The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma xinhong Li, surong Liu, Tongyu Sun, Hongyan Li, Juan Liu, nan Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4337582/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. Therefore, we conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO-regression analysis. We compared the immune infiltration profiles between the risk groups. The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Furthermore, enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. In summary, this study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis. single-cell RNA-sequencing B-cell marker genes immunotherapy esophageal carcinoma prognostic signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Esophageal carcinoma (ESCA), a highly prevalent gastrointestinal tumor worldwide, is frequently associated with delayed diagnosis and unfavorable outcomes 1 , 2 . While surgical intervention remains the primary curative approach for early-stage ESCA, its effectiveness remains limited for patients with advanced disease 3 . Despite advancements in early detection and combination therapies, the 5-year survival rate for ESCA patients remains disappointingly low, hovering around 20% 4,5 . While certain combination treatment strategies have shown promise in extending survival for individuals with advanced stages 6 , 7 , their clinical responses continue to be a subject of debate. Accurate prognosis prediction holds paramount significance for effective treatment planning. Hence, there is an urgent need to identify novel biomarkers that can provide insights into the prognosis of ESCA. The role of the tumor microenvironment (TME) is highly significant in the progression and infiltration of tumors 8 . The TME holds crucial implications for immunotherapy, thus impacting patient survival 9 . The importance of single-cell RNA sequencing (scRNA-seq) in advancing targeted therapy and immunotherapy has gained widespread recognition 10 . Recent advancements in scRNA-seq have revealed distinct subpopulations of immune cells within the TME, presenting a novel approach for characterizing functional biomarkers 11 . Accumulating evidence emphasizes the existence of tumor heterogeneity defined by unique immunosubtypes, with T cells, B cells, natural killer (NK) cells, and infiltrating myeloid cells constituting the predominant components of the tumor immune microenvironment 12 – 14 . In recent years, the analysis of single-cell transcriptomes has introduced a novel avenue for exploring intra-tumor heterogeneity and predicting interactions within the microenvironment 15 , 16 . However, investigations into the immune microenvironment of ESCA have predominantly focused on T cell functionality. In comparison, B cells, the second most prevalent immune cell type, have received relatively less attention in previous research 17 . Therefore, it is essential to gain a comprehensive understanding of the roles played by B cells within the ESCA microenvironment to comprehend the intricate interactions among distinct immunosubtypes, potentially leading to the discovery of innovative therapeutic strategies. Numerous investigations have aimed to uncover novel cancer biomarkers by integrating scRNA-seq and bulk RNA-seq data 18 – 20 . In our study, we conducted an integrated analysis of both scRNA-seq and bulk RNA-seq data from ESCA, with the goal of identifying B-cell marker genes and constructing a prognostic signature within the training cohort. To further assess the predictive capacity of this signature, we applied it to the test cohort and the integrated TCGA dataset. Additionally, we scrutinized disparities in the tumor immune microenvironment (TIME) and drug sensitivity across the two risk groups. We hold the belief that our findings have the potential to yield valuable prognostic biomarkers and therapeutic targets for ESCA. 2. Materials and methods 2.1. Data Collection We retrieved transcript per million (TPM) values for RNA-seq gene expression data and clinical details for ESCA from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). Our analysis focused solely on primary solid tumor patients. Additionally, single-cell RNA-seq data (GSE160269) were sourced from the GEO databases ( https://www.ncbi.nlm.nih.gov/geo/ ). 2.2. Identification of Differentially Expressed Genes Associated with B Cells in ESCA The Tumor Immune Single Cell Hub 2 (TISCH2) served as a valuable source of single-cell RNA-seq (scRNA-seq) data originating from both human and mouse tumors. This resource facilitated a comprehensive analysis of gene expression within the TME 21 . Our initial step involved extracting B cells-related genes (BRGs) from TISCH2 using specific criteria (|log2FC| < 0.25 and adjusted p-value < 0.05). Subsequently, we conducted an intersection of the genes found in the scRNA-seq GEO dataset and the TCGA dataset. Ultimately, a total of 274 BRGs were identified and compiled from these analyses. Differential analysis was conducted using the Limma package. 2.3. Establishment of a B Cells-Associated Gene Signature For the construction and validation of the B cells-associated gene signature, we randomly partitioned the TCGA-ESCA dataset into training and testing datasets in a 7:3 ratio. To identify genes linked with overall survival (OS) in ESCA patients, we performed a sequential application of univariate Cox regression followed by the least absolute shrinkage and selection operator (LASSO) regression. This methodology enabled us to isolate ten significant B cell-related genes. Utilizing their expression levels and corresponding regression coefficients, we calculated the riskscore as follows: BRGrisk = ∑ regression coefficient (gene i) x gene expression value (gene i). The patient population was then categorized into high-risk and low-risk subsets based on their median riskscore values. We subsequently employed Kaplan-Meier (K-M) survival analysis to compare overall survival between these subgroups. We also validated the prognostic gene signature in entire TCGA-ESCA samples. 2.4. Nomogram and Calibration Assessment Within the TCGA training dataset, we performed time-dependent receiver operating characteristic (ROC) curve analysis to assess the prognostic capability of the riskscore over various time intervals. Additionally, the nomogram was developed using multivariate Cox regression analysis, integrating both clinical information and the riskscore (implemented using the R package "regplot"). 2.5. Functional Enrichment Analysis We conducted functional enrichment analysis using the GSEA v4.3.2 tool sourced from the MSigDB database ( http://software.broadinstitute.org/gsea/msigdb/ ) 22 . This analysis aimed to identify significantly associated HALLMARK pathways between the high-risk and low-risk subgroups. The criteria for pathway selection included Nominal p-value < 0.25. 2.6. Analysis of Tumor Microenvironment and Immune Infiltration Levels We employed the "estimate" package to assess immune scores, stroma scores, and estimate scores. For the quantification of immune cells and immune function, a single-sample gene set enrichment analysis (ssGSEA) was conducted using R packages "GSVA" 23 . 2.7. Drug Sensitivity Analysis The initial data regarding chemotherapy response were sourced from Genomics of Drug Sensitivity in Cancer (GDSC version 2) ( https://www.cancerrxgene.org/ ). Curated data were obtained from https://osf.io/temyk . To predict the variance in chemotherapy response between the high-risk and low-risk subgroups, we employed the R package pRRophetic 24 . 2.8. Statistical Analysis All statistical analyses were conducted using R software version 4.3.0. A p-value < 0.05 was considered statistically significant unless indicated otherwise. Survival analysis was executed utilizing the R packages "survival" and "survminer". The Wilcoxon test was employed for comparing two groups, while the Kruskal-Wallis test was used for comparing more than two groups. 3. Results 3.1. Identification of B-cell marker genes expression profiles The scRNA-seq data utilized in this research were derived from 208,659 single-cell transcriptomes obtained from 60 individuals. Thirty-two clusters and 13 cell types were identified and visualized using the UMAP algorithm (Fig. 1 A, B). Among them, B cells numbered 14,773, ranking within the top 5 (Fig. 1 C). Ultimately, 279 B-cell marker genes of ESCA according to |logFC| > 0.25 and adjusted P-value < 0.05. Among them, 274 genes can be matched in the TCGA-ESCA data, with 160 genes showing differential expression between cancer and normal samples ( Table S1 ). 3.2. Prognostic Model Development and Validation Using univariate Cox regression analysis, we identified 18 B-cell marker genes significantly associated with prognosis within the training cohort (P < 0.05) ( Table S2 ). Through LASSO analysis, we further refined the selection to 10 genes based on optimal lambda values and corresponding coefficients. Utilizing these genes, we formulated the BRGrisk model as follows: BRGrisk = -0.235 * CD38 + -0.129 * AHNAK + 0.532 * DSTN + 0.289 * DNAJB1 + -0.207 * ANXA5 + 0.122 * CD3D + 0.117 * CXCL8 + -0.055 * MT1E + 0.183 * CD7 + 0.491 * CCL3. The patients were then classified into high-risk and low-risk groups based on the median BRGrisk value. Visualizing BRGrisk scores on a scatter plot revealed a proportional correlation with decreasing OS and escalating mortality (Figs. 2 A, D). Subsequently, we subjected the model to prognostic evaluation. The high-risk group exhibited significantly shorter survival than the low-risk group (P < 0.001) (Fig. 2 G). The area under the curve (AUC) values for 1-, 3-, and 5-year survival in the training cohort were 0.847, 0.835, and 0.976, respectively (Fig. 2 J). To ascertain the model's robustness, we performed identical analyses on the test cohort (Figs. 2 B, E) and all TCGA-ESCA samples (Figs. 2 C, F). The test cohort results revealed superior OS for the low-risk group compared to the high-risk group (P = 0.012) (Fig. 2 H), with AUCs of 0.784, 0.631, and 0.590 for 1-, 3-, and 5-year survival, respectively (Fig. 2 K). Similarly, analysis of all TCGA-ESCA samples demonstrated better OS for the low-risk group (P < 0.001) (Fig. 2 I), accompanied by AUCs of 0.827, 0.765, and 0.761 for 1-, 3-, and 5-year survival, respectively (Fig. 2 L). The collective outcomes consistently indicated the model's strong predictive capability. 3.3. Construction and Assessment of the Nomogram Survival Model To ascertain the potential independent prognostic role of BRGrisk, we performed both univariate and multivariate Cox regression analyses in traing cohort. Our findings from the univariate Cox regression analysis unveiled BRGrisk as a significant risk factor, with a hazard ratio (HR) of 4.992 and a 95% confidence interval (CI) spanning from 2.977 to 8.372 (P < 0.001, Fig. 3 A). Additionally, in a multivariate analysis adjusted for other potential confounding factors, BRGrisk sustained its status as an independent prognostic factor for ESCA patients, yielding an HR of 3.983 and a 95% CI of 2.332 to 6.804 (P < 0.001, Fig. 3 B). These were further confirmed in all TCGA-ESCA patients (P < 0.001, Fig. 3 C-D). In order to provide a practical prognostic tool, we developed a nomogram model by integrating N stage, M stage, overall Stage, and BRGrisk. This nomogram aimed to estimate the probabilities of 1-, 3-, and 5-year OS for ESCA patients within the TCGA cohort (Fig. 3 E). The model's predictive accuracy was verified through the ROC curves, the AUC values demonstrated the nomogram's efficacy in accurately predicting the 1-, 3-, and 5-year survival outcomes of ESCA patients (Fig. 3 F-H). 3.4. Gene Set Enrichment Analysis The outcomes of the GSEA demonstrated predominant enrichment of cancer-related pathways within the high-risk group of the training cohort. These pathways encompassed processes such as ALLOGRAFT_REJECTION, TNFA_SIGNALING_VIA_NFKB, and COMPLEMENT, among others (Fig. 4 ). The high-risk group primarily shows enrichment in HEDGEHOG_SIGNALING, WNT_BETA_CATENIN_SIGNALING, and UV_RESPONSE_DN pathways. 3.5. Utilizing the BRGrisk Gene Signature for Assessing Tumor Immune Characteristics Employing the ssGSEA, we unveiled distinct levels of immune cell infiltration, immune functions, and immune scores that differentiated the high and low BRGrisk groups (Fig. 5 A). Notably, the high BRGrisk group demonstrated an enrichment of CCR, CD8 + T cells, check-point pathways, cytolytic activity, inflammation promoting, and MHC class I (Fig. 5 A). Additionally, immune checkpoint genes, including HAVCR2, LAG3, CD274, PDCD1, TIGIT and CTLA4, exhibited significantly heightened expression in the high BRGrisk group (Fig. 5 B). Furthermore, the high-risk group exhibited a higher frequency of gene mutations (Fig. 5 C). 3.6. Drug Sensitivity Analysis In a more comprehensive assessment, we delved into the variation of IC50 levels for various chemotherapeutic drugs between the low-risk and high-risk groups (Figs. 6 ). Our findings unveiled that individuals classified within the low-risk group exhibited higher IC50 values for a range of anticancer agents, including etopside, 5-fluorouracil, docetxel and methotrexate. These results strongly suggest that the potential of the BRGrisk model as a predictive tool for aiding in the choice of suitable anticancer therapies is significant, and individuals classified as low-risk might exhibit heightened responsiveness to anticancer agents. 4. Discussion The body of evidence suggests that ESCA is characterized by significant heterogeneity, manifesting complex interactions between tumor cells and immune cells 12 , 25 . In the context of the burgeoning field of tumor immunotherapy, investigations into the tumor immune microenvironment have proliferated, revealing diverse immune cell subtypes and their pertinent functions. Despite the absence of direct tumor-killing capabilities, B cells are pivotal in their roles as antigen-presenting cells. Furthermore, B cells exert influence on tumor cells through the secretion of antibodies and cytokines 26 , 27 . In the present investigation, we conducted scRNA-seq analysis to investigate the B-cell marker genes within the context of ESCA. Subsequently, utilizing the training cohort, we established a prognostic signature. The efficacy of this signature was subsequently assessed using both the test cohort and the entirety of TCGA-ESCA samples, further validating its predictive potential. Furthermore, our analysis revealed elevated levels of immune scores, immune cell infiltration, immune checkpoint expression, and somatic mutations within the high-risk group. In this study, the prognostic signature consisted of 10 B-cell marker genes, namely CD38, AHNAK, DSTN, DNAJB1, ANXA5, CD3D, CXCL8, MT1E, CD7, and CCL3. There are reports indicating that most of these genes were correlated with the prognosis of cancer 28 – 34 . Additionally, some of these genes have been suggested as targeting agents for cancer treatment 35 – 41 . The effectiveness of the prognostic signature, which relies on the 10 identified B-cell marker genes, was subsequently confirmed through validation in both the testing cohort and across all TCGA-ESCA samples. Our findings consistently aligned across both cohorts, underscoring the robustness and reproducibility of the signature. In addition, we developed a nomogram that graphically depicts and predicts patients' probabilities of 1-, 3-, and 5-year survival. The ROC curves further substantiate the enhanced predictive accuracy of the nomogram. Thus, this nomogram holds the potential to guide the formulation of personalized assessment protocols for individuals with ESCA, facilitating optimal utilization of medical resources. Given the pivotal role of the TME in influencing antitumor responses and ultimately impacting prognosis 42 , our investigation delved into the correlation between BRGrisk and the TME. Initially, we noted a notable elevation in stromal scores within the high-risk group when contrasted with the low-risk group. Subsequently, an exploration of 29 immune cell infiltration levels unveiled a heightened presence of CD8 + T cells, NK cells, dendritic cells, and neutrophils in the high-risk group. This observation suggests that individuals in this group might be experiencing a more activated state of anti-tumor immune response. Moreover, immune checkpoint inhibitors (ICIs) represent a potential therapeutic avenue for ESCA 43 . Several randomized clinical trials have shown a substantial improvement in OS when using immunochemotherapy as the first-line treatment for metastatic ESCC 44 , 45 , as compared to doublet chemotherapy. Our findings further indicated elevated expressions of common immune checkpoint-related genes (HAVCR2, LAG3, CD274, PDCD1, TIGIT and CTLA4) within the high-risk group, suggesting that immunotherapy might be particularly advantageous for this subgroup. In conclusion, individuals within the high-risk group exhibited enhanced immune cell infiltration and immune responsiveness, potentially rendering them more amenable to benefiting from immunotherapy interventions. In order to offer more precise treatment guidance for ESCA, we conducted a drug sensitivity analysis across different risk groups. Our findings demonstrated that the low-risk group exhibited sensitivity to these anticancer drugs (etopside, 5-fluorouracil, docetxel and methotrexate). These results offer valuable insights for the clinical selection of chemotherapy medications. In future studies, we aim to delve deeper into the clinical implications of these drug sensitivities among ESCA patients. While this study has contributed fresh perspectives to advance the progression of novel therapies for ESCA, certain limitations warrant consideration. Primarily, the entirety of the cohort studies employed herein were retrospective, necessitating subsequent validation through prospective cohort investigations. Additionally, the confirmation of drug sensitivity necessitates further substantiation via cellular experimentation. Furthermore, the limited number of scRNA-seq samples and the volume of data accessible in public databases have constrained the comprehensiveness of the analyzed clinical and pathological parameters, potentially introducing biases. As such, the execution of multi-center, extensive-sample, prospective double-blind trials become imperative for substantiating these findings in the future. 5. Conclusions In summary, we have successfully formulated an innovative prognostic signature comprising 10 B-cell marker genes through the integration of scRNA-seq and bulk RNA-seq data. Additionally, the BRGrisk exhibited significant correlations with both the TIME and drug sensitivity. Our study furnishes novel theoretical insights into the impact of B-cell marker genes on the prognosis and targeted therapy potential for patients with esophageal carcinoma. Abbreviations tumor microenvironment (TME); esophageal carcinoma (ESCA); a B-cell-related gene signature (BRGrisk); single-cell RNA sequencing (scRNA-seq); natural killer (NK); tumor immune microenvironment (TIME); transcript per million (TPM); The Cancer Genome Atlas (TCGA); The Tumor Immune Single Cell Hub 2 (TISCH2); B cells-related genes (BRGs); overall survival (OS); the least absolute shrinkage and selection operator (LASSO); Kaplan-Meier (K-M); time-dependent receiver operating characteristic (ROC); a single-sample gene set enrichment analysis (ssGSEA); area under the curve (AUC); hazard ratio (HR); confidence interval (CI); immune checkpoint inhibitors (ICIs). Declarations Author Contribution Author Contributions Statement:All seven authors played integral roles in the conception, planning, execution, and reporting of the experiment described in this manuscript. Author Xinhong Li, Author Surong Liu, and Author Tongyu Su were involved in developing the initial research idea and designing the experimental protocol. Author Hongyan Li, Author Juan Liu, and Author Nan Huang carried out the experimental procedures and collected the data. Author Surong Liu conducted the data analysis and interpretation. Author Xinhong Li, Author Surong Liu, and Author Tongyu Su drafted the initial manuscript, while Author Hongyan Li, Author Juan Liu, and Author Nan Huang provided critical feedback and revisions. All authors have reviewed and approved the final version of the manuscript for submission and take responsibility for the integrity and accuracy of the work presented. Ethical approval statement Not applicable. The data of this study were obtained from the public database and no ethical approval was required. Conflict of Interest statement All of the authors declare that there is no conflict of interest. Availability of data and materials The datasets generated and analyzed during the present study are available from the corresponding author on reasonable request. Funding NA Acknowledgement NA References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Thrift AP. Global burden and epidemiology of Barrett oesophagus and oesophageal cancer. Nat Rev Gastroenterol Hepatol. 