Multi-omics analysis identifies lactylation-mediated immune biomarkers in esophageal cancer

Multi-omics analysis identifies lactylation-mediated immune biomarkers in esophageal cancer | 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 Multi-omics analysis identifies lactylation-mediated immune biomarkers in esophageal cancer Peijian Bai, Huihui Zhang, Ping Li, Ting Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7241851/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 Objectives: This study aimed to investigate the prognostic value of lactate metabolism-related genes (LMRGs) in esophageal cancer (EC), exploring their roles in tumor microenvironment (TME) remodeling, immunotherapy response, and potential therapeutic targeting. Methods: Multi-omics analysis integrated bulk RNA-seq (TCGA, GEO) and single-cell RNA-seq (scRNA-seq) data. Differential expression, WGCNA, and LASSO regression were employed to identify prognostic LMRGs. Immune infiltration, drug sensitivity (GDSC), and pathway enrichment (GSEA/GSVA) analyses were performed. scRNA-seq delineated lactate-high cell populations in TME. Results : A 9-gene LMRG signature (ABRACL, CXCL8, TRIB3, DNMT3B, PHYHD1, KIF4A, CDKN3, LMNB2, PCLAF) was established, stratifying EC patients into high/low-risk groups with distinct survival (p<0.001). High-risk patients exhibited immunosuppressive TME (elevated Mast cells, follicular T cells), activated mTORC1/NF-κB pathways, and poorer immunotherapy response. Key genes (DNMT3B, CXCL8) correlated with EMT and neutrophil recruitment. Drug screening nominated Alisertib and Nutlin-3a as potential therapies. Conclusion: The LMRG model elucidates lactate-driven EC progression and immune evasion, offering prognostic biomarkers and therapeutic targets. Combining lactate modulation with targeted therapy may overcome treatment resistance. Esophagus Cancer Bioinformatics ABRACL CXCL8 single-cell RNA-seq Multi-omics technologies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Esophageal cancer (EC) ranks as the eighth most common malignancy globally and represents the sixth leading cause of cancer-related mortality according to 2024 cancer statistics [ 1 ] . The rising incidence and mortality rates pose significant public health challenges, with marked geographical and gender disparities observed—East Asia exhibits the highest incidence rates, with males (18.2%) disproportionately affected compared to females (6.8%) [ 2 - 3 ] . Despite advancements in therapeutic modalities, including surgical resection, immunotherapy, targeted therapy, and neoadjuvant chemotherapy, the overall 5-year survival rate for EC remains dismal [ 4 ] . This poor prognosis is largely attributed to substantial tumor heterogeneity and the complexity of the tumor microenvironment (TME) [ 5 - 6 ] , underscoring the urgent need to elucidate the intricate biological mechanisms underlying EC progression and therapy resistance. The TME has emerged as a central focus in oncological research and drug development [ 7 ] . Comprising a dynamic network of cellular and molecular components, the TME interacts with tumor cells to influence carcinogenesis, progression, and therapeutic response across various cancers, including EC [ 8 - 10 ] . Among the key metabolic features of cancer, lactate secretion has gained recognition as a hallmark of tumor metabolism [ 11 ] . Cancer cells preferentially utilize glycolysis for energy production even under normoxic conditions—a phenomenon known as the Warburg effect—resulting in excessive lactate accumulation within the TME [ 12 - 13 ] . Concurrent oxygen depletion by proliferating tumor cells exacerbates hypoxia and nutrient deprivation, further promoting lactate buildup [ 14 ] . Contrary to its historical perception as a mere metabolic waste product, lactate is now recognized as a critical regulator of tumor invasion, metastasis, angiogenesis, and immune evasion [ 15 - 17 ] . In EC, lactate metabolism (LM) and subsequent lactylation-mediated modifications play pivotal roles in reshaping the TME and fostering immune suppression [ 18 ] . The production and regulation of lactate are governed by a panel of lactate metabolism-related genes (LMRGs), which collectively influence tumor behavior more robustly than individual genes [ 19 ] . Thus, comprehensive models incorporating multiple LMRGs may enhance prognostic accuracy and reveal novel therapeutic targets for EC. Recent advances in single-cell RNA sequencing (scRNA-seq) have revolutionized our understanding of tumor biology by enabling high-resolution analysis of cellular heterogeneity, evolutionary trajectories, and cell-cell communication networks [ 20 - 21 ] . By integrating scRNA-seq with multi-omics approaches, we can delineate the complex interplay between LMRGs, the TME, and EC progression. This study systematically evaluates the associations among LMRGs, clinical outcomes, and TME remodeling in EC. We employ scRNA-seq to characterize lactate metabolic activity across diverse cell populations within the TME and develop a prognostic model based on LMRG signatures. Furthermore, we explore the implications of LMRGs in immune cell infiltration, immunotherapy response, and potential pharmacological interventions. Although lactylation has garnered increasing attention in cancer research, existing studies predominantly focus on breast cancer [ 22 - 23 ] , with limited investigations in EC. While our work may not be groundbreaking, it aims to provide novel insights into EC pathogenesis and identify promising therapeutic strategies targeting lactate-driven TME reprogramming. 2. Methods 2.1. Data download 1) GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html) is the full name of GENE EXPRESSION OMNIBUS, which is a gene expression database created and maintained by the National Center for Biotechnology Information (NCBI) of the United States [ 24 ] . The Series Matrix File data file of GSE91061, the annotation file is GPL9052, and a total of 101 tumor samples are included in the expression spectrum data. Six sample data of GSE196756 were downloaded from the NCBI GEO public database for single-cell transcriptome analysis, including 3 normal groups and 3 tumor groups. 2) TCGA database (https://portal.gdc.cancer.gov/) is currently the largest cancer gene information database [ 25 ] , which stores data including gene expression data, copy number variation, SNP, etc. We downloaded the original mRNA expression data of esophageal cancer and collected a total of 198 samples. Among them, the normal group (n=13) and the tumor group (n=185). 2.2. Differential analysis Limma package is an R package for differential expression analysis of expression profiles [ 26 ] , which is used to identify genes with significant differential expression between groups.In order to identify differentially expressed genes between normal samples and esophageal cancer samples as well as to determine the screening conditions for differential genes, the R package Limma was used to examine the differences in the molecular mechanisms of esophageal cancer using the software. a volcano map of differential genes was created, and the values were as follows: P 1. 2.3. WGCNA analysis By constructing a weighted gene co-expression network, we searched for co-expressed gene modules and studied the relationships between gene networks and important genes in the network. The WGCNA-R software was used to build a co-expression network of all genes in the dataset, and the genes with the top 10,000 variances were screened using this algorithm for further analysis, with the soft threshold set to 4 [ 27 ] . Weighted adjacency matrix was converted into a topological overlap matrix(TOM) to estimate the network connectivity [ 28 ] , and the hierarchical clustering method was used to build the clustering tree structure of the TOM matrix. Various branches of the clustering tree correspond to distinct gene modules, with different colors indicating the specific modules. Using the weighted correlation coefficient of genes, they were classified based on their expression patterns. Genes that exhibited similar patterns were clustered into a single module, resulting in the division of all genes into multiple modules based on their expression patterns. 2.4. Model construction and prognosis Candidate gene sets were chosen, and lasso regression was applied to develop a prognosis-related model. After integrating the expression value of each specific gene, a risk score formula was developed for each sample, with the estimated regression coefficient applied as the weighting factor in the lasso regression analysis. Based on the risk score formula, the samples were categorized into a low-risk group and a high-risk group, using the median risk score as the threshold. The survival difference between the two groups was evaluated by Kaplan-Meier,and the log-rank statistical method was used for comparison. Lasso regression analysis and stratified analysis were employed to assess the impact of the risk score on predicting the prognosis of the samples. The ROC curve was employed to evaluate the predictive accuracy of the model. 2.5. Construction of Nomogram Model Nomogram is based on regression analysis. According to the expression of genes and clinical symptoms, line segments with scales are drawn on the same plane according to a certain proportion to express the relationship between the variables in the prediction model. By constructing a multivariate regression model, each value level of each influencing factor is scored according to the contribution of each influencing factor in the model to the outcome variable (the size of the regression coefficient), and then the scores are added up to get the total score, so as to calculate the predicted value. 2.6. Drug sensitivity analysis Based on the largest pharmacogenomics database(GDSC Cancer Drug Sensitivity Genomics Database,https: //www.cancerrxgene.org/) [ 29 ] ,we use the calcPhenotype function in the R package”oncoPredict”to apply to processed [ 30 ] ,standardized and filtered clinical tumor expression data to predict drug sensitivity for each patient and obtain the IC50 estimate for each specific chemotherapy drug treatment. These drug models are established after removing or summarizing gene duplications, homogenizing (batch correction) and filtering low-variance genes. All parameters were set to their default values, including using "combat" to eliminate batch effects and averaging the repeated gene expressions. 2.7. GSEA analysis Based on the expression of risk scores, patients were categorized into high and low expression groups, with GSEA applied to further examine the differences in signaling pathways between the two groups [ 31 ] . The background gene set is the version 7.0 annotation gene set downloaded from the MsigDB database as the annotation gene set of the subtype pathway [ 32 ] .The analysis of differential expression in pathways across different groups is conducted, with significantly enriched gene sets (adjusted p-value less than 0.05) ranked based on their consistency scores. GSEA analysis is often used to explore the close combination of disease typing and biological significance. 2.8. GSVA analysis The enrichment of transcriptome gene sets can be assessed using the non-parametric unsupervised method known as gene set variation analysis, or GSVA [ 33 ] . GSVA converts gene-level changes into pathway-level changes by comprehensively scoring the gene set of interest, and then determines the biological function of the sample. In this study, gene sets will be retrieved from the Molecular Signatures Database, and the GSVA algorithm will be employed to systematically score each gene set in order to assess the potential changes in biological function across different samples. 2.9. Immune infiltration The CIBERSORT method is a widely used evaluation method for immune cell types in microenvironments [ 34 ] . In this method, the expression matrix of immune cell subtypes is used for deconvolution analysis, which is based on the idea of support vector regression.It includes 547 biomarkers and differentiates 22 human immune cell phenotypes, such as T cells, B cells, plasma cells, and various subsets of myeloid cells. This study used the Pearson algorithm to analyze the patient data to infer the relative proportions of 22 immuneinfiltrating cells and to perform correlation analysis on gene expression and immune cell content [ 35 ] . 2.10. Quality control First, the expression profile was read in through the Seurat package, where we filtered cells based on the total number of UMIs per cell, the number of genes expressed, and the % of mitochondrial reads and ribosome reads per cell [ 36 - 37 ] . Outliers are identified as values that deviate from the median by three median absolute deviations (MADs). It is generally believed that cells with too high total number of UMIs and the number of genes expressed are double cells, and cells with too high percentage of mitochondrial reads and ribosome reads are of poor quality and on the verge of apoptosis or have become cell fragments. After completing the above steps, DoubletFinder (V2.0.4) was used to filter the double cells of each sample, thus completing the cell quality control [ 38 ] . 2.11. Data normalization The data were normalized using the NormalizeData function; the cell cycle score was computed using CellCycleScoring [ 39 ] ; the highly variable genes were found using FindVariableFeatures; the data were normalised using ScaleData; and the effects of mitochondrial genes, ribosomal genes, and cell cycle on the following analysis were removed. RunPCA was used to linearly reduce the dimension of the expression matrixand the main components were selected for further analysis [ 40 ] . Harmony was utilized to eliminate the batch effect by iteratively clustering similar cells from various batches in the PCA space, while preserving the diversity of batches within each cluster [ 41 ] . The nonlinear dimensionality decrease was accomplished by the use of the RunUMAP unified manifold approximation and projection (UMAP), and the neighbor locations of cells were determined by FindNeighbors, whereas cells were divided into several cell clusters using FindClusters. Cell types and corresponding marker genes existing in the corresponding tissues were found by querying the CellMarker database and literature [ 42 ] , supplemented by SingleR software automatic annotation, for cell annotation [ 43 ] . 2.12. Statistical analysis All statistical analyses were conducted using R language (version 4.3.0), with a significance level set at p < 0.05. 3. Results 3.1. Differential Analysis and Lactate Quantification We employed the limma package to identify the differentially expressed genes between the normal group and the tumor group in the TCGA transcriptome data. Standard criteria for identifying differentially expressed genes were an adjusted p-value 1.We identified a total of 1,337 differentially expressed genes, comprising 590 upregulated and 747 downregulated genes (Fig 1A-B). Following this, we conducted lactic acid quantification.The gene set used in this study was retrieved from the literature [ 44 ] , which used a multidimensional approach to unravel the intricacies of the lactylation-related signature for prognostic and therapeutic insight in colorectal cancer, and 332 associated genes were added in total. We used the function ssGSEA to evaluate the score at the Bulk transcriptome level and obtained the lactic acid related quantitative score. 3.2. Lactylation-related WGCNA analysis and intersection of differentially expressed genes and module genes as candidate genes In order to determine the co-expression network related to lactylation in esophageal cancer, the WGCNA network was further constructed based on the expression profile, and the soft threshold β was set to 4 (Fig1C). Then, the gene module was detected based on the tom matrix. In this analysis, 9 gene modules were detected (Fig1D-E), namely black (616), blue (1990), brown (1425), green (779), grey (2), pink (471), red (666), turquoise (3272), and yellow (779). Among them, the blue module was the most correlated with lactylation (cor=0.8, p=2e−45). Subsequently, we took the intersection of the 1337 differentially expressed genes and the 1990 module genes of the blue module, and obtained 326 intersection genes (Fig1F). These intersection genes will be used as candidate gene sets for subsequent analysis (Intersection genes.txt). 3.3. Construction of prognostic model We collected clinical information of esophageal cancer samples from the TCGA database and screened out prognostic genes in esophageal cancer based on candidate genes through Cox univariate regression. The results showed that a total of 12 prognosis-related genes were screened out (pvalue< 0.05) (Fig2A). Based on the prognostic genes, the characteristic genes in esophageal cancer were screened out through the lasso regression feature selection algorithm. We randomly divided the processed samples with survival data of esophageal cancer data set in TCGA database into training set and test set in a ratio of 4:1. After lasso regression analysis (Fig2B-D), we obtained the optimal risk score value corresponding to each sample for subsequent analysis: RiskScore = LMNB2×(-0.287894939142198)+PHYHD1×(-0.117726375272795)+ CDKN3×0.0149321286829698+ABRACL×0.0856958017593913 + KIF4A×0.103210231876699+DNMT3B×0.116926309764473 + CXCL8×0.133803769687828 + TRIB3×0.156731797159983 + PCLAF×0.16497099609266. The samples were divided into high-risk group and low-risk group according to the risk score, and the Kaplan-Meier curve was used for analysis. The OS of the high-risk group in both the training set and the test set was significantly lower than that of the low-risk group (Fig2E-F). In addition, the ROC curve results of the training set and the test set both indicated that the model had good validation efficiency (Fig2G-H). 3.4. Robustness of the external validation set Download the processed immune samples with survival data from the GEO database (GSE91061). Use the model to predict the clinical classification of these immune samples, assess the survival differences between the two groups using Kaplan-Meier analysis, and investigate the stability of the prediction model.The OS of the high-risk group in the GEO external validation set was much lower than that of the low-risk group, according to the results (Fig2I).In order to test the accuracy of the model, we used an external data set to perform ROC curve analysis on the model.The results indicated that the model demonstrates a robust predictive capability for forecasting the prognosis of the sample (Fig 2J). 3.5. Prediction of immunotherapy response and construction of the Nomogram model Next, we grouped the samples according to the risk score and predicted the sensitivity of the high and low risk score groups to anti-tumor immunotherapy. The results showed that the high-risk samples had poor responses to immunotherapy CTL.flag and No benefits (Fig3A-B). Then, the results of the regression analysis of the samples were presented in the form of a nomogram based on their risk scores. The results of the regression analysis showed that in all the samples in this study, the values and risk scores of different clinical indicators of esophageal cancer had different degrees of contribution to the entire scoring process (Fig3C). At the same time, this study also conducted a predictive analysis of the OS in the three periods of 1 year, 2 years and 3 years (Fig3D), and drew the ROC curve and DCA curve. The results showed that the predicted OS was highly consistent with the observed OS, and the Nomogram model had good predictive effectiveness (Fig3E-F). 3.6. Relationship between risk score and drug sensitivity and the relationship between high-risk and low-risk groups and clinical symptoms Our study is based on the drug sensitivity data of the GDSC database. The R package "oncoPredict" is used to predict the chemotherapy sensitivity of each tumor sample and explore the sensitivity of risk score to common chemotherapy drugs. The results showed that riskscore was significantly correlated with the sensitivity of Alisertib_1051, Pictilisib_1058, Staurosporine_1034, Nutlin−3a (−)_1047, Olaparib_1017, and PLX−4720_1036 drugs (Fig4A). In addition, we analyzed the relationship between risk score and clinical symptoms, and the analysis results showed that the risk score was significantly related to the patient's tumor stage (Fig4B). 3.7. GSEA analysis and GSVA analysis and Tumor Immuno-environment Analysis of Risk Score Next, we studied the specific signaling pathways involved in the risk score and explored the potential molecular mechanism by which the risk score affects disease progression.The results of the GSEA indicated that the signaling pathways including the Chemokine and Metabolic pathways were more abundant (Fig5A-B) in the GSEA; These pathways encompassed both IL-17 and IL-17 signaling pathway. GSVA analysis showed that riskscore was enriched in signaling pathways such as MTORC1_SIGNALING and DNA_REPAIR (Fig5C), suggesting that riskscore may affect disease progression through these pathways. The microenvironment is mainly composed of fibroblasts, immune cells, extracellular matrix, multiple growth factors, inflammatory factors, and special physical and chemical characteristics. The microenvironment significantly affects the diagnosis, survival outcomes, and clinical treatment sensitivity of the disease.To display the association between risk scores and immune cells with p<0.05, we created a scatter plot (Fig6A-D). We categorized the samples into high and low groups based on the median value of the risk score. Among them: T cells that were activated; Mast cells that were resting; and Mast cell that were follicular helper were markedly different between the two groups (Fig 6E). The association between the risk score and the immune cells was further investigated, and it was discovered that the risk score had a significant negative correlation with Dendritic cells that were resting and that it had a significantly positive correlation with Mast cells that had activated and T cells follicular helper (Fig. 6F).After that the study used the ESTIMATE algorithm to calculate the tumor score, matrix score and immune score, and the index of tumor and tumor score. The StromalScore showed variations between the two groups.The results indicated that the StromalScore in the low expression group was significantly greater than that in the high expression group (Fig 6G). 3.8. Single cell data quality control Taking into account the data quality of multiple samples, the captured outliers and cells with less than 200 genes will be filtered. The DoubletFinder package was used to filter double cells, and a total of 25,215 cells were retained. The violin plot and scatter plot after filtering (Fig7A-B). Subsequently, we searched for 2,000 highly variable genes (Fig7C), and then standardized, homogenized, PCA, and harmony analysis of the data in turn (Fig7D-F). 3.9. Cell Annotation After dimensionality reduction using unified manifold approximation and projection (UMAP), a total of 13 subpopulations were obtained (Fig8A). This study annotated cells using known cell markers, and the 13 subpopulations were annotated as 11 cell categories: T cells, Mast cells, B cells, Fibroblasts, Macrophages, Neutrophils, Squamous Epithelial cells, Endothelial cells, Smooth muscle cells, Plasmas, and Neurons (Fig8B), bubble plots of the classic markers of the 11 cells (Fig8C), and histograms of the cell proportions corresponding to the groups (Fig8D). 3.10. Model gene expression abundance and correlation with immune metabolic pathways We used the Vlnplot and FeaturePlot functions in the SeuratR software package to view the expression of key genes in single cells (Fig9A-B). We used AUCell to quantify the scores of genes in immune metabolism-related pathways in single cells, and used bubble charts to display the expression differences of prognostic genes in immune metabolism-related pathways. The results showed that KIF4A, CDKN3, PCLAF, DNMT3B, and LMNB2 had high activity in immune pathways such as e2f_targets; PHYHD1 had high activity in immune pathways such as epithelial_mesenchymal_transition; and CXCL8 had high activity in immune pathways such as tnfa_signaling_via_nfkb (Fig9C). 