---
title: "Palmitoylation-related Signature Predicts Prognosis and Immune Microenvironