the advancement of KMT2Ar-AML and points to the promising strategy of focusing on LactoKey genes to improve the infiltration of immune cells and inhibit tumor proliferation. This offers promising directions for developing innovative immunotherapies. The discovery of unique lactylation subtypes, namely Cluster A and Cluster B, highlights the diversity within KMT2Ar-AML. This finding exposes intricate relationships between lactylation mechanisms and the underlying biology of the disease. This heterogeneity suggests that personalized therapeutic strategies targeting these subtypes could significantly improve patient outcomes. The notably short median survival of 4 months in relapsed/refractory KMT2Ar-AML cases underscores the urgent need for effective biomarkers and therapies. Our drug sensitivity analysis identified 43 potential therapeutic agents, with PI3K inhibitors like Dactolisib and GNE-317 standing out due to their strong association with LactoKey gene expression. The results of this study underscore the significant role of LactoKey genes in influencing drug responsiveness, indicating that these factors may be crucial for addressing resistance in the treatment of KMT2Ar-AML. Furthermore, the clinical ramifications of these discoveries necessitate additional exploration through clinical trials to confirm the effectiveness of these targeted therapeutic approaches. Future investigations should aim to elucidate the intricate molecular mechanisms governing the relationship between LactoKey genes and immune modulation in KMT2Ar-AML, as well as to devise combination therapies capable of simultaneously addressing both the epigenetic and immune aspects of the disease. In addition, investigating the involvement of lactylation in other subtypes of acute myeloid leukemia (AML) may broaden the potential application of these treatment strategies to various leukemia forms.

Pharmacological compounds such as Dactolisib interfere with the metabolic functions and growth mechanisms of tumor cells that are modulated by LactoKey genes by targeting and inhibiting the PI3K/Akt/mTOR signaling pathway. Likewise, Epirubicin, AZD7762, and Trametinib target cell proliferation pathways[29–31], thereby indirectly affecting LactoKey genes. Pevonedistat, through its influence on protein stability[32], implies a wider-ranging impact on tumor biology involving LactoKey genes.

Moreover, the association between LactoKey genes and increased immune infiltration highlights their involvement in the progression of acute myeloid leukemia (AML) by modulating the immune response. This suggests that targeting pathways such as PI3K/Akt/mTOR may alter the immunosuppressive milieu[33, 34], promoting a more robust immune reaction against tumors. By connecting molecular findings with therapeutic advancements, this strategy represents a significant advancement in the fight against AML.

Future studies should focus on empirically validating these findings through functional assays and in vivo studies to establish the clinical relevance of LactoKey genes in KMT2Ar-AML. Additionally, exploring the role of lactylation in other hematologic malignancies and solid tumors could unveil broader applications for these epigenetic regulators, potentially leading to the development of universal therapeutic targets. Specifically, investigating how lactylation affects immune evasion and resistance mechanisms in different cancer contexts could provide valuable insights for designing more effective combination therapies. Considering the potential demonstrated by PI3K inhibitors in our investigation, subsequent studies ought to concentrate on refining the dosage and timing of these agents within clinical applications. The aim should be to enhance therapeutic effectiveness while concurrently reducing adverse reactions. Furthermore, the combination of lactylation-targeted treatments with current therapeutic strategies, including chemotherapy and immune checkpoint inhibitors, may facilitate the development of more holistic and efficient treatment protocols [35, 36]. As we continue to unravel the complexities of KMT2Ar-AML, a multidisciplinary approach combining genomics, epigenetics, and immunology will be essential to fully exploit the therapeutic potential of LactoKey genes and improve patient outcomes across different cancer types.

In summary, our research sheds light on the importance of lactylation in the development and advancement of KMT2Ar-AML, identifying new biomarkers and possible therapeutic targets. This supports a shift toward personalized treatment strategies based on a comprehensive molecular understanding of cancer. As we further investigate the intricate nature of epigenetic alterations such as lactylation, we move towards a future where cancer therapy is customized to the specific genetic and epigenetic profile of each patient's condition.