2021;18(6):432–43. Maret-Ouda J, Santoni G, Wahlin K, et al. Esophageal Adenocarcinoma After Antireflux Surgery in a Cohort Study From the 5 Nordic Countries. Ann Surg. 2021;274(6):e535–40. Eyck BM, van Lanschot JJB, Hulshof M, et al. 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Yap DWT, Leone AG, Wong NZH, et al. Effectiveness of Immune Checkpoint Inhibitors in Patients With Advanced Esophageal Squamous Cell Carcinoma: A Meta-analysis Including Low PD-L1 Subgroups. JAMA Oncol. 2023;9(2):215–24. Lu Z, Wang J, Shu Y, et al. Sintilimab versus placebo in combination with chemotherapy as first line treatment for locally advanced or metastatic oesophageal squamous cell carcinoma (ORIENT-15): multicentre, randomised, double blind, phase 3 trial. BMJ. 2022;377:e068714. Luo H, Lu J, Bai Y, et al. Effect of Camrelizumab vs Placebo Added to Chemotherapy on Survival and Progression-Free Survival in Patients With Advanced or Metastatic Esophageal Squamous Cell Carcinoma: The ESCORT-1st Randomized Clinical Trial. JAMA. 2021;326(10):916–25. Additional Declarations No competing interests reported. Supplementary Files TableS.xls Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 May, 2024 Reviews received at journal 26 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers invited by journal 01 May, 2024 Editor assigned by journal 01 May, 2024 Submission checks completed at journal 29 Apr, 2024 First submitted to journal 28 Apr, 2024 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-4337582","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299195459,"identity":"250f7e66-d1a5-465c-bda0-b1fa03acf6ac","order_by":0,"name":"xinhong Li","email":"","orcid":"","institution":"521 Hospital of Norinco Group","correspondingAuthor":false,"prefix":"","firstName":"xinhong","middleName":"","lastName":"Li","suffix":""},{"id":299195463,"identity":"b2514ae4-bf4a-45d5-ac44-7bc35a89ca84","order_by":1,"name":"surong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACxvbGxgcJPBI8bOwNRGph7jl82OCBjI0MP88BIrWwz3BLk3xgk2YjOSOBSC28M3gMJBJyDvMY3Hy88QZDjU00QS2Ss3sMDBLOALXcTiu2YDiWlttASIvhnDMGCYk9IC05ZhKMDYcJa7G/kWNwIPEfyGFniNTCOCMtsSGBJ41HcgYPsVqAgcyQwGPDw88D9EsCMX4BRmX7zx88EvZs7Ic33vhQY0NYCzIAhjYpyiFaSNUxCkbBKBgFIwMAAI3cQhMsAWZyAAAAAElFTkSuQmCC","orcid":"","institution":"521 Hospital of Norinco Group","correspondingAuthor":true,"prefix":"","firstName":"surong","middleName":"","lastName":"Liu","suffix":""},{"id":299195467,"identity":"4e7c4b38-4a5c-4dce-a284-ac7d86ab15f9","order_by":2,"name":"Tongyu Sun","email":"","orcid":"","institution":"521 Hospital of Norinco Group","correspondingAuthor":false,"prefix":"","firstName":"Tongyu","middleName":"","lastName":"Sun","suffix":""},{"id":299195469,"identity":"612453c1-0555-43de-8adb-b80153a3626a","order_by":3,"name":"Hongyan Li","email":"","orcid":"","institution":"521 Hospital of Norinco Group","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Li","suffix":""},{"id":299195471,"identity":"93a58ffc-5018-4db8-9f5b-35d82d7b36fd","order_by":4,"name":"Juan Liu","email":"","orcid":"","institution":"521 Hospital of Norinco Group","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Liu","suffix":""},{"id":299195474,"identity":"0ce4c088-9ed7-484f-9e92-729f64510466","order_by":5,"name":"nan Huang","email":"","orcid":"","institution":"the Second Affiliated Hospital of Xi’ an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"nan","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-04-28 10:59:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4337582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4337582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56140154,"identity":"5e72576a-c7be-479d-8446-e860ab9fddae","added_by":"auto","created_at":"2024-05-09 04:05:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":561272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of B Cells through scRNA-seq Analysis. (A)\u003c/strong\u003e Thirty-two clusters were discerned through the application of the UMAP algorithm. \u003cstrong\u003e(B)\u003c/strong\u003e Thirteen distinct cell types were characterized based on established cell marker genes. \u003cstrong\u003e(C)\u003c/strong\u003eThe pie chart provides a visual representation of the relative proportions of each defined cell type. \u003cstrong\u003e(D)\u003c/strong\u003e The bar plot illustrates the relative distribution of each defined cell type across individual samples.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/793d4597835fae5c7555aa5c.png"},{"id":56140248,"identity":"645dd7e0-4a37-429b-8bde-e37f4ae6db0f","added_by":"auto","created_at":"2024-05-09 04:10:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":714402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of prognostic models.\u003c/strong\u003e \u003cstrong\u003e(A–C)\u003c/strong\u003eDistribution of BRGrisk score in training cohort, testing cohort and whole TCGA-ESCA cohort, respective. \u003cstrong\u003e(D–F)\u003c/strong\u003e Scatter plot of the OS of each patient in the training cohort, testing cohort and whole TCGA-ESCA cohort, respective. \u003cstrong\u003e(G–I)\u003c/strong\u003e The Kaplan-Meier curves in the training cohort, testing cohort and whole TCGA-ESCA cohort, respective. \u003cstrong\u003e(J–L) \u003c/strong\u003eThe AUC at 1-, 3-, and 5-years of prognostic models in the training cohort, testing cohort and whole TCGA-ESCA cohort,respective.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/a7453dd23978ca664b58de27.png"},{"id":56140173,"identity":"c92978a9-21c4-4030-9c85-28442a625cf1","added_by":"auto","created_at":"2024-05-09 04:08:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":718947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive Nomogram.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Univariate analysis of clinicopathologic features and BRG risk in the training cohort. \u003cstrong\u003e(B)\u003c/strong\u003eMultivariate analysis of clinicopathologic features and BRG risk in the training cohort. \u003cstrong\u003e(C)\u003c/strong\u003e Univariate analysis of clinicopathologic features and BRG risk in the entire TCGA-ESCA cohort. \u003cstrong\u003e(D)\u003c/strong\u003e Multivariate analysis of clinicopathologic features and BRG risk in the entire TCGA-ESCA cohort. \u003cstrong\u003e(E)\u003c/strong\u003eNomogram for predicting the survival of ESCA patients. \u003cstrong\u003e(F-H)\u003c/strong\u003e ROC curves for clinicopathologic features and BRG risk, depicting 1-, 3-, and 5-year survival, respectively.