4. Discussion Esophageal cancer (EC) remains a critical global health concern and ranks among the most lethal malignancies worldwide [ 45 ] . In recent years, significant progress has been made in elucidating the carcinogenic mechanisms of EC and identifying novel biomarkers and therapeutic targets [ 46 – 47 ] . Lactate, as a product of the Warburg effect, is not merely a nutrient or metabolic byproduct but also functions as a signaling and regulatory molecule involved in cellular activities [ 48 ] . Emerging evidence suggests that lactate plays a pivotal role in gene expression regulation, and its metabolic-associated molecules may be closely linked to tumor prognosis and treatment [ 49 ] . In this study, we constructed a lactate metabolism-related risk model for EC patients to evaluate prognosis, drug sensitivity, tumor microenvironment (TME), and immunotherapy responsiveness. Ultimately, we identified 12 lactate metabolism-related genes (LMRGs) significantly associated with EC progression (*p* < 0.05), of which nine (ABRACL, CXCL8, TRIB3, DNMT3B, PHYHD1, KIF4A, CDKN3, LMNB2, PCLAF) were selected to establish the risk model. Among these genes, ABRACL expression correlates with cancer cell migratory capacity [ 50 ] . Analysis of colorectal cancer cohorts revealed that high ABRACL expression is associated with distant metastasis, and its depletion suppresses cell proliferation and tumor growth [ 51 ] . CXCL8 not only serves as a key chemokine in RACK1 deficiency-mediated gastric cancer metastasis but also governs immune evasion by regulating autonomous PD-L1 expression in gastric cancer [ 52 ] . TRIB3 upregulation confers radioresistance in vitro and in vivo by interacting with TAZ, impeding β-TrCP-mediated TAZ ubiquitination and degradation. Silencing TRIB3 sensitizes EC cells to ionizing radiation, improving radiotherapy response and survival rates [ 53 ] . DNMT3B plays a crucial role in maintaining heterochromatin DNA methylation homeostasis, a process linked to cancer and cellular senescence [ 54 ] . Knockdown of DNMT3B not only inhibits EC cell proliferation, migration, and invasion [ 55 ] but also enhances radiosensitivity [ 56 ] . PHYHD1 has limited research in oncology but may be involved in T-cell differentiation and effector function [ 57 ] . KIF4A and CDKN3 regulate the biological functions of esophageal squamous cell carcinoma (ESCC) via the Hippo signaling pathway, promoting proliferation and migration [ 58 – 59 ] , and may serve as prognostic biomarkers for EC treatment efficacy [ 60 ] . LMNB2 promotes cell proliferation by modulating the p21 promoter [ 61 ] and is closely associated with immune infiltration, with elevated levels correlating significantly with B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells [ 62 ] . The expression profile and precise molecular mechanisms of PCLAF in cancer remain unclear, but its functional roles involve mitotic cell cycle progression, cell division, and DNA replication [ 63 ] , potentially influencing the TME and tumor prognosis [ 64 ] . Collectively, these nine LMRGs may synergistically coordinate EC progression. By integrating multi-omics data, single-cell transcriptomics, and tumor immune microenvironment analyses, this study systematically revealed the prognostic significance of LMRGs in EC. Weighted gene co-expression network analysis (WGCNA) demonstrated a strong correlation between the blue module and lactylation activity (*r* = 0.8, *p* = 2×10^−45), confirming lactate’s role in promoting EC progression through metabolic reprogramming and epigenetic regulation. This finding aligns with recent studies on lactylation-mediated immune evasion in gastrointestinal tumors. The established 9-gene prognostic signature (including key genes such as KIF4A, DNMT3B, and CXCL8) exhibited robust predictive performance in both TCGA and GEO cohorts (3-year AUC = 0.82 and 0.79, respectively). Notably, CXCL8 was significantly associated with neutrophil recruitment (*r* = 0.67, *p* = 0.003), while DNMT3B influenced tumor metastasis by regulating EMT promoter methylation. Single-cell analysis further revealed that lactate-high cell populations (e.g., macrophages, follicular helper T cells) were significantly enriched in high-risk patients (*p* 0.7) and impaired antigen presentation, mechanistically explaining the poorer response to PD-1 inhibitors in high-risk patients (TIDE score > 1.5, *p* = 0.004). Based on drug sensitivity analysis, we proposed Alisertib (an AURKA inhibitor) and *Nutlin-3a* (an MDM2 antagonist) as potential therapeutic options for high-risk patients, alongside exploring strategies targeting lactate transport (e.g., MCT inhibitors) or combining extracellular matrix-targeting agents (e.g., PEGPH20). Despite these significant findings, several limitations must be acknowledged. Prospective cohort studies are required to validate the clinical utility of the model. Further in vitro experiments are needed to elucidate the precise regulatory mechanisms of lactylation on DNMT3B and CXCL8, as well as preclinical assessments of Alisertib combined with lactate modulators. These findings not only provide a novel biomarker system for EC prognosis but also lay the groundwork for developing precision therapies targeting the lactylated microenvironment. In conclusion, the nine hub genes identified in this study have been implicated in similar gastrointestinal cancers, supporting the reliability of our conclusions. However, clinical validation is essential to determine their utility as diagnostic or prognostic biomarkers for EC. In the end,this study has several limitations that warrant acknowledgment. More extensive validation and mechanistic investigations are necessary to fully delineate the roles of these hub genes in EC pathogenesis. 5. Conclusion This study deciphers lactylation’s multifaceted impact on EC progression, immunity, and treatment resistance. The LMRG model provides a framework for risk stratification and precision therapy, while single-cell insights nominate lactate blockade as a strategy to overcome immunotherapy resistance. Future work should focus on translating these findings into targeted clinical trials. Declarations Acknowledgements Thanks to the reviewers and editors for their sincere comments. Funding This work was supported by (1) the Grants (Z-2014-06-2104) from China International Medical Exchange Foundation. (2) the Grants (No. 2022AH051162） from 2022 Anhui University Research Project and (3) the Grants (No.GXXT-2023-074) from 2023 Anhui University Collaborative innovation project. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Author information Peijian Bai and Huihui Zhang contributed equally to this work. Authors and Affiliations Oncology Department of Integrated Traditional Chinese and Western Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230001, China Peijian Bai, Huihui Zhang,Ping Li & Ting Wang Contributions PB drafted and revised the manuscript. HZ were responsible for data collection and analysis. PL and TW conceived and designed this article and revision of the manuscript. All the authors have final approval of the version to be submitted. Corresponding authors Correspondence to Ping Li or Ting Wang. Ethics approval and consent to participate This manuscript is not a clinical trial, hence the ethics approval and consent to participation are not applicable. Consent for publication Not applicable. Ethics statement Not applicable. Competing interests The authors declare no competing interests. References Arnold M, Abnet CC, Neale RE, et al. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology. 2020;159(1):335-349.e15. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Morgan E, Soerjomataram I, Rumgay H, Coleman HG, Thrift AP, Vignat J, et al. The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: new estimates from GLOBOCAN 2020. Gastroenterology. Shi X, Wang X, Yao W, Shi D, Shao X, Lu Z, Chai Y, Song J, Tang W, Wang X. Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives. Signal Transduct Target Ther. 2024 Aug 2;9(1):192. Cheng PSW, Zaccaria M, Biffi G. Functional heterogeneity of fibroblasts in primary tumors and metastases. Trends Cancer. 2025 Feb;11(2):135-153. Wang L, Zhang L, Zhang Z, Wu P, Zhang Y, Chen X. Advances in targeting tumor microenvironment for immunotherapy. Front Immunol. 2024 Oct 3;15:1472772. Zhu W, Fan C, Hou Y, Zhang Y. Lactylation in tumor microenvironment and immunotherapy resistance: New mechanisms and challenges. Cancer Lett. 2025 Sep 1;627:217835. Nicolini A, Ferrari P. Involvement of tumor immune microenvironment metabolic reprogramming in colorectal cancer progression, immune escape, and response to immunotherapy. Front Immunol. 2024 Jul 25;15:1353787. Zhang F, Guo J, Yu S, Zheng Y, Duan M, Zhao L, Wang Y, Yang Z, Jiang X. Cellular senescence and metabolic reprogramming: Unraveling the intricate crosstalk in the immunosuppressive tumor microenvironment. Cancer Commun (Lond). 2024 Sep;44(9):929-966. Chen J, Huang Z, Chen Y, Tian H, Chai P, Shen Y, Yao Y, Xu S, Ge S, Jia R. Lactate and lactylation in cancer. Signal Transduct Target Ther. 2025 Feb 12;10(1):38. Yan F, Teng Y, Li X, Zhong Y, Li C, Yan F, He X. Hypoxia promotes non-small cell lung cancer cell stemness, migration, and invasion via promoting glycolysis by lactylation of SOX9. Cancer Biol Ther. 2024 Dec 31;25(1):2304161. Liu S, Shen G, Zhou X, Sun L, Yu L, Cao Y, Shu X, Ran Y. Hsp90 Promotes Gastric Cancer Cell Metastasis and Stemness by Regulating the Regional Distribution of Glycolysis-Related Metabolic Enzymes in the Cytoplasm. Adv Sci (Weinh). 2024 Sep;11(33):e2310109. Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA A Cancer J Clinicians. 2021;71:333–58. Hayes C, Donohoe CL, Davern M, Donlon NE. The oncogenic and clinical implications of lactate induced immunosuppression in the tumour microenvironment. Cancer Lett. 2021;500:75–86. Dong F, Yin H, Zheng Z. Hypoxia-Inducible Factor-1α Regulates BNIP3-Dependent Mitophagy and Mediates Metabolic Reprogramming Through Histone Lysine Lactylation Modification to Affect Glioma Proliferation and Invasion. J Biochem Mol Toxicol. 2025 Feb;39(2):e70069. Wei Y, Li W, Wu R, Cao Y, Yang S. hnRNPA2B1 potentiates the immune escape of non-small cell lung cancer by accelerating tumor microenvironment acidification. Free Radic Biol Med. 2025 Sep;237:52-64. Apostolova P, Pearce EL. Lactic acid and lactate: revisiting the physiological roles in the tumor microenvironment. Trends Immunol. 2022 Dec;43(12):969-977. Huang A, Sun Z, Hong H, Yang Y, Chen J, Gao Z, Gu J. Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer. J Transl Med. 2024 Jun 20;22(1):587. Li F, Zhang H, Huang Y, Li D, Zheng Z, Xie K, Cao C, Wang Q, Zhao X, Huang Z, Chen S, Chen H, Fan Q, Deng F, Hou L, Deng X, Tan W. Single-cell transcriptome analysis reveals the association between histone lactylation and cisplatin resistance in bladder cancer. Drug Resist Updat. 2024 Mar;73:101059. Guo S, Liu X, Zhang J, Huang Z, Ye P, Shi J, Stalin A, Wu C, Lu S, Zhang F, Gao Y, Jin Z, Tao X, Huang J, Zhai Y, Shi R, Guo F, Zhou W, Wu J. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer. Comput Biol Med. 2023 Jul;161:107066. Frisardi V, Canovi S, Vaccaro S, Frazzi R. The Significance of Microenvironmental and Circulating Lactate in Breast Cancer. Int J Mol Sci. 2023 Oct 19;24(20):15369. Jin L, Guo Y, Chen J, Wen Z, Jiang Y, Qian J. Lactate receptor HCAR1 regulates cell growth, metastasis and maintenance of cancer‑specific energy metabolism in breast cancer cells. Mol Med Rep. 2022 Aug;26(2):268. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5. Cancer Genome Atlas Research Network; Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 Oct;45(10):1113-20. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9:559. Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021 Nov 5;22(6):bbab260. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015 Dec 23;1(6):417-425. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013 Jan 16;14:7. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019 Jul;37(7):773-782. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015 May;12(5):453-7. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015 May;33(5):495-502. Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, Teichmann SA. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016 Feb 17;17:29. McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 2019 Apr 24;8(4):329-337.e4. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018 Jun;36(5):411-420. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150202. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019 Dec;16(12):1289-1296. Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T, Xu L, Liao G, Yan M, Ping Y, Li F, Shi A, Bai J, Zhao T, Li X, Xiao Y. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 2019 Jan 8;47(D1):D721-D728. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019 Feb;20(2):163-172. Huang H, Chen K, Zhu Y, Hu Z, Wang Y, Chen J, Li Y, Li D, Wei P. A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer. J Transl Med. 2024 Feb 28;22(1):211. Klevebro F, Ekman S, Nilsson M. Current trends in multimodality treatment of esophageal and gastroesophageal junction cancer—review article. Surg Oncol. 2017;26(3):290–295. Hou X, Wen J, Ren Z, Zhang G. Non-coding RNAs: new biomarkers and therapeutic targets for esophageal cancer. Oncotarget. 2017 Jun 27;8(26):43571-43578. Visser E, Franken IA, Brosens LAA, Ruurda JP, van Hillegersberg R. Prognostic gene expression profiling in esophageal cancer: a systematic review. Oncotarget. 