4.1 Microarray Data Source

A database of gene expression data (e.g., https://www.ncbi.nlm.nih.gov/geo/) can be found at the Gene Expression Omnibus (GEO)[37]. Searching the GEO database for “Acute Myeloid Leukemia with Mixed Lineage Leukemia rearrangements”, “Homo sapiens” [porgn: txid9606], and “Expression profiling by array”. The R package sva(version 3.40.0) was used to analyze 70 KMT2Ar-AML samples, characterized by the 11q23 translocation of the KMT2A gene, and 52 control samples from wild-type AML without chromosomal abnormalities, contained in GSE17855[38–40] and GSE22056[41]. The PMID:37242427[42], 35761067[43], 36092712[44] databases (Table S1) were used to obtain 336 lactylation genes, respectively.

4.2 Identification of immune-differentiated genes

In order to assess the influence of immune-related gene expression levels on KMT2A, a differential gene analysis was conducted using the R package "limma." This analysis aimed to identify genes that exhibit differential expression between diseased samples and control samples within the integrated dataset[45]. The criteria established for determining differentially expressed genes (DEGs) included an absolute log2 fold change (log2FC) greater than 0.5 and an adjusted P-value of less than 0.05, which indicates genes with upregulated expression. Conversely, a log2FC of less than − 0.5 alongside an adjusted P-value of less than 0.05 signifies genes with downregulated expression. Following this, DEGs associated with lactylation were analyzed. Specifically, a DEG is considered to be differentially expressed when its log2 fold change exceeds 0.5 and its adjusted P-value is below 0.05.

4.3 Enhancement of functionality

To ascertain the probable functions of various potential targets, Gene Ontology (GO) was utilized for the analysis. This approach specifically focused on three main categories: molecular functions (MF), biological processes (BP), and cellular components (CC)[46]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) serves as a prominent repository for information pertaining to genomes, biological pathways, diseases, and pharmaceuticals [47]. In this study, the analysis of Gene Ontology (GO) annotations and the enrichment of KEGG pathways for differentially expressed genes were conducted utilizing the clusterProfiler package available in R [48]. A threshold of less than 0.05 for the false discovery rate was deemed to indicate statistical significance.

4.4 Gene set enrichment analysis (GSEA)

Gene Set Enrichment Analysis (GSEA) was conducted utilizing the clusterProfiler package (version 4.2.2) in R. This analytical approach assesses both the statistical significance and the consistency of designated gene sets between two distinct biological states. To ascertain the adjusted p-values, the Benjamini-Hochberg method was employed, with a threshold for significance established at an adjusted p-value of less than 0.05[49].

4.5 RegNetwork Analysis

RegNetwork serves as a comprehensive online resource that aggregates both experimentally confirmed and predicted data pertaining to gene regulation. Within this repository dedicated to regulatory networks, users have the capability to explore and identify various transcription factors (TFs), microRNAs (miRNAs), and genes, allowing for the examination of their combinatorial and synergistic regulatory interactions. The construction of a TF-miRNA-gene regulatory network was accomplished utilizing Cytoscape[50].

4.6 Identification of Molecular Subtypes Utilizing Key Immunomodulators

The consistency clustering approach is a resampling technique employed to ascertain the membership of individual components and their respective subgroup classifications, along with the validation of these clusters. The R package ConsensusClusterPlus was utilized to unveil immunological patterns predicated on notable differentially expressed immune-related genes (Imm-DEGs) [51].

4.7 Screening and Validation of Diagnostic Markers

In this study, novel and significant biomarkers for KMT2Ar-AML were identified through the application of three distinct machine-learning algorithms: random forests (RF)[52], least absolute shrinkage and selection operator (LASSO) logistic regression[53], and support vector machine-recursive feature elimination (SVM-RFE)[54]. The random forest methodology was executed utilizing the R package "randomForest" (version 4.14). The LASSO logistic regression analysis was performed using the R package "glmnet" (version 4.4)[55]. A ten-fold cross-validation approach was employed to select the optimization parameters that satisfied the minimum criteria. Subsequently, genes exhibiting common characteristics across more than one of the aforementioned classification models were identified. To evaluate the predictive performance of the algorithms, receiver operating characteristic (ROC) curves were analyzed, and the area under the curve (AUC) was computed. The statistical significance of the findings was established through a two-tailed t-test, with a P value threshold set at less than 0.05.