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/ef87e53d0062e8058a0e1ac7.png"},{"id":56140241,"identity":"027c64f0-5fc5-4e3e-8260-8133f254f096","added_by":"auto","created_at":"2024-05-09 04:09:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":315262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological characterization of of high- and low- BRGrisk groups.\u003c/strong\u003eThe GSEA pathway enrichment analysis in low- (left) and high- (right) ERS groups.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/79765aa80c6a51e0c0a37365.png"},{"id":56140159,"identity":"e14787fb-b30f-4108-abd8-e1e161da5e6c","added_by":"auto","created_at":"2024-05-09 04:06:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":779444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunological characterization of high- and low- BRGrisk groups.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eThe expression of immune function between different groups. \u003cstrong\u003e(B) \u003c/strong\u003eThe expression levels of immune checkpoint genes between different groups. ns, not significant, *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/8412de67c803531b534b74b6.png"},{"id":56140233,"identity":"7dc8a146-9af2-4aa2-bc16-a28ec3c317b3","added_by":"auto","created_at":"2024-05-09 04:09:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":189571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis of high- and low- BRGrisk groups.\u003c/strong\u003e The boxplot of sensitivity of common chemotherapy drugs in different groups.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/ca457a2fc4b01a4001d5dea4.png"},{"id":56140886,"identity":"0decde92-7d63-4342-992c-c8bb4b5b8e21","added_by":"auto","created_at":"2024-05-09 04:40:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4096394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/8026abd5-5aee-4c39-959e-450c1e6c1f32.pdf"},{"id":56133137,"identity":"4aad39a6-859d-400d-8867-b1e335e91351","added_by":"auto","created_at":"2024-05-09 02:22:45","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46080,"visible":true,"origin":"","legend":"","description":"","filename":"TableS.xls","url":"https://assets-eu.researchsquare.com/files/rs-4337582/v1/abf58c581a3f9743e4b105b0.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEsophageal carcinoma (ESCA), a highly prevalent gastrointestinal tumor worldwide, is frequently associated with delayed diagnosis and unfavorable outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While surgical intervention remains the primary curative approach for early-stage ESCA, its effectiveness remains limited for patients with advanced disease\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite advancements in early detection and combination therapies, the 5-year survival rate for ESCA patients remains disappointingly low, hovering around 20%\u003csup\u003e4,5\u003c/sup\u003e. While certain combination treatment strategies have shown promise in extending survival for individuals with advanced stages\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, their clinical responses continue to be a subject of debate. Accurate prognosis prediction holds paramount significance for effective treatment planning. Hence, there is an urgent need to identify novel biomarkers that can provide insights into the prognosis of ESCA.\u003c/p\u003e \u003cp\u003eThe role of the tumor microenvironment (TME) is highly significant in the progression and infiltration of tumors\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The TME holds crucial implications for immunotherapy, thus impacting patient survival\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The importance of single-cell RNA sequencing (scRNA-seq) in advancing targeted therapy and immunotherapy has gained widespread recognition\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Recent advancements in scRNA-seq have revealed distinct subpopulations of immune cells within the TME, presenting a novel approach for characterizing functional biomarkers\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Accumulating evidence emphasizes the existence of tumor heterogeneity defined by unique immunosubtypes, with T cells, B cells, natural killer (NK) cells, and infiltrating myeloid cells constituting the predominant components of the tumor immune microenvironment\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In recent years, the analysis of single-cell transcriptomes has introduced a novel avenue for exploring intra-tumor heterogeneity and predicting interactions within the microenvironment\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, investigations into the immune microenvironment of ESCA have predominantly focused on T cell functionality. In comparison, B cells, the second most prevalent immune cell type, have received relatively less attention in previous research\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Therefore, it is essential to gain a comprehensive understanding of the roles played by B cells within the ESCA microenvironment to comprehend the intricate interactions among distinct immunosubtypes, potentially leading to the discovery of innovative therapeutic strategies.\u003c/p\u003e \u003cp\u003eNumerous investigations have aimed to uncover novel cancer biomarkers by integrating scRNA-seq and bulk RNA-seq data\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In our study, we conducted an integrated analysis of both scRNA-seq and bulk RNA-seq data from ESCA, with the goal of identifying B-cell marker genes and constructing a prognostic signature within the training cohort. To further assess the predictive capacity of this signature, we applied it to the test cohort and the integrated TCGA dataset. Additionally, we scrutinized disparities in the tumor immune microenvironment (TIME) and drug sensitivity across the two risk groups. We hold the belief that our findings have the potential to yield valuable prognostic biomarkers and therapeutic targets for ESCA.