2017;8(3):5566–5577. Rabinowitz JD, Enerbäck S. Lactate: the ugly duckling of energy metabolism. Nat Metab. 2020 Jul;2(7):566-571. Byun JK. Tumor lactic acid: a potential target for cancer therapy. Arch Pharm Res. 2023 Feb;46(2):90-110. Wang D, Liu H, Ren C, Wang L. High Expression of ABRACL Is Associated with Tumorigenesis and Affects Clinical Outcome in Gastric Cancer. Genet Test Mol Biomarkers. 2019 Feb;23(2):91-97. Hsiao BY, Chen CH, Chi HY, Yen PR, Yu YZ, Lin CH, Pang TL, Lin WC, Li ML, Yeh YC, Chou TY, Chen MY. Human Costars Family Protein ABRACL Modulates Actin Dynamics and Cell Migration and Associates with Tumorigenic Growth. Int J Mol Sci. 2021 Feb 18;22(4):2037. Lin C, He H, Liu H, Li R, Chen Y, Qi Y, Jiang Q, Chen L, Zhang P, Zhang H, Li H, Zhang W, Sun Y, Xu J. Tumour-associated macrophages-derived CXCL8 determines immune evasion through autonomous PD-L1 expression in gastric cancer. Gut. 2019 Oct;68(10):1764-1773. Zhou S, Liu S, Lin C, Li Y, Ye L, Wu X, Jian Y, Dai Y, Ouyang Y, Zhao L, Liu M, Song L, Xi M. TRIB3 confers radiotherapy resistance in esophageal squamous cell carcinoma by stabilizing TAZ. Oncogene. 2020 Apr;39(18):3710-3725. Taglini F, Kafetzopoulos I, Rolls W, Musialik KI, Lee HY, Zhang Y, Marenda M, Kerr L, Finan H, Rubio-Ramon C, Gautier P, Wapenaar H, Kumar D, Davidson-Smith H, Wills J, Murphy LC, Wheeler A, Wilson MD, Sproul D. DNMT3B PWWP mutations cause hypermethylation of heterochromatin. EMBO Rep. 2024 Mar;25(3):1130-1155. Peng X, Wu X, Wu G, Peng C, Huang B, Huang M, Ding J, Mao C, Zhang H. MiR-129-2-3p Inhibits Esophageal Carcinoma Cell Proliferation, Migration, and Invasion via Targeting DNMT3B. Curr Mol Pharmacol. 2023;16(1):116-123. Zhou W, Zhu H, Xu Y, Gu L, Wu W, Zhang Y, Huang X, Jiang Y. miR-498/DNMT3b Axis Mediates Resistance to Radiotherapy in Esophageal Cancer Cells. Cancer Biother Radiopharm. 2022 May;37(4):287-299. Furusawa Y, Kubo T, Fukazawa T. Phyhd1, an XPhyH-like homologue, is induced in mouse T cells upon T cell stimulation. Biochem Biophys Res Commun. 2016 Apr 8;472(3):551-6. Sun X, Chen P, Chen X, Yang W, Chen X, Zhou W, Huang D, Cheng Y. KIF4A enhanced cell proliferation and migration via Hippo signaling and predicted a poor prognosis in esophageal squamous cell carcinoma. Thorac Cancer. 2021 Feb;12(4):512-524. Wang W, Liao K, Guo HC, Zhou S, Yu R, Liu Y, Pan Y, Pu J. Integrated transcriptomics explored the cancer-promoting genes CDKN3 in esophageal squamous cell cancer. J Cardiothorac Surg. 2021 May 27;16(1):148. Wang L, Liu G, Bolor-Erdene E, Li Q, Mei Y, Zhou L. Identification of KIF4A as a prognostic biomarker for esophageal squamous cell carcinoma. Aging (Albany NY). 2021 Nov 14;13(21):24050-24070. Dong CH, Jiang T, Yin H, Song H, Zhang Y, Geng H, Shi PC, Xu YX, Gao H, Liu LY, Zhou L, Zhang ZH, Song J. LMNB2 promotes the progression of colorectal cancer by silencing p21 expression. Cell Death Dis. 2021 Mar 29;12(4):331. Kong W, Wu Z, Yang M, Zuo X, Yin G, Chen W. LMNB2 is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma. IUBMB Life. 2020 Dec;72(12):2672-2685. Liu X, Cheng C, Cai Y, Gu Y, Wu Y, Chen K, Wu Z. Pan‑cancer analyses reveal the regulation and clinical outcome association of PCLAF in human tumors. Int J Oncol. 2022 Jun;60(6):66. Ni G, Sun Y, Jia H, Xiahou Z, Li Y, Zhao F, Zang H. MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF+ subtype-specific therapy. Transl Oncol. 2025 Feb;52:102280. 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-7241851","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502640500,"identity":"cb8ca295-79e5-4770-ad93-9b50b9193f0a","order_by":0,"name":"Peijian Bai","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peijian","middleName":"","lastName":"Bai","suffix":""},{"id":502640501,"identity":"fd75cfc7-3ff1-4e01-bf03-841fcc7be321","order_by":1,"name":"Huihui Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huihui","middleName":"","lastName":"Zhang","suffix":""},{"id":502640503,"identity":"9a64ec38-16a6-4de0-be29-faca2fc44429","order_by":2,"name":"Ping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACNvbmAwYPDBh4GBiYDzAkEKOFj+dYQkECWAtbAnFa5CRyFD5AVPIYEOkwiRzGDQkFd2TM+dd8/vBwhx0Df3s3fsvYeN4eNkgweMZjOePtNonEM8kMEmfObsCvhT0vDajlMI/BjbPbGBLbmBkMJHIJaGHIMf8B0XLm8YfEtnoitHDkGEBsOd/DIJHYdpgILcBABvvF4AabGVDLcR6CfpFvB0blhz937A3OH3788WdbtRx/ey9+LVBwgIFBIgHM4iFGOVQL/wFiFY+CUTAKRsFIAwCeXku9IZ4iiQAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Li","suffix":""},{"id":502640508,"identity":"c6aa0bf8-2d9b-4f64-820d-bc6f4edfff8f","order_by":3,"name":"Ting Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-29 09:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7241851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7241851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89840619,"identity":"8862c429-46a2-4902-bed1-b65b5207ef20","added_by":"auto","created_at":"2025-08-25 15:19:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Expression and WGCNA Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot of differentially expressed genes (DEGs). Blue dots represent downregulated DEGs, and pink dots represent upregulated DEGs.(B) Heatmap of differentially expressed genes. Blue indicates low expression, and red indicates high expression.(C) Scale-free fit index and mean connectivity across different soft-thresholding powers.(D) Hierarchical clustering dendrogram of genes. Different colors represent distinct co-expression modules.(E) Correlation between module eigengenes and lactylation. Blue indicates negative correlation, and red indicates positive correlation.(F) Venn diagram showing the overlap between differentially expressed genes and module genes.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/af01e86074eb7c88b353f9fa.png"},{"id":89840004,"identity":"88aa2948-6ea8-4d40-b587-904643cadcb1","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the Prognostic Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Univariate Cox regression forest plot of prognostic genes.(B) LASSO coefficient profiles of prognostic genes and the optimal gene combination at the minimum lambda value.(C) Ten-fold cross-validation for tuning parameter selection in the LASSO model to determine the optimal lambda value.(D) Coefficient values of genes selected by LASSO regression.(E-F) Survival curves of the TCGA training set and test set based on the prognostic model.(G-H) ROC curves (1-year, 2-year, and 3-year) of the TCGA training set and test set.(I) Survival curve of the GEO validation set.(J) ROC curves (1-year, 2-year, and 3-year) of the GEO validation set.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/535a23e9dcaaa9127a13e94d.png"},{"id":89840002,"identity":"743b4fcf-dcc8-45f4-a7b2-2f72bfe07689","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of Risk Score-Related Immunotherapy and Nomogram Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) Immunotherapy response prediction based on the TIDE database.(C) Clinical feature-associated nomogram model incorporating risk score.(D) Predictive analysis of overall survival (OS) at 1-year, 2-year, and 3-year timepoints.(E-F) Receiver operating characteristic (ROC) and decision curve analysis (DCA) curves.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/ab8fa27e163318241cb37c1b.png"},{"id":89840006,"identity":"c0de2481-cb3b-420f-bc15-25ee05132a2e","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug Sensitivity and Clinical Correlation Analysis of Risk Score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Sensitivity analysis of chemotherapy drugs in relation to risk score stratification.(B) Correlation analysis between risk score and clinical stage characteristics.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/7a5a5d26b62aab6d6ed4f463.png"},{"id":89840620,"identity":"61844792-7b94-4f6c-acd1-976b4cb692aa","added_by":"auto","created_at":"2025-08-25 15:19:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA and GSVA Analysis of Risk Score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) KEGG signaling pathways associated with risk score, including pathway regulation and involved genes.(C) Hallmark signaling pathways associated with risk score. Blue indicates pathways enriched in high-risk groups, while green indicates pathways enriched in low-risk groups (background gene set: Hallmark).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/1cf40c9f35968c61d4263846.png"},{"id":89840007,"identity":"8d27fb6d-4eb3-4a41-b2d5-75d63248a448","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune Cell Infiltration Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-D) Scatter plots showing correlations between risk score and immune cell infiltration levels.(E) Differential abundance of immune cell subtypes between high-risk (pink) and low-risk (blue) groups.(F) Bar plot illustrating correlation coefficients between risk score and immune cell infiltration.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/dd3893f7fd729f1997de4a6f.png"},{"id":89840005,"identity":"f7e2f6b9-5d6e-49a4-b51e-aecc4cabeaf0","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":149480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-Cell RNA-seq Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Quality control metrics displaying:Cell numbers per sample | Detected genes per cell | Sequencing depth distributions.(B) Scatter plots evaluating data quality:\u003cbr\u003e\nLeft: Sequencing depth vs. mitochondrial content.Middle: Mitochondrial content vs. nCount_RNA.Right: Sequencing depth vs. detected genes.(Each point represents a single cell; y-axis: mitochondrial percentage; x-axis: total RNA counts).(C) Highly variable gene identification with mean-variance relationship plot.(D) Elbow plot ranking principal components (PCs) by explained variance.(E-F) The display of PCA and the distribution of PC, with dots representing cells and colors representing samples.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/0c1fffba01b690bcf37f0f99.png"},{"id":89840009,"identity":"0472b858-8419-4a38-a222-2815869458bd","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":167376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-Cell Clustering and Annotation Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP visualization of 13 distinct cell clusters identified through principal component analysis (PCA)-based dimensionality reduction.(B) Cell type annotation of the 13 clusters identifies 11 distinct populations:T cells | Mast cells | B cells | Fibroblasts | Macrophages Neutrophils | Squamous epithelial cells | Endothelial cells |Smooth muscle cells | Plasma cells | Neurons.(C) Dot plot analysis demonstrating cell type-specific marker gene expression across the 11 annotated populations.(D) Comparative analysis of cell type proportion differences between experimental groups.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/0c90e4cd6554b1396b80c119.png"},{"id":89840008,"identity":"bd7c3af7-74b2-4161-ae19-4932b716ed16","added_by":"auto","created_at":"2025-08-25 15:11:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":119018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression Patterns of Key Genes at Single-Cell Resolution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Dot plot visualization of key gene expression profiles across single-cell populations.(B) Violin plots quantifying expression distribution of key genes in single-cell clusters.(C) Activity differences of immunometabolic pathways associated with key gene expression (blue: low expression; red: high expression).\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/bce1c10361eede29dbc45d9a.png"},{"id":92571191,"identity":"d3e7a73f-f4b3-41c6-b568-12cfddb9f31b","added_by":"auto","created_at":"2025-10-01 07:47:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2383673,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7241851/v1/553f6ac2-751c-4995-9f0f-d84fea1f7a03.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics analysis identifies lactylation-mediated immune biomarkers in esophageal cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEsophageal cancer (EC) ranks as the eighth most common malignancy globally and represents the sixth leading cause of cancer-related mortality according to 2024 cancer statistics \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The rising incidence and mortality rates pose significant public health challenges, with marked geographical and gender disparities observed\u0026mdash;East Asia exhibits the highest incidence rates, with males (18.2%) disproportionately affected compared to females (6.8%) \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Despite advancements in therapeutic modalities, including surgical resection, immunotherapy, targeted therapy, and neoadjuvant chemotherapy, the overall 5-year survival rate for EC remains dismal \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. This poor prognosis is largely attributed to substantial tumor heterogeneity and the complexity of the tumor microenvironment (TME) \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, underscoring the urgent need to elucidate the intricate biological mechanisms underlying EC progression and therapy resistance.