4.8 Analysis of Immune Infiltration

The immune microenvironment is primarily composed of various immune cells, inflammatory cells, fibroblasts, mesenchymal components, as well as cytokines and chemokines. Analyzing immune cell infiltration is crucial for predicting disease progression and the efficacy of therapeutic interventions. In this context, a method for RNA transcript analysis known as "Cell type identification by estimating relative subsets of RNA transcripts" (CIBERSORT) was utilized to quantify the infiltration of 23 distinct immune cell types [56]. The CIBERSORT algorithm facilitates the identification of significant subpopulations of immune cells within pathological conditions by enabling a quantitative analysis of these cell types. For visualization purposes, a box plot was employed to present the infiltration results across different groups, while a scatter plot illustrated the interrelations among various immune cells. Additionally, a lollipop plot was constructed to depict the relationship between specific characteristic genes and the infiltrating immune cell populations. Only findings with a significance level of P < 0.05 were included in the presentation of results [57].

4.9 The Correlation Between Gene Expression and Drug Sensitivity

The Drug Sensitivity Analysis of GSCA Lite has compiled data on 198 small molecules sourced from the Cancer Therapeutics Response Portal (CTRP) (https://portals.broadinstitute.org/ctrp/) and an additional 198 small molecules obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) (https://www.cancerrxgene.org/) [58, 59]. This analysis integrates cancer cell lines from both GDSC and CTRP to facilitate the assessment of drug sensitivity alongside gene expression profiling [60]. In this dataset, each gene's expression is evaluated through Spearman correlation analysis in relation to the sensitivity of small molecules/drugs, quantified as the IC50 value.

Ethics approval and consent to participate

This study utilized publicly available data from the Gene Expression Omnibus (GEO) database, specifically datasets GSE17855 and GSE22056. Experimental data were generated in-house. As the study did not involve direct human or animal subjects, ethical approval was not required. All data used in this study were obtained in compliance with relevant guidelines and regulations.

Funding

This study was supported by grant from the National Natural Science Foundation of China (No. 81900130), the Natural Science Foundation of the Jiangsu Higher Education Institution of China (No. 18KJA320005), and the Translational Research Grant of NCRCH (No. 2020WSB03).

Author Contribution

All of the authors contributed to the original ideas and writing of this paper. D.L. contributed to data collection, statistical analysis, and paper writing. D.L., YJ. J., and X.M. wrote the paper. YJ. J. and YJ. J. drew the figures. X.M. and YJ. J. made critical revisions to this paper. All of the authors read and approved the final manuscript.

Acknowledgments

We thank the participants of the Suzhou Yuande Youqin Medical Laboratory who made their summary statistics publicly available for this study.

Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability

Gene expression data for this study are from the GEO database, accessible at [https://www.ncbi.nlm.nih.gov/geo/], specifically datasets GSE17855 and GSE22056. Information on 336 lactylation genes derived from PMIDs 37242427, 35761067, and 36092712 is in Supplementary Table 1. Drug sensitivity data for 198 small molecules from CTRP and GDSC can be found at (https://portals.broadinstitute.org/ctrp/) and [https://www.cancerrxgene.org/].