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Collection\u003c/h2\u003e \u003cp\u003eWe retrieved transcript per million (TPM) values for RNA-seq gene expression data and clinical details for ESCA from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Our analysis focused solely on primary solid tumor patients. Additionally, single-cell RNA-seq data (GSE160269) were sourced from the GEO databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identification of Differentially Expressed Genes Associated with B Cells in ESCA\u003c/h2\u003e \u003cp\u003eThe Tumor Immune Single Cell Hub 2 (TISCH2) served as a valuable source of single-cell RNA-seq (scRNA-seq) data originating from both human and mouse tumors. This resource facilitated a comprehensive analysis of gene expression within the TME\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Our initial step involved extracting B cells-related genes (BRGs) from TISCH2 using specific criteria (|log2FC| \u0026lt; 0.25 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, we conducted an intersection of the genes found in the scRNA-seq GEO dataset and the TCGA dataset. Ultimately, a total of 274 BRGs were identified and compiled from these analyses. Differential analysis was conducted using the Limma package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Establishment of a B Cells-Associated Gene Signature\u003c/h2\u003e \u003cp\u003eFor the construction and validation of the B cells-associated gene signature, we randomly partitioned the TCGA-ESCA dataset into training and testing datasets in a 7:3 ratio. To identify genes linked with overall survival (OS) in ESCA patients, we performed a sequential application of univariate Cox regression followed by the least absolute shrinkage and selection operator (LASSO) regression. This methodology enabled us to isolate ten significant B cell-related genes. Utilizing their expression levels and corresponding regression coefficients, we calculated the riskscore as follows: BRGrisk = \u0026sum; regression coefficient (gene i) x gene expression value (gene i). The patient population was then categorized into high-risk and low-risk subsets based on their median riskscore values. We subsequently employed Kaplan-Meier (K-M) survival analysis to compare overall survival between these subgroups. We also validated the prognostic gene signature in entire TCGA-ESCA samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Nomogram and Calibration Assessment\u003c/h2\u003e \u003cp\u003eWithin the TCGA training dataset, we performed time-dependent receiver operating characteristic (ROC) curve analysis to assess the prognostic capability of the riskscore over various time intervals. Additionally, the nomogram was developed using multivariate Cox regression analysis, integrating both clinical information and the riskscore (implemented using the R package \"regplot\").\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eWe conducted functional enrichment analysis using the GSEA v4.3.2 tool sourced from the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e22\u003c/sup\u003e. This analysis aimed to identify significantly associated HALLMARK pathways between the high-risk and low-risk subgroups. The criteria for pathway selection included Nominal p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Analysis of Tumor Microenvironment and Immune Infiltration Levels\u003c/h2\u003e \u003cp\u003eWe employed the \"estimate\" package to assess immune scores, stroma scores, and estimate scores. For the quantification of immune cells and immune function, a single-sample gene set enrichment analysis (ssGSEA) was conducted using R packages \"GSVA\" \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Drug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eThe initial data regarding chemotherapy response were sourced from Genomics of Drug Sensitivity in Cancer (GDSC version 2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Curated data were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/temyk\u003c/span\u003e\u003cspan address=\"https://osf.io/temyk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. To predict the variance in chemotherapy response between the high-risk and low-risk subgroups, we employed the R package pRRophetic\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R software version 4.3.0. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant unless indicated otherwise. Survival analysis was executed utilizing the R packages \"survival\" and \"survminer\". The Wilcoxon test was employed for comparing two groups, while the Kruskal-Wallis test was used for comparing more than two groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Identification of B-cell marker genes expression profiles\u003c/h2\u003e \u003cp\u003eThe scRNA-seq data utilized in this research were derived from 208,659 single-cell transcriptomes obtained from 60 individuals. Thirty-two clusters and 13 cell types were identified and visualized using the UMAP algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Among them, B cells numbered 14,773, ranking within the top 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Ultimately, 279 B-cell marker genes of ESCA according to |logFC| \u0026gt; 0.25 and adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Among them, 274 genes can be matched in the TCGA-ESCA data, with 160 genes showing differential expression between cancer and normal samples (\u003cb\u003eTable S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Prognostic Model Development and Validation\u003c/h2\u003e \u003cp\u003eUsing univariate Cox regression analysis, we identified 18 B-cell marker genes significantly associated with prognosis within the training cohort (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eTable S2\u003c/b\u003e). Through LASSO analysis, we further refined the selection to 10 genes based on optimal lambda values and corresponding coefficients. Utilizing these genes, we formulated the BRGrisk model as follows: BRGrisk = -0.235 * CD38 + -0.129 * AHNAK\u0026thinsp;+\u0026thinsp;0.532 * DSTN\u0026thinsp;+\u0026thinsp;0.289 * DNAJB1 + -0.207 * ANXA5\u0026thinsp;+\u0026thinsp;0.122 * CD3D\u0026thinsp;+\u0026thinsp;0.117 * CXCL8 + -0.055 * MT1E\u0026thinsp;+\u0026thinsp;0.183 * CD7\u0026thinsp;+\u0026thinsp;0.491 * CCL3. The patients were then classified into high-risk and low-risk groups based on the median BRGrisk value. Visualizing BRGrisk scores on a scatter plot revealed a proportional correlation with decreasing OS and escalating mortality (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, D). Subsequently, we subjected the model to prognostic evaluation. The high-risk group exhibited significantly shorter survival than the low-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). The area under the curve (AUC) values for 1-, 3-, and 5-year survival in the training cohort were 0.847, 0.835, and 0.976, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). To ascertain the model's robustness, we performed identical analyses on the test cohort (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, E) and all TCGA-ESCA samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, F). The test cohort results revealed superior OS for the low-risk group compared to the high-risk group (P\u0026thinsp;=\u0026thinsp;0.012) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH), with AUCs of 0.784, 0.631, and 0.590 for 1-, 3-, and 5-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eK). Similarly, analysis of all TCGA-ESCA samples demonstrated better OS for the low-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI), accompanied by AUCs of 0.827, 0.765, and 0.761 for 1-, 3-, and 5-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL). The collective outcomes consistently indicated the model's strong predictive capability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Construction and Assessment of the Nomogram Survival Model\u003c/h2\u003e \u003cp\u003eTo ascertain the potential independent prognostic role of BRGrisk, we performed both univariate and multivariate Cox regression analyses in traing cohort. Our findings from the univariate Cox regression analysis unveiled BRGrisk as a significant risk factor, with a hazard ratio (HR) of 4.992 and a 95% confidence interval (CI) spanning from 2.977 to 8.372 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, in a multivariate analysis adjusted for other potential confounding factors, BRGrisk sustained its status as an independent prognostic factor for ESCA patients, yielding an HR of 3.983 and a 95% CI of 2.332 to 6.804 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These were further confirmed in all TCGA-ESCA patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). In order to provide a practical prognostic tool, we developed a nomogram model by integrating N stage, M stage, overall Stage, and BRGrisk. This nomogram aimed to estimate the probabilities of 1-, 3-, and 5-year OS for ESCA patients within the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The model's predictive accuracy was verified through the ROC curves, the AUC values demonstrated the nomogram's efficacy in accurately predicting the 1-, 3-, and 5-year survival outcomes of ESCA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Gene Set Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe outcomes of the GSEA demonstrated predominant enrichment of cancer-related pathways within the high-risk group of the training cohort. These pathways encompassed processes such as ALLOGRAFT_REJECTION, TNFA_SIGNALING_VIA_NFKB, and COMPLEMENT, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The high-risk group primarily shows enrichment in HEDGEHOG_SIGNALING, WNT_BETA_CATENIN_SIGNALING, and UV_RESPONSE_DN pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Utilizing the BRGrisk Gene Signature for Assessing Tumor Immune Characteristics\u003c/h2\u003e \u003cp\u003eEmploying the ssGSEA, we unveiled distinct levels of immune cell infiltration, immune functions, and immune scores that differentiated the high and low BRGrisk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Notably, the high BRGrisk group demonstrated an enrichment of CCR, CD8\u0026thinsp;+\u0026thinsp;T cells, check-point pathways, cytolytic activity, inflammation promoting, and MHC class I (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Additionally, immune checkpoint genes, including HAVCR2, LAG3, CD274, PDCD1, TIGIT and CTLA4, exhibited significantly heightened expression in the high BRGrisk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, the high-risk group exhibited a higher frequency of gene mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Drug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eIn a more comprehensive assessment, we delved into the variation of IC50 levels for various chemotherapeutic drugs between the low-risk and high-risk groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Our findings unveiled that individuals classified within the low-risk group exhibited higher IC50 values for a range of anticancer agents, including etopside, 5-fluorouracil, docetxel and methotrexate. These results strongly suggest that the potential of the BRGrisk model as a predictive tool for aiding in the choice of suitable anticancer therapies is significant, and individuals classified as low-risk might exhibit heightened responsiveness to anticancer agents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe body of evidence suggests that ESCA is characterized by significant heterogeneity, manifesting complex interactions between tumor cells and immune cells\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In the context of the burgeoning field of tumor immunotherapy, investigations into the tumor immune microenvironment have proliferated, revealing diverse immune cell subtypes and their pertinent functions. Despite the absence of direct tumor-killing capabilities, B cells are pivotal in their roles as antigen-presenting cells. Furthermore, B cells exert influence on tumor cells through the secretion of antibodies and cytokines\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In the present investigation, we conducted scRNA-seq analysis to investigate the B-cell marker genes within the context of ESCA. Subsequently, utilizing the training cohort, we established a prognostic signature. The efficacy of this signature was subsequently assessed using both the test cohort and the entirety of TCGA-ESCA samples, further validating its predictive potential. Furthermore, our analysis revealed elevated levels of immune scores, immune cell infiltration, immune checkpoint expression, and somatic mutations within the high-risk group.\u003c/p\u003e \u003cp\u003eIn this study, the prognostic signature consisted of 10 B-cell marker genes, namely CD38, AHNAK, DSTN, DNAJB1, ANXA5, CD3D, CXCL8, MT1E, CD7, and CCL3. There are reports indicating that most of these genes were correlated with the prognosis of cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31 CR32 CR33\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Additionally, some of these genes have been suggested as targeting agents for cancer treatment\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The effectiveness of the prognostic signature, which relies on the 10 identified B-cell marker genes, was subsequently confirmed through validation in both the testing cohort and across all TCGA-ESCA samples. Our findings consistently aligned across both cohorts, underscoring the robustness and reproducibility of the signature. In addition, we developed a nomogram that graphically depicts and predicts patients' probabilities of 1-, 3-, and 5-year survival. The ROC curves further substantiate the enhanced predictive accuracy of the nomogram. Thus, this nomogram holds the potential to guide the formulation of personalized assessment protocols for individuals with ESCA, facilitating optimal utilization of medical resources.\u003c/p\u003e \u003cp\u003eGiven the pivotal role of the TME in influencing antitumor responses and ultimately impacting prognosis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, our investigation delved into the correlation between BRGrisk and the TME. Initially, we noted a notable elevation in stromal scores within the high-risk group when contrasted with the low-risk group. Subsequently, an exploration of 29 immune cell infiltration levels unveiled a heightened presence of CD8\u0026thinsp;+\u0026thinsp;T cells, NK cells, dendritic cells, and neutrophils in the high-risk group. This observation suggests that individuals in this group might be experiencing a more activated state of anti-tumor immune response. Moreover, immune checkpoint inhibitors (ICIs) represent a potential therapeutic avenue for ESCA\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Several randomized clinical trials have shown a substantial improvement in OS when using immunochemotherapy as the first-line treatment for metastatic ESCC\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, as compared to doublet chemotherapy. Our findings further indicated elevated expressions of common immune checkpoint-related genes (HAVCR2, LAG3, CD274, PDCD1, TIGIT and CTLA4) within the high-risk group, suggesting that immunotherapy might be particularly advantageous for this subgroup. In conclusion, individuals within the high-risk group exhibited enhanced immune cell infiltration and immune responsiveness, potentially rendering them more amenable to benefiting from immunotherapy interventions.\u003c/p\u003e \u003cp\u003eIn order to offer more precise treatment guidance for ESCA, we conducted a drug sensitivity analysis across different risk groups. Our findings demonstrated that the low-risk group exhibited sensitivity to these anticancer drugs (etopside, 5-fluorouracil, docetxel and methotrexate). These results offer valuable insights for the clinical selection of chemotherapy medications. In future studies, we aim to delve deeper into the clinical implications of these drug sensitivities among ESCA patients.\u003c/p\u003e \u003cp\u003eWhile this study has contributed fresh perspectives to advance the progression of novel therapies for ESCA, certain limitations warrant consideration. Primarily, the entirety of the cohort studies employed herein were retrospective, necessitating subsequent validation through prospective cohort investigations. Additionally, the confirmation of drug sensitivity necessitates further substantiation via cellular experimentation. Furthermore, the limited number of scRNA-seq samples and the volume of data accessible in public databases have constrained the comprehensiveness of the analyzed clinical and pathological parameters, potentially introducing biases. As such, the execution of multi-center, extensive-sample, prospective double-blind trials become imperative for substantiating these findings in the future.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, we have successfully formulated an innovative prognostic signature comprising 10 B-cell marker genes through the integration of scRNA-seq and bulk RNA-seq data. Additionally, the BRGrisk exhibited significant correlations with both the TIME and drug sensitivity. Our study furnishes novel theoretical insights into the impact of B-cell marker genes on the prognosis and targeted therapy potential for patients with esophageal carcinoma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003etumor microenvironment (TME); esophageal carcinoma (ESCA); a B-cell-related gene signature (BRGrisk); single-cell RNA sequencing (scRNA-seq); natural killer (NK); tumor immune microenvironment (TIME); transcript per million (TPM); The Cancer Genome Atlas (TCGA); The Tumor Immune Single Cell Hub 2 (TISCH2); B cells-related genes (BRGs); overall survival (OS); the least absolute shrinkage and selection operator (LASSO); Kaplan-Meier (K-M); time-dependent receiver operating characteristic (ROC); a single-sample gene set enrichment analysis (ssGSEA); area under the curve (AUC); hazard ratio (HR); confidence interval (CI); immune checkpoint inhibitors (ICIs).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eAuthor Contribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAuthor Contributions Statement:All seven authors played integral roles in the conception, planning, execution, and reporting of the experiment described in this manuscript. Author Xinhong Li, Author Surong Liu, and Author Tongyu Su were involved in developing the initial research idea and designing the experimental protocol. Author Hongyan Li, Author Juan Liu, and Author Nan Huang carried out the experimental procedures and collected the data. Author Surong Liu conducted the data analysis and interpretation. Author Xinhong Li, Author Surong Liu, and Author Tongyu Su drafted the initial manuscript, while Author Hongyan Li, Author Juan Liu, and Author Nan Huang provided critical feedback and revisions. All authors have reviewed and approved the final version of the manuscript for submission and take responsibility for the integrity and accuracy of the work presented.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;The data of this study were obtained from the public database and no ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the authors declare that there is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the present study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. 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JAMA. 2021;326(10):916\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"single-cell RNA-sequencing, B-cell marker genes, immunotherapy, esophageal carcinoma, prognostic signature","lastPublishedDoi":"10.21203/rs.3.rs-4337582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4337582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. Therefore, we conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO-regression analysis. We compared the immune infiltration profiles between the risk groups. The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Furthermore, enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. In summary, this study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.\u003c/p\u003e","manuscriptTitle":"The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 02:22:28","doi":"10.21203/rs.3.rs-4337582/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-26T23:08:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-26T07:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306959632596922671168812234195820293407","date":"2024-05-20T04:26:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-01T10:30:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-01T10:13:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-29T12:36:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Gastrointestinal Cancer","date":"2024-04-28T10:50:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f78f173-baf5-4e8e-98d5-f994fd53cf6c","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-15T14:59:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-09 02:22:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4337582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4337582","identity":"rs-4337582","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}