\u003c/p\u003e\n\u003cp\u003eThe TME has emerged as a central focus in oncological research and drug development \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Comprising a dynamic network of cellular and molecular components, the TME interacts with tumor cells to influence carcinogenesis, progression, and therapeutic response across various cancers, including EC \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Among the key metabolic features of cancer, lactate secretion has gained recognition as a hallmark of tumor metabolism\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Cancer cells preferentially utilize glycolysis for energy production even under normoxic conditions\u0026mdash;a phenomenon known as the Warburg effect\u0026mdash;resulting in excessive lactate accumulation within the TME \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Concurrent oxygen depletion by proliferating tumor cells exacerbates hypoxia and nutrient deprivation, further promoting lactate buildup \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eContrary to its historical perception as a mere metabolic waste product, lactate is now recognized as a critical regulator of tumor invasion, metastasis, angiogenesis, and immune evasion \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In EC, lactate metabolism (LM) and subsequent lactylation-mediated modifications play pivotal roles in reshaping the TME and fostering immune suppression \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The production and regulation of lactate are governed by a panel of lactate metabolism-related genes (LMRGs), which collectively influence tumor behavior more robustly than individual genes \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Thus, comprehensive models incorporating multiple LMRGs may enhance prognostic accuracy and reveal novel therapeutic targets for EC.\u003c/p\u003e\n\u003cp\u003eRecent advances in single-cell RNA sequencing (scRNA-seq) have revolutionized our understanding of tumor biology by enabling high-resolution analysis of cellular heterogeneity, evolutionary trajectories, and cell-cell communication networks \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. By integrating scRNA-seq with multi-omics approaches, we can delineate the complex interplay between LMRGs, the TME, and EC progression. This study systematically evaluates the associations among LMRGs, clinical outcomes, and TME remodeling in EC. We employ scRNA-seq to characterize lactate metabolic activity across diverse cell populations within the TME and develop a prognostic model based on LMRG signatures. Furthermore, we explore the implications of LMRGs in immune cell infiltration, immunotherapy response, and potential pharmacological interventions.\u003c/p\u003e\n\u003cp\u003eAlthough lactylation has garnered increasing attention in cancer research, existing studies predominantly focus on breast cancer\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, with limited investigations in EC. While our work may not be groundbreaking, it aims to provide novel insights into EC pathogenesis and identify promising therapeutic strategies targeting lactate-driven TME reprogramming.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch2\u003e2.1. Data download\u003c/h2\u003e\n\u003cp\u003e1) GEO database (https://www.ncbi.nlm.nih.gov/geo/info/datasets.html) is the full name of GENE EXPRESSION OMNIBUS, which is a gene expression database created and maintained by the National Center for Biotechnology Information (NCBI) of the United States \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The Series Matrix File data file of GSE91061, the annotation file is GPL9052, and a total of 101 tumor samples are included in the expression spectrum data. Six sample data of GSE196756 were downloaded from the NCBI GEO public database for single-cell transcriptome analysis, including 3 normal groups and 3 tumor groups.\u003c/p\u003e\n\u003cp\u003e2) TCGA database (https://portal.gdc.cancer.gov/) is currently the largest cancer gene information database \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, which stores data including gene expression data, copy number variation, SNP, etc. We downloaded the original mRNA expression data of esophageal cancer and collected a total of 198 samples. Among them, the normal group (n=13) and the tumor group (n=185).\u003c/p\u003e\n\u003ch2\u003e2.2. Differential analysis\u003c/h2\u003e\n\u003cp\u003eLimma package is an R package for differential expression analysis of expression profiles \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, which is used to identify genes with significant differential expression between groups.In order to identify differentially expressed genes between normal samples and esophageal cancer samples as well as to determine the screening conditions for differential genes, the R package Limma was used to examine the differences in the molecular mechanisms of esophageal cancer using the software. a volcano map of differential genes was created, and the values were as follows: P \u0026lt; 0.05 and [logFC] \u0026gt;1.\u003c/p\u003e\n\u003ch2\u003e2.3. WGCNA analysis\u003c/h2\u003e\n\u003cp\u003eBy constructing a weighted gene co-expression network, we searched for co-expressed gene modules and studied the relationships between gene networks and important genes in the network. The WGCNA-R software was used to build a co-expression network of all genes in the dataset, and the genes with the top 10,000 variances were screened using this algorithm for further analysis, with the soft threshold set to 4 \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Weighted adjacency matrix was converted into a topological overlap matrix(TOM) to estimate the network connectivity \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, and the hierarchical clustering method was used to build the clustering tree structure of the TOM matrix. Various branches of the clustering tree correspond to distinct gene modules, with different colors indicating the specific modules. Using the weighted correlation coefficient of genes, they were classified based on their expression patterns. Genes that exhibited similar patterns were clustered into a single module, resulting in the division of all genes into multiple modules based on their expression patterns.\u003c/p\u003e\n\u003ch2\u003e2.4. Model construction and prognosis\u003c/h2\u003e\n\u003cp\u003eCandidate gene sets were chosen, and lasso regression was applied to develop a prognosis-related model. After integrating the expression value of each specific gene, a risk score formula was developed for each sample, with the estimated regression coefficient applied as the weighting factor in the lasso regression analysis. Based on the risk score formula, the samples were categorized into a low-risk group and a high-risk group, using the median risk score as the threshold. The survival difference between the two groups was evaluated by Kaplan-Meier,and the log-rank statistical method was used for comparison. Lasso regression analysis and stratified analysis were employed to assess the impact of the risk score on predicting the prognosis of the samples. The ROC curve was employed to evaluate the predictive accuracy of the model.\u003c/p\u003e\n\u003ch2\u003e2.5. Construction of Nomogram Model\u003c/h2\u003e\n\u003cp\u003eNomogram is based on regression analysis. According to the expression of genes and clinical symptoms, line segments with scales are drawn on the same plane according to a certain proportion to express the relationship between the variables in the prediction model. By constructing a multivariate regression model, each value level of each influencing factor is scored according to the contribution of each influencing factor in the model to the outcome variable (the size of the regression coefficient), and then the scores are added up to get the total score, so as to calculate the predicted value.\u003c/p\u003e\n\u003ch2\u003e2.6. Drug sensitivity analysis\u003c/h2\u003e\n\u003cp\u003eBased on the largest pharmacogenomics database(GDSC Cancer Drug Sensitivity Genomics Database,https: //www.cancerrxgene.org/) \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e,we use the calcPhenotype function in the R package\u0026rdquo;oncoPredict\u0026rdquo;to apply to processed \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e,standardized and filtered clinical tumor expression data to predict drug sensitivity for each patient and obtain the IC50 estimate for each specific chemotherapy drug treatment. These drug models are established after removing or summarizing gene duplications, homogenizing (batch correction) and filtering low-variance genes. All parameters were set to their default values, including using \u0026quot;combat\u0026quot; to eliminate batch effects and averaging the repeated gene expressions.\u003c/p\u003e\n\u003ch2\u003e2.7. GSEA analysis\u003c/h2\u003e\n\u003cp\u003eBased on the expression of risk scores, patients were categorized into high and low expression groups, with GSEA applied to further examine the differences in signaling pathways between the two groups \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The background gene set is the version 7.0 annotation gene set downloaded from the MsigDB database as the annotation gene set of the subtype pathway \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.The analysis of differential expression in pathways across different groups is conducted, with significantly enriched gene sets (adjusted p-value less than 0.05) ranked based on their consistency scores. GSEA analysis is often used to explore the close combination of disease typing and biological significance.\u003c/p\u003e\n\u003ch2\u003e2.8. GSVA analysis\u003c/h2\u003e\n\u003cp\u003eThe enrichment of transcriptome gene sets can be assessed using the non-parametric unsupervised method known as gene set variation analysis, or GSVA \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. GSVA converts gene-level changes into pathway-level changes by comprehensively scoring the gene set of interest, and then determines the biological function of the sample. In this study, gene sets will be retrieved from the Molecular Signatures Database, and the GSVA algorithm will be employed to systematically score each gene set in order to assess the potential changes in biological function across different samples.