Shimony, S., Stahl, M. & Stone, R. M. Acute myeloid leukemia: 2023 update on diagnosis, risk-stratification, and management. Am. J. Hematol. 98 (3), 502–526 (2023).
Miraki-Moud, F. et al. Acute myeloid leukemia does not deplete normal hematopoietic stem cells but induces cytopenias by impeding their differentiation. Proc. Natl. Acad. Sci. U S A. 110 (33), 13576–13581 (2013).
De Kouchkovsky, I. & Abdul-Hay, M. Acute myeloid leukemia: a comprehensive review and 2016 update. Blood Cancer J. 6 (7), e441 (2016).
Winters, A. C. & Bernt, K. M. MLL-Rearranged Leukemias-An Update on Science and Clinical Approaches. Front. Pediatr. 5, 4 (2017).
Groschel, S. et al. Deregulated expression of EVI1 defines a poor prognostic subset of MLL-rearranged acute myeloid leukemias: a study of the German-Austrian Acute Myeloid Leukemia Study Group and the Dutch-Belgian-Swiss HOVON/SAKK Cooperative Group. J. Clin. Oncol. 31 (1), 95–103 (2013).
Cerveira, N. et al. Genetic and clinical characterization of 45 acute leukemia patients with MLL gene rearrangements from a single institution. Mol. Oncol. 6 (5), 553–564 (2012).
Meyer, C. et al. The KMT2A recombinome of acute leukemias in 2023. Leukemia. 37 (5), 988–1005 (2023).
Thol, F., Schlenk, R. F., Heuser, M. & Ganser, A. How I treat refractory and early relapsed acute myeloid leukemia. Blood. 126 (3), 319–327 (2015).
Hu, L. et al. DNA methylation-based prognostic biomarkers of acute myeloid leukemia patients. Ann. Transl Med. 7 (23), 737 (2019).
Zhang, D. et al. Metabolic regulation of gene expression by histone lactylation. Nature. 574 (7779), 575–580 (2019).
Xiong, J. et al. Lactylation-driven METTL3-mediated RNA m(6)A modification promotes immunosuppression of tumor-infiltrating myeloid cells. Mol. Cell. 82 (9), 1660–77e10 (2022).
Wang, T. et al. Lactate-induced protein lactylation: A bridge between epigenetics and metabolic reprogramming in cancer. Cell. Prolif. 56 (10), e13478 (2023).
Jiang, J. et al. Lactate Modulates Cellular Metabolism Through Histone Lactylation-Mediated Gene Expression in Non-Small Cell Lung Cancer. Front. Oncol. 11, 647559 (2021).
Yu, J. et al. Histone lactylation drives oncogenesis by facilitating m(6)A reader protein YTHDF2 expression in ocular melanoma. Genome Biol. 22 (1), 85 (2021).
Yang, J. et al. A Positive Feedback Loop between Inactive VHL-Triggered Histone Lactylation and PDGFRbeta Signaling Drives Clear Cell Renal Cell Carcinoma Progression. Int. J. Biol. Sci. 18 (8), 3470–3483 (2022).
Yang, D. et al. Identification of lysine-lactylated substrates in gastric cancer cells. iScience. 25 (7), 104630 (2022).
Yang, H. et al. Identification of lactylation related model to predict prognostic, tumor infiltrating immunocytes and response of immunotherapy in gastric cancer. Front. Immunol. 14, 1149989 (2023).
He, Y. et al. Numb/Parkin-directed mitochondrial fitness governs cancer cell fate via metabolic regulation of histone lactylation. Cell. Rep. 42 (2), 112033 (2023).
Wei, S. et al. Histone lactylation promotes malignant progression by facilitating USP39 expression to target PI3K/AKT/HIF-1alpha signal pathway in endometrial carcinoma. Cell. Death Discov. 10 (1), 121 (2024).
Gao, X. Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Unravels the Molecular Feature of Tumor-Associated Macrophage of Acute Myeloid Leukemia. Genet. Res. (Camb). 2024, 5539065 (2024).
Li, X. et al. Clinical significance of CD34(+)CD117(dim)/CD34(+)CD117(bri) myeloblast-associated gene expression in t(8;21) acute myeloid leukemia. Front. Med. 15 (4), 608–620 (2021).
Li, K. et al. Single-cell analysis reveals the chemotherapy-induced cellular reprogramming and novel therapeutic targets in relapsed/refractory acute myeloid leukemia. Leukemia. 37 (2), 308–325 (2023).
Ruvolo, P. P. et al. LGALS1 acts as a pro-survival molecule in AML. Biochim. Biophys. Acta Mol. Cell. Res. 1867 (10), 118785 (2020).
Zhai, Y. et al. Upregulation of S100A6 and its relation with CD34(+) cells apoptosis in high-risk myelodysplastic syndromes patients. Heliyon. 9 (8), e18947 (2023).
Bateman, R. L., Rauh, D., Tavshanjian, B. & Shokat, K. M. Human carbonyl reductase 1 is an S-nitrosoglutathione reductase. J. Biol. Chem. 283 (51), 35756–35762 (2008).
Sun, W. et al. Lactate dehydrogenase B is associated with the response to neoadjuvant chemotherapy in oral squamous cell carcinoma. PLoS One. 10 (5), e0125976 (2015).
De Cicco, P., Ercolano, G. & Ianaro, A. The New Era of Cancer Immunotherapy: Targeting Myeloid-Derived Suppressor Cells to Overcome Immune Evasion. Front. Immunol. 11, 1680 (2020).
Melssen, M. M., Sheybani, N. D., Leick, K. M. & Slingluff, C. L. Jr. Barriers to immune cell infiltration in tumors. J. Immunother Cancer ;11(4). (2023).
Zabludoff, S. D. et al. AZD7762, a novel checkpoint kinase inhibitor, drives checkpoint abrogation and potentiates DNA-targeted therapies. Mol. Cancer Ther. 7 (9), 2955–2966 (2008).
Salama, A. K. & Kim, K. B. Trametinib (GSK1120212) in the treatment of melanoma. Expert Opin. Pharmacother. 14 (5), 619–627 (2013).
Luiz, M. T. et al. Epirubicin: Biological Properties, Analytical Methods, and Drug Delivery Nanosystems. Crit. Rev. Anal. Chem. 53 (5), 1080–1093 (2023).
Ferris, J. et al. Pevonedistat (MLN4924): mechanism of cell death induction and therapeutic potential in colorectal cancer. Cell. Death Discov. 6, 61 (2020).
Geng, Y. et al. PI3K/AKT/mTOR pathway-derived risk score exhibits correlation with immune infiltration in uveal melanoma patients. Front. Oncol. 13, 1167930 (2023).
Wen, W. et al. Expression and Clinical Significance of NUDCD1, PI3K/AKT/mTOR Signaling Pathway-Related Molecules and Immune Infiltration in Breast Cancer. Clin. Breast Cancer (2024).
Huang, Z. W. et al. STAT5 promotes PD-L1 expression by facilitating histone lactylation to drive immunosuppression in acute myeloid leukemia. Signal. Transduct. Target. Ther. 8 (1), 391 (2023).
Lin, J., Liu, G., Chen, L., Kwok, H. F. & Lin, Y. Targeting lactate-related cell cycle activities for cancer therapy. Semin Cancer Biol. 86 (Pt 3), 1231–1243 (2022).
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40 (10), 4288–4297 (2012).
Balgobind, B. V. et al. Evaluation of gene expression signatures predictive of cytogenetic and molecular subtypes of pediatric acute myeloid leukemia. Haematologica. 96 (2), 221–230 (2011).
Hartsink-Segers, S. A. et al. Aurora kinases in childhood acute leukemia: the promise of aurora B as therapeutic target. Leukemia. 27 (3), 560–568 (2013).
Sandahl, J. D. et al. t(6;9)(p22;q34)/DEK-NUP214-rearranged pediatric myeloid leukemia: an international study of 62 patients. Haematologica. 99 (5), 865–872 (2014).
de Jonge, H. J. et al. High VEGFC expression is associated with unique gene expression profiles and predicts adverse prognosis in pediatric and adult acute myeloid leukemia. Blood. 116 (10), 1747–1754 (2010).
Cheng, Z. et al. Lactylation-Related Gene Signature Effectively Predicts Prognosis and Treatment Responsiveness in Hepatocellular Carcinoma. Pharmaceuticals (Basel) ;16(5). (2023).
Wan, N. et al. Cyclic immonium ion of lactyllysine reveals widespread lactylation in the human proteome. Nat. Methods. 19 (7), 854–864 (2022).
Liu, X., Zhang, Y., Li, W. & Zhou, X. Lactylation, an emerging hallmark of metabolic reprogramming: Current progress and open challenges. Front. Cell. Dev. Biol. 10, 972020 (2022).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 (7), e47 (2015).
Gene Ontology, C. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43 (Database issue), D1049–D1056 (2015).
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 (1), 27–30 (2000).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 16 (5), 284–287 (2012).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U S A. 102 (43), 15545–15550 (2005).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13 (11), 2498–2504 (2003).
Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 26 (12), 1572–1573 (2010).
Tai, A. M. Y. et al. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artif. Intell. Med. 99, 101704 (2019).
Cheung-Lee, W. L. & Link, A. J. Genome mining for lasso peptides: past, present, and future. J. Ind. Microbiol. Biotechnol. 46 (9–10), 1371–1379 (2019).
Huang, S. et al. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteom. 15 (1), 41–51 (2018).
Engebretsen, S. & Bohlin, J. Statistical predictions with glmnet. Clin. Epigenetics. 11 (1), 123 (2019).
Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 462 (7269), 108–112 (2009).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37 (7), 773–782 (2019).
Rees, M. G. et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12 (2), 109–116 (2016).
Liu, C. J. et al. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 34 (21), 3771–3772 (2018).
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 483 (7391), 570–575 (2012).