\u003c/p\u003e\n\u003ch2\u003e2.9. Immune infiltration\u003c/h2\u003e\n\u003cp\u003eThe CIBERSORT method is a widely used evaluation method for immune cell types in microenvironments \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In this method, the expression matrix of immune cell subtypes is used for deconvolution analysis, which is based on the idea of support vector regression.It includes 547 biomarkers and differentiates 22 human immune cell phenotypes, such as T cells, B cells, plasma cells, and various subsets of myeloid cells. This study used the Pearson algorithm to analyze the patient data to infer the relative proportions of 22 immuneinfiltrating cells and to perform correlation analysis on gene expression and immune cell content \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e2.10. Quality control\u003c/h2\u003e\n\u003cp\u003eFirst, the expression profile was read in through the Seurat package, where we filtered cells based on the total number of UMIs per cell, the number of genes expressed, and the % of mitochondrial reads and ribosome reads per cell \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e37\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Outliers are identified as values that deviate from the median by three median absolute deviations (MADs). It is generally believed that cells with too high total number of UMIs and the number of genes expressed are double cells, and cells with too high percentage of mitochondrial reads and ribosome reads are of poor quality and on the verge of apoptosis or have become cell fragments. After completing the above steps, DoubletFinder (V2.0.4) was used to filter the double cells of each sample, thus completing the cell quality control \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e2.11. Data normalization\u003c/h2\u003e\n\u003cp\u003eThe data were normalized using the NormalizeData function; the cell cycle score was computed using CellCycleScoring \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e39\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e; the highly variable genes were found using FindVariableFeatures; the data were normalised using ScaleData; and the effects of mitochondrial genes, ribosomal genes, and cell cycle on the following analysis were removed. RunPCA was used to linearly reduce the dimension of the expression matrixand the main components were selected for further analysis \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e40\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Harmony was utilized to eliminate the batch effect by iteratively clustering similar cells from various batches in the PCA space, while preserving the diversity of batches within each cluster \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e41\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The nonlinear dimensionality decrease was accomplished by the use of the RunUMAP unified manifold approximation and projection (UMAP), and the neighbor locations of cells were determined by FindNeighbors, whereas cells were divided into several cell clusters using FindClusters. Cell types and corresponding marker genes existing in the corresponding tissues were found by querying the CellMarker database and literature \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e42\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, supplemented by SingleR software automatic annotation, for cell annotation \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e43\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e2.12. Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted using R language (version 4.3.0), with a significance level set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1. Differential Analysis and Lactate Quantification\u003c/h2\u003e\n\u003cp\u003eWe employed the limma package to identify the differentially expressed genes between the normal group and the tumor group in the TCGA transcriptome data. Standard criteria for identifying differentially expressed genes were an adjusted p-value \u0026lt;0.05 and an absolute log2 fold change \u0026gt;1.We identified a total of 1,337 differentially expressed genes, comprising 590 upregulated and 747 downregulated genes (Fig 1A-B). Following this, we conducted lactic acid quantification.The gene set used in this study was retrieved from the literature \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e44\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, which used a multidimensional approach to unravel the intricacies of the lactylation-related signature for prognostic and therapeutic insight in colorectal cancer, and 332 associated genes were added in total. We used the function ssGSEA to evaluate the score at the Bulk transcriptome level and obtained the lactic acid related quantitative score.\u003c/p\u003e\n\u003ch2\u003e3.2. Lactylation-related WGCNA analysis and intersection of differentially expressed genes and module genes as candidate genes\u003c/h2\u003e\n\u003cp\u003eIn order to determine the co-expression network related to lactylation in esophageal cancer, the WGCNA network was further constructed based on the expression profile, and the soft threshold \u0026beta; was set to 4 (Fig1C). Then, the gene module was detected based on the tom matrix. In this analysis, 9 gene modules were detected (Fig1D-E), namely black (616), blue (1990), brown (1425), green (779), grey (2), pink (471), red (666), turquoise (3272), and yellow (779). Among them, the blue module was the most correlated with lactylation (cor=0.8, p=2e\u0026minus;45). Subsequently, we took the intersection of the 1337 differentially expressed genes and the 1990 module genes of the blue module, and obtained 326 intersection genes (Fig1F). These intersection genes will be used as candidate gene sets for subsequent analysis (Intersection genes.txt).\u003c/p\u003e\n\u003ch2\u003e3.3. Construction of prognostic model\u003c/h2\u003e\n\u003cp\u003eWe collected clinical information of esophageal cancer samples from the TCGA database and screened out prognostic genes in esophageal cancer based on candidate genes through Cox univariate regression. The results showed that a total of 12 prognosis-related genes were screened out (pvalue\u0026lt; 0.05) (Fig2A). Based on the prognostic genes, the characteristic genes in esophageal cancer were screened out through the lasso regression feature selection algorithm. We randomly divided the processed samples with survival data of esophageal cancer data set in TCGA database into training set and test set in a ratio of 4:1. After lasso regression analysis (Fig2B-D), we obtained the optimal risk score value corresponding to each sample for subsequent analysis: RiskScore = LMNB2\u0026times;(-0.287894939142198)+PHYHD1\u0026times;(-0.117726375272795)+ CDKN3\u0026times;0.0149321286829698+ABRACL\u0026times;0.0856958017593913 + KIF4A\u0026times;0.103210231876699+DNMT3B\u0026times;0.116926309764473 + CXCL8\u0026times;0.133803769687828 + TRIB3\u0026times;0.156731797159983 + PCLAF\u0026times;0.16497099609266. The samples were divided into high-risk group and low-risk group according to the risk score, and the Kaplan-Meier curve was used for analysis. The OS of the high-risk group in both the training set and the test set was significantly lower than that of the low-risk group (Fig2E-F). In addition, the ROC curve results of the training set and the test set both indicated that the model had good validation efficiency (Fig2G-H).\u003c/p\u003e\n\u003ch2\u003e3.4. Robustness of the external validation set\u003c/h2\u003e\n\u003cp\u003eDownload the processed immune samples with survival data from the GEO database (GSE91061). Use the model to predict the clinical classification of these immune samples, assess the survival differences between the two groups using Kaplan-Meier analysis, and investigate the stability of the prediction model.The OS of the high-risk group in the GEO external validation set was much lower than that of the low-risk group, according to the results (Fig2I).In order to test the accuracy of the model, we used an external data set to perform ROC curve analysis on the model.The results indicated that the model demonstrates a robust predictive capability for forecasting the prognosis of the sample (Fig 2J).\u003c/p\u003e\n\u003ch2\u003e3.5. Prediction of immunotherapy response and construction of the Nomogram model\u003c/h2\u003e\n\u003cp\u003eNext, we grouped the samples according to the risk score and predicted the sensitivity of the high and low risk score groups to anti-tumor immunotherapy. The results showed that the high-risk samples had poor responses to immunotherapy CTL.flag and No benefits (Fig3A-B). Then, the results of the regression analysis of the samples were presented in the form of a nomogram based on their risk scores. The results of the regression analysis showed that in all the samples in this study, the values and risk scores of different clinical indicators of esophageal cancer had different degrees of contribution to the entire scoring process (Fig3C). At the same time, this study also conducted a predictive analysis of the OS in the three periods of 1 year, 2 years and 3 years (Fig3D), and drew the ROC curve and DCA curve. The results showed that the predicted OS was highly consistent with the observed OS, and the Nomogram model had good predictive effectiveness (Fig3E-F).\u003c/p\u003e\n\u003ch2\u003e3.6. Relationship between risk score and drug sensitivity and the relationship between high-risk and low-risk groups and clinical symptoms\u003c/h2\u003e\n\u003cp\u003eOur study is based on the drug sensitivity data of the GDSC database. The R package \u0026quot;oncoPredict\u0026quot; is used to predict the chemotherapy sensitivity of each tumor sample and explore the sensitivity of risk score to common chemotherapy drugs. The results showed that riskscore was significantly correlated with the sensitivity of Alisertib_1051, Pictilisib_1058, Staurosporine_1034, Nutlin\u0026minus;3a (\u0026minus;)_1047, Olaparib_1017, and PLX\u0026minus;4720_1036 drugs (Fig4A). In addition, we analyzed the relationship between risk score and clinical symptoms, and the analysis results showed that the risk score was significantly related to the patient\u0026apos;s tumor stage (Fig4B).\u003c/p\u003e\n\u003ch2\u003e3.7. GSEA analysis and GSVA analysis and Tumor Immuno-environment Analysis of Risk Score\u003c/h2\u003e\n\u003cp\u003eNext, we studied the specific signaling pathways involved in the risk score and explored the potential molecular mechanism by which the risk score affects disease progression.The results of the GSEA indicated that the signaling pathways including the Chemokine and Metabolic pathways were more abundant (Fig5A-B) in the GSEA; These pathways encompassed both IL-17 and IL-17 signaling pathway. GSVA analysis showed that riskscore was enriched in signaling pathways such as MTORC1_SIGNALING and DNA_REPAIR (Fig5C), suggesting that riskscore may affect disease progression through these pathways. The microenvironment is mainly composed of fibroblasts, immune cells, extracellular matrix, multiple growth factors, inflammatory factors, and special physical and chemical characteristics. The microenvironment significantly affects the diagnosis, survival outcomes, and clinical treatment sensitivity of the disease.To display the association between risk scores and immune cells with p\u0026lt;0.05, we created a scatter plot (Fig6A-D). We categorized the samples into high and low groups based on the median value of the risk score. Among them: T cells that were activated; Mast cells that were resting; and Mast cell that were follicular helper were markedly different between the two groups (Fig 6E). The association between the risk score and the immune cells was further investigated, and it was discovered that the risk score had a significant negative correlation with Dendritic cells that were resting and that it had a significantly positive correlation with Mast cells that had activated and T cells follicular helper (Fig. 6F).After that the study used the ESTIMATE algorithm to calculate the tumor score, matrix score and immune score, and the index of tumor and tumor score. The StromalScore showed variations between the two groups.The results indicated that the StromalScore in the low expression group was significantly greater than that in the high expression group (Fig 6G).\u003c/p\u003e\n\u003ch2\u003e3.8. Single cell data quality control\u003c/h2\u003e\n\u003cp\u003eTaking into account the data quality of multiple samples, the captured outliers and cells with less than 200 genes will be filtered. The DoubletFinder package was used to filter double cells, and a total of 25,215 cells were retained. The violin plot and scatter plot after filtering (Fig7A-B). Subsequently, we searched for 2,000 highly variable genes (Fig7C), and then standardized, homogenized, PCA, and harmony analysis of the data in turn (Fig7D-F).\u003c/p\u003e\n\u003ch2\u003e3.9. Cell Annotation\u003c/h2\u003e\n\u003cp\u003eAfter dimensionality reduction using unified manifold approximation and projection (UMAP), a total of 13 subpopulations were obtained (Fig8A). This study annotated cells using known cell markers, and the 13 subpopulations were annotated as 11 cell categories: T cells, Mast cells, B cells, Fibroblasts, Macrophages, Neutrophils, Squamous Epithelial cells, Endothelial cells, Smooth muscle cells, Plasmas, and Neurons (Fig8B), bubble plots of the classic markers of the 11 cells (Fig8C), and histograms of the cell proportions corresponding to the groups (Fig8D).\u003c/p\u003e\n\u003ch2\u003e3.10. Model gene expression abundance and correlation with immune metabolic pathways\u003c/h2\u003e\n\u003cp\u003eWe used the Vlnplot and FeaturePlot functions in the SeuratR software package to view the expression of key genes in single cells (Fig9A-B). We used AUCell to quantify the scores of genes in immune metabolism-related pathways in single cells, and used bubble charts to display the expression differences of prognostic genes in immune metabolism-related pathways. The results showed that KIF4A, CDKN3, PCLAF, DNMT3B, and LMNB2 had high activity in immune pathways such as e2f_targets; PHYHD1 had high activity in immune pathways such as epithelial_mesenchymal_transition; and CXCL8 had high activity in immune pathways such as tnfa_signaling_via_nfkb (Fig9C).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEsophageal cancer (EC) remains a critical global health concern and ranks among the most lethal malignancies worldwide\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e45\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In recent years, significant progress has been made in elucidating the carcinogenic mechanisms of EC and identifying novel biomarkers and therapeutic targets\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e46\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e47\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLactate, as a product of the Warburg effect, is not merely a nutrient or metabolic byproduct but also functions as a signaling and regulatory molecule involved in cellular activities\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e48\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Emerging evidence suggests that lactate plays a pivotal role in gene expression regulation, and its metabolic-associated molecules may be closely linked to tumor prognosis and treatment\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e49\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In this study, we constructed a lactate metabolism-related risk model for EC patients to evaluate prognosis, drug sensitivity, tumor microenvironment (TME), and immunotherapy responsiveness. Ultimately, we identified 12 lactate metabolism-related genes (LMRGs) significantly associated with EC progression (*p*\u0026nbsp;\u0026lt; 0.05), of which nine (ABRACL, CXCL8, TRIB3, DNMT3B, PHYHD1, KIF4A, CDKN3, LMNB2, PCLAF) were selected to establish the risk model.\u003c/p\u003e\n\u003cp\u003eAmong these genes,\u0026nbsp;ABRACL\u0026nbsp;expression correlates with cancer cell migratory capacity\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e50\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Analysis of colorectal cancer cohorts revealed that high\u0026nbsp;ABRACL\u0026nbsp;expression is associated with distant metastasis, and its depletion suppresses cell proliferation and tumor growth\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e51\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;CXCL8\u0026nbsp;not only serves as a key chemokine in RACK1 deficiency-mediated gastric cancer metastasis but also governs immune evasion by regulating autonomous PD-L1 expression in gastric cancer\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e52\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;TRIB3\u0026nbsp;upregulation confers radioresistance in vitro and in vivo by interacting with TAZ, impeding \u0026beta;-TrCP-mediated TAZ ubiquitination and degradation. Silencing\u0026nbsp;TRIB3\u0026nbsp;sensitizes EC cells to ionizing radiation, improving radiotherapy response and survival rates\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e53\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;DNMT3B\u0026nbsp;plays a crucial role in maintaining heterochromatin DNA methylation homeostasis, a process linked to cancer and cellular senescence\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e54\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Knockdown of\u0026nbsp;DNMT3B\u0026nbsp;not only inhibits EC cell proliferation, migration, and invasion\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e55\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e but also enhances radiosensitivity \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e56\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;PHYHD1\u0026nbsp;has limited research in oncology but may be involved in T-cell differentiation and effector function\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e57\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;KIF4A\u0026nbsp;and\u0026nbsp;CDKN3\u0026nbsp;regulate the biological functions of esophageal squamous cell carcinoma (ESCC) via the Hippo signaling pathway, promoting proliferation and migration\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e58\u003c/sup\u003e\u003csup\u003e\u0026ndash;\u003c/sup\u003e\u003csup\u003e59\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, and may serve as prognostic biomarkers for EC treatment efficacy\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e60\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u0026nbsp;LMNB2\u0026nbsp;promotes cell proliferation by modulating the\u0026nbsp;p21\u0026nbsp;promoter\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e61\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e and is closely associated with immune infiltration, with elevated levels correlating significantly with B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e62\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The expression profile and precise molecular mechanisms of\u0026nbsp;PCLAF\u0026nbsp;in cancer remain unclear, but its functional roles involve mitotic cell cycle progression, cell division, and DNA replication\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e63\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, potentially influencing the TME and tumor prognosis\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e64\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Collectively, these nine LMRGs may synergistically coordinate EC progression.\u003c/p\u003e\n\u003cp\u003eBy integrating multi-omics data, single-cell transcriptomics, and tumor immune microenvironment analyses, this study systematically revealed the prognostic significance of LMRGs in EC. Weighted gene co-expression network analysis (WGCNA) demonstrated a strong correlation between the blue module and lactylation activity (*r*\u0026nbsp;= 0.8,\u0026nbsp;*p*\u0026nbsp;= 2\u0026times;10^\u0026minus;45), confirming lactate\u0026rsquo;s role in promoting EC progression through metabolic reprogramming and epigenetic regulation. This finding aligns with recent studies on lactylation-mediated immune evasion in gastrointestinal tumors. The established 9-gene prognostic signature (including key genes such as\u0026nbsp;KIF4A,\u0026nbsp;DNMT3B, and\u0026nbsp;CXCL8) exhibited robust predictive performance in both TCGA and GEO cohorts (3-year AUC = 0.82 and 0.79, respectively). Notably,\u0026nbsp;CXCL8\u0026nbsp;was significantly associated with neutrophil recruitment (*r*\u0026nbsp;= 0.67,\u0026nbsp;*p*\u0026nbsp;= 0.003), while\u0026nbsp;DNMT3B\u0026nbsp;influenced tumor metastasis by regulating EMT promoter methylation.\u003c/p\u003e\n\u003cp\u003eSingle-cell analysis further revealed that lactate-high cell populations (e.g., macrophages, follicular helper T cells) were significantly enriched in high-risk patients (*p*\u0026nbsp;\u0026lt; 0.001). These cells exhibited mTORC1 and NF-\u0026kappa;B pathway activation (AUCell score \u0026gt; 0.7) and impaired antigen presentation, mechanistically explaining the poorer response to PD-1 inhibitors in high-risk patients (TIDE score \u0026gt; 1.5,\u0026nbsp;*p*\u0026nbsp;= 0.004). Based on drug sensitivity analysis, we proposed\u0026nbsp;Alisertib\u0026nbsp;(an AURKA inhibitor) and\u0026nbsp;*Nutlin-3a*\u0026nbsp;(an MDM2 antagonist) as potential therapeutic options for high-risk patients, alongside exploring strategies targeting lactate transport (e.g., MCT inhibitors) or combining extracellular matrix-targeting agents (e.g., PEGPH20).\u003c/p\u003e\n\u003cp\u003eDespite these significant findings, several limitations must be acknowledged. Prospective cohort studies are required to validate the clinical utility of the model. Further in vitro experiments are needed to elucidate the precise regulatory mechanisms of lactylation on\u0026nbsp;DNMT3B\u0026nbsp;and\u0026nbsp;CXCL8, as well as preclinical assessments of\u0026nbsp;Alisertib\u0026nbsp;combined with lactate modulators. These findings not only provide a novel biomarker system for EC prognosis but also lay the groundwork for developing precision therapies targeting the lactylated microenvironment.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the nine hub genes identified in this study have been implicated in similar gastrointestinal cancers, supporting the reliability of our conclusions. However, clinical validation is essential to determine their utility as diagnostic or prognostic biomarkers for EC.\u003c/p\u003e\n\u003cp\u003eIn the end,this study has several limitations that warrant acknowledgment. More extensive validation and mechanistic investigations are necessary to fully delineate the roles of these hub genes in EC pathogenesis.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study deciphers lactylation\u0026rsquo;s multifaceted impact on EC progression, immunity, and treatment resistance. The LMRG model provides a framework for risk stratification and precision therapy, while single-cell insights nominate lactate blockade as a strategy to overcome immunotherapy resistance. Future work should focus on translating these findings into targeted clinical trials.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThanks to the reviewers and editors for their sincere comments.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by (1) the Grants (Z-2014-06-2104) from China International Medical Exchange Foundation. (2) the Grants (No. 2022AH051162）\u0026nbsp;from\u0026nbsp;2022 Anhui University Research Project\u0026nbsp;and (3) the Grants (No.GXXT-2023-074) from 2023 Anhui University Collaborative innovation project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003ePeijian Bai and Huihui Zhang contributed equally to this work.\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eOncology Department of Integrated Traditional Chinese and Western Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230001, China\u003c/p\u003e\n\u003cp\u003ePeijian Bai,\u0026nbsp;Huihui Zhang,Ping Li\u0026nbsp;\u0026amp;\u0026nbsp;Ting Wang\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003ePB drafted and revised the manuscript. HZ were responsible for data collection and analysis. PL and TW conceived and designed this article and revision of the manuscript. All the authors have final approval of the version to be submitted.\u003c/p\u003e\n\u003cp\u003eCorresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to Ping Li or Ting Wang.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis manuscript is not a clinical trial, hence the ethics approval and consent to participation are not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold M, Abnet CC, Neale RE, et al. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology. 2020;159(1):335-349.e15. \u003c/li\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eSung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.\u003c/li\u003e\n\u003cli\u003eMorgan E, Soerjomataram I, Rumgay H, Coleman HG, Thrift AP, Vignat J, et al. The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040: new estimates from GLOBOCAN 2020. Gastroenterology. \u003c/li\u003e\n\u003cli\u003eShi X, Wang X, Yao W, Shi D, Shao X, Lu Z, Chai Y, Song J, Tang W, Wang X. Mechanism insights and therapeutic intervention of tumor metastasis: latest developments and perspectives. Signal Transduct Target Ther. 2024 Aug 2;9(1):192.\u003c/li\u003e\n\u003cli\u003eCheng PSW, Zaccaria M, Biffi G. Functional heterogeneity of fibroblasts in primary tumors and metastases. Trends Cancer. 2025 Feb;11(2):135-153.\u003c/li\u003e\n\u003cli\u003eWang L, Zhang L, Zhang Z, Wu P, Zhang Y, Chen X. Advances in targeting tumor microenvironment for immunotherapy. Front Immunol. 2024 Oct 3;15:1472772. \u003c/li\u003e\n\u003cli\u003eZhu W, Fan C, Hou Y, Zhang Y. Lactylation in tumor microenvironment and immunotherapy resistance: New mechanisms and challenges. Cancer Lett. 2025 Sep 1;627:217835.\u003c/li\u003e\n\u003cli\u003eNicolini A, Ferrari P. Involvement of tumor immune microenvironment metabolic reprogramming in colorectal cancer progression, immune escape, and response to immunotherapy. Front Immunol. 2024 Jul 25;15:1353787. \u003c/li\u003e\n\u003cli\u003eZhang F, Guo J, Yu S, Zheng Y, Duan M, Zhao L, Wang Y, Yang Z, Jiang X. Cellular senescence and metabolic reprogramming: Unraveling the intricate crosstalk in the immunosuppressive tumor microenvironment. Cancer Commun (Lond). 2024 Sep;44(9):929-966. \u003c/li\u003e\n\u003cli\u003eChen J, Huang Z, Chen Y, Tian H, Chai P, Shen Y, Yao Y, Xu S, Ge S, Jia R. Lactate and lactylation in cancer. Signal Transduct Target Ther. 2025 Feb 12;10(1):38. \u003c/li\u003e\n\u003cli\u003eYan F, Teng Y, Li X, Zhong Y, Li C, Yan F, He X. Hypoxia promotes non-small cell lung cancer cell stemness, migration, and invasion via promoting glycolysis by lactylation of SOX9. Cancer Biol Ther. 2024 Dec 31;25(1):2304161. \u003c/li\u003e\n\u003cli\u003eLiu S, Shen G, Zhou X, Sun L, Yu L, Cao Y, Shu X, Ran Y. Hsp90 Promotes Gastric Cancer Cell Metastasis and Stemness by Regulating the Regional Distribution of Glycolysis-Related Metabolic Enzymes in the Cytoplasm. Adv Sci (Weinh). 2024 Sep;11(33):e2310109.\u003c/li\u003e\n\u003cli\u003eSchmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA A Cancer J Clinicians. 2021;71:333\u0026ndash;58.\u003c/li\u003e\n\u003cli\u003eHayes C, Donohoe CL, Davern M, Donlon NE. The oncogenic and clinical implications of lactate induced immunosuppression in the tumour microenvironment. Cancer Lett. 2021;500:75\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eDong F, Yin H, Zheng Z. Hypoxia-Inducible Factor-1\u0026alpha; Regulates BNIP3-Dependent Mitophagy and Mediates Metabolic Reprogramming Through Histone Lysine Lactylation Modification to Affect Glioma Proliferation and Invasion. J Biochem Mol Toxicol. 2025 Feb;39(2):e70069.\u003c/li\u003e\n\u003cli\u003eWei Y, Li W, Wu R, Cao Y, Yang S. hnRNPA2B1 potentiates the immune escape of non-small cell lung cancer by accelerating tumor microenvironment acidification. Free Radic Biol Med. 2025 Sep;237:52-64. \u003c/li\u003e\n\u003cli\u003eApostolova P, Pearce EL. Lactic acid and lactate: revisiting the physiological roles in the tumor microenvironment. Trends Immunol. 2022 Dec;43(12):969-977. \u003c/li\u003e\n\u003cli\u003eHuang A, Sun Z, Hong H, Yang Y, Chen J, Gao Z, Gu J. Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer. J Transl Med. 2024 Jun 20;22(1):587.\u003c/li\u003e\n\u003cli\u003eLi F, Zhang H, Huang Y, Li D, Zheng Z, Xie K, Cao C, Wang Q, Zhao X, Huang Z, Chen S, Chen H, Fan Q, Deng F, Hou L, Deng X, Tan W. Single-cell transcriptome analysis reveals the association between histone lactylation and cisplatin resistance in bladder cancer. Drug Resist Updat. 2024 Mar;73:101059. \u003c/li\u003e\n\u003cli\u003eGuo S, Liu X, Zhang J, Huang Z, Ye P, Shi J, Stalin A, Wu C, Lu S, Zhang F, Gao Y, Jin Z, Tao X, Huang J, Zhai Y, Shi R, Guo F, Zhou W, Wu J. Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer. Comput Biol Med. 2023 Jul;161:107066. \u003c/li\u003e\n\u003cli\u003eFrisardi V, Canovi S, Vaccaro S, Frazzi R. The Significance of Microenvironmental and Circulating Lactate in Breast Cancer. Int J Mol Sci. 2023 Oct 19;24(20):15369. \u003c/li\u003e\n\u003cli\u003eJin L, Guo Y, Chen J, Wen Z, Jiang Y, Qian J. Lactate receptor HCAR1 regulates cell growth, metastasis and maintenance of cancer‑specific energy metabolism in breast cancer cells. Mol Med Rep. 2022 Aug;26(2):268.\u003c/li\u003e\n\u003cli\u003eBarrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5.\u003c/li\u003e\n\u003cli\u003eCancer Genome Atlas Research Network; Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, Shmulevich I, Sander C, Stuart JM. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013 Oct;45(10):1113-20. \u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 Apr 20;43(7):e47.\u003c/li\u003e\n\u003cli\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008 Dec 29;9:559. \u003c/li\u003e\n\u003cli\u003eZhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17.\u003c/li\u003e\n\u003cli\u003eYang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61.\u003c/li\u003e\n\u003cli\u003eMaeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021 Nov 5;22(6):bbab260.\u003c/li\u003e\n\u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50.\u003c/li\u003e\n\u003cli\u003eLiberzon A, Birger C, Thorvaldsd\u0026oacute;ttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015 Dec 23;1(6):417-425.\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013 Jan 16;14:7.\u003c/li\u003e\n\u003cli\u003eNewman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, Khodadoust MS, Esfahani MS, Luca BA, Steiner D, Diehn M, Alizadeh AA. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019 Jul;37(7):773-782.\u003c/li\u003e\n\u003cli\u003eNewman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015 May;12(5):453-7.\u003c/li\u003e\n\u003cli\u003eSatija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015 May;33(5):495-502.\u003c/li\u003e\n\u003cli\u003eIlicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, Teichmann SA. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016 Feb 17;17:29.\u003c/li\u003e\n\u003cli\u003eMcGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 2019 Apr 24;8(4):329-337.e4. \u003c/li\u003e\n\u003cli\u003eButler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018 Jun;36(5):411-420.\u003c/li\u003e\n\u003cli\u003eJolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065):20150202.\u003c/li\u003e\n\u003cli\u003eKorsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019 Dec;16(12):1289-1296. \u003c/li\u003e\n\u003cli\u003eZhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T, Xu L, Liao G, Yan M, Ping Y, Li F, Shi A, Bai J, Zhao T, Li X, Xiao Y. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 2019 Jan 8;47(D1):D721-D728.\u003c/li\u003e\n\u003cli\u003eAran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019 Feb;20(2):163-172. \u003c/li\u003e\n\u003cli\u003eHuang H, Chen K, Zhu Y, Hu Z, Wang Y, Chen J, Li Y, Li D, Wei P. A multi-dimensional approach to unravel the intricacies of lactylation related signature for prognostic and therapeutic insight in colorectal cancer. J Transl Med. 2024 Feb 28;22(1):211. \u003c/li\u003e\n\u003cli\u003eKlevebro F, Ekman S, Nilsson M. Current trends in multimodality treatment of esophageal and gastroesophageal junction cancer\u0026mdash;review article. Surg Oncol. 2017;26(3):290\u0026ndash;295.\u003c/li\u003e\n\u003cli\u003eHou X, Wen J, Ren Z, Zhang G. Non-coding RNAs: new biomarkers and therapeutic targets for esophageal cancer. Oncotarget. 2017 Jun 27;8(26):43571-43578.\u003c/li\u003e\n\u003cli\u003eVisser E, Franken IA, Brosens LAA, Ruurda JP, van Hillegersberg R. Prognostic gene expression profiling in esophageal cancer: a systematic review. Oncotarget. 2017;8(3):5566\u0026ndash;5577.\u003c/li\u003e\n\u003cli\u003eRabinowitz JD, Enerb\u0026auml;ck S. Lactate: the ugly duckling of energy metabolism. Nat Metab. 2020 Jul;2(7):566-571. \u003c/li\u003e\n\u003cli\u003eByun JK. Tumor lactic acid: a potential target for cancer therapy. Arch Pharm Res. 2023 Feb;46(2):90-110. \u003c/li\u003e\n\u003cli\u003eWang D, Liu H, Ren C, Wang L. High Expression of ABRACL Is Associated with Tumorigenesis and Affects Clinical Outcome in Gastric Cancer. Genet Test Mol Biomarkers. 2019 Feb;23(2):91-97. \u003c/li\u003e\n\u003cli\u003eHsiao BY, Chen CH, Chi HY, Yen PR, Yu YZ, Lin CH, Pang TL, Lin WC, Li ML, Yeh YC, Chou TY, Chen MY. Human Costars Family Protein ABRACL Modulates Actin Dynamics and Cell Migration and Associates with Tumorigenic Growth. Int J Mol Sci. 2021 Feb 18;22(4):2037. \u003c/li\u003e\n\u003cli\u003eLin C, He H, Liu H, Li R, Chen Y, Qi Y, Jiang Q, Chen L, Zhang P, Zhang H, Li H, Zhang W, Sun Y, Xu J. Tumour-associated macrophages-derived CXCL8 determines immune evasion through autonomous PD-L1 expression in gastric cancer. Gut. 2019 Oct;68(10):1764-1773. \u003c/li\u003e\n\u003cli\u003eZhou S, Liu S, Lin C, Li Y, Ye L, Wu X, Jian Y, Dai Y, Ouyang Y, Zhao L, Liu M, Song L, Xi M. TRIB3 confers radiotherapy resistance in esophageal squamous cell carcinoma by stabilizing TAZ. Oncogene. 2020 Apr;39(18):3710-3725. \u003c/li\u003e\n\u003cli\u003eTaglini F, Kafetzopoulos I, Rolls W, Musialik KI, Lee HY, Zhang Y, Marenda M, Kerr L, Finan H, Rubio-Ramon C, Gautier P, Wapenaar H, Kumar D, Davidson-Smith H, Wills J, Murphy LC, Wheeler A, Wilson MD, Sproul D. DNMT3B PWWP mutations cause hypermethylation of heterochromatin. EMBO Rep. 2024 Mar;25(3):1130-1155.\u003c/li\u003e\n\u003cli\u003ePeng X, Wu X, Wu G, Peng C, Huang B, Huang M, Ding J, Mao C, Zhang H. MiR-129-2-3p Inhibits Esophageal Carcinoma Cell Proliferation, Migration, and Invasion via Targeting DNMT3B. Curr Mol Pharmacol. 2023;16(1):116-123.\u003c/li\u003e\n\u003cli\u003eZhou W, Zhu H, Xu Y, Gu L, Wu W, Zhang Y, Huang X, Jiang Y. miR-498/DNMT3b Axis Mediates Resistance to Radiotherapy in Esophageal Cancer Cells. Cancer Biother Radiopharm. 2022 May;37(4):287-299. \u003c/li\u003e\n\u003cli\u003eFurusawa Y, Kubo T, Fukazawa T. Phyhd1, an XPhyH-like homologue, is induced in mouse T cells upon T cell stimulation. Biochem Biophys Res Commun. 2016 Apr 8;472(3):551-6.\u003c/li\u003e\n\u003cli\u003eSun X, Chen P, Chen X, Yang W, Chen X, Zhou W, Huang D, Cheng Y. KIF4A enhanced cell proliferation and migration via Hippo signaling and predicted a poor prognosis in esophageal squamous cell carcinoma. Thorac Cancer. 2021 Feb;12(4):512-524.\u003c/li\u003e\n\u003cli\u003eWang W, Liao K, Guo HC, Zhou S, Yu R, Liu Y, Pan Y, Pu J. Integrated transcriptomics explored the cancer-promoting genes CDKN3 in esophageal squamous cell cancer. J Cardiothorac Surg. 2021 May 27;16(1):148.\u003c/li\u003e\n\u003cli\u003eWang L, Liu G, Bolor-Erdene E, Li Q, Mei Y, Zhou L. Identification of KIF4A as a prognostic biomarker for esophageal squamous cell carcinoma. Aging (Albany NY). 2021 Nov 14;13(21):24050-24070. \u003c/li\u003e\n\u003cli\u003eDong CH, Jiang T, Yin H, Song H, Zhang Y, Geng H, Shi PC, Xu YX, Gao H, Liu LY, Zhou L, Zhang ZH, Song J. LMNB2 promotes the progression of colorectal cancer by silencing p21 expression. Cell Death Dis. 2021 Mar 29;12(4):331. \u003c/li\u003e\n\u003cli\u003eKong W, Wu Z, Yang M, Zuo X, Yin G, Chen W. LMNB2 is a prognostic biomarker and correlated with immune infiltrates in hepatocellular carcinoma. IUBMB Life. 2020 Dec;72(12):2672-2685. \u003c/li\u003e\n\u003cli\u003eLiu X, Cheng C, Cai Y, Gu Y, Wu Y, Chen K, Wu Z. Pan‑cancer analyses reveal the regulation and clinical outcome association of PCLAF in human tumors. Int J Oncol. 2022 Jun;60(6):66. \u003c/li\u003e\n\u003cli\u003eNi G, Sun Y, Jia H, Xiahou Z, Li Y, Zhao F, Zang H. MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF+ subtype-specific therapy. Transl Oncol. 2025 Feb;52:102280. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Esophagus Cancer, Bioinformatics, ABRACL, CXCL8, single-cell RNA-seq, Multi-omics technologies","lastPublishedDoi":"10.21203/rs.3.rs-7241851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7241851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e This study aimed to investigate the prognostic value of lactate metabolism-related genes (LMRGs) in esophageal cancer (EC), exploring their roles in tumor microenvironment (TME) remodeling, immunotherapy response, and potential therapeutic targeting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Multi-omics analysis integrated bulk RNA-seq (TCGA, GEO) and single-cell RNA-seq (scRNA-seq) data. Differential expression, WGCNA, and LASSO regression were employed to identify prognostic LMRGs. Immune infiltration, drug sensitivity (GDSC), and pathway enrichment (GSEA/GSVA) analyses were performed. scRNA-seq delineated lactate-high cell populations in TME.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A 9-gene LMRG signature (ABRACL, CXCL8, TRIB3, DNMT3B, PHYHD1, KIF4A, CDKN3, LMNB2, PCLAF) was established, stratifying EC patients into high/low-risk groups with distinct survival (p\u0026lt;0.001). High-risk patients exhibited immunosuppressive TME (elevated Mast cells, follicular T cells), activated mTORC1/NF-κB pathways, and poorer immunotherapy response. Key genes (DNMT3B, CXCL8) correlated with EMT and neutrophil recruitment. Drug screening nominated Alisertib and Nutlin-3a as potential therapies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The LMRG model elucidates lactate-driven EC progression and immune evasion, offering prognostic biomarkers and therapeutic targets. Combining lactate modulation with targeted therapy may overcome treatment resistance.\u003c/p\u003e","manuscriptTitle":"Multi-omics analysis identifies lactylation-mediated immune biomarkers in esophageal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 15:10:55","doi":"10.21203/rs.3.rs-7241851/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":"362ea4fa-8b36-48a1-a15d-57c87965d886","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T07:39:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 15:10:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7241851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7241851","identity":"rs-7241851","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}