No competing interests reported.

Download PDF

Editorial decision: Revision requested
11 Nov, 2024
Reviews received at journal
10 Nov, 2024
Reviews received at journal
08 Nov, 2024
Reviewers agreed at journal
04 Nov, 2024
Reviewers agreed at journal
31 Oct, 2024
Reviewers agreed at journal
31 Oct, 2024
Reviewers invited by journal
30 Oct, 2024
Editor assigned by journal
30 Oct, 2024
Editor invited by journal
25 Oct, 2024
Submission checks completed at journal
23 Oct, 2024
First submitted to journal
07 Oct, 2024

You are reading this latest preprint version

Lactylation Modulation Identifies Key Biomarkers and Therapeutic Targets in KMT2A- Rearranged AML

Status:

Version 1

Abstract

Figures

1. Introduction

2. Results

2.1 Examination of Lactylation-Dependent Differentially Expressed Genes (Lac-DEGs) in Acute Myeloid Leukemia (AML) with KMT2A Rearrangement

2.2 Identification and Validation of Diagnostic Markers

2.3 Immune Infiltration Analysis of LactoKey Genes

2.4 Biological Functions of LactoKey Genes

2.5 Examination of Gene-miRNA and Gene-TF Interaction Networks

2.6 Lactylation Subtypes in KMT2Ar-AML: Divergent Expression Profiles and Pathway Associations

2.7 Drug Sensitivity Analysis of LactoKey Genes

3. Discussion

4. Materials and methods

4.1 Microarray Data Source

4.2 Identification of immune-differentiated genes

4.3 Enhancement of functionality

4.4 Gene set enrichment analysis (GSEA)

4.5 RegNetwork Analysis

4.6 Identification of Molecular Subtypes Utilizing Key Immunomodulators

4.7 Screening and Validation of Diagnostic Markers

4.8 Analysis of Immune Infiltration

4.9 The Correlation Between Gene Expression and Drug Sensitivity

Declarations

Ethics approval and consent to participate

Funding

Author Contribution

Acknowledgments

Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1

Lactylation Modulation Identifies Key Biomarkers and Therapeutic Targets in KMT2A- Rearranged AML

Status:

Version 1

Abstract

Figures

1. Introduction

2. Results

2.1 Examination of Lactylation-Dependent Differentially Expressed Genes (Lac-DEGs) in Acute Myeloid Leukemia (AML) with KMT2A Rearrangement

2.2 Identification and Validation of Diagnostic Markers

2.3 Immune Infiltration Analysis of LactoKey Genes

2.4 Biological Functions of LactoKey Genes

2.5 Examination of Gene-miRNA and Gene-TF Interaction Networks

2.6 Lactylation Subtypes in KMT2Ar-AML: Divergent Expression Profiles and Pathway Associations

2.7 Drug Sensitivity Analysis of LactoKey Genes

3. Discussion

4. Materials and methods

4.1 Microarray Data Source

4.2 Identification of immune-differentiated genes

4.3 Enhancement of functionality

4.4 Gene set enrichment analysis (GSEA)

4.5 RegNetwork Analysis

4.6 Identification of Molecular Subtypes Utilizing Key Immunomodulators

4.7 Screening and Validation of Diagnostic Markers

4.8 Analysis of Immune Infiltration

4.9 The Correlation Between Gene Expression and Drug Sensitivity

Declarations

Ethics approval and consent to participate

Funding

Author Contribution

Acknowledgments

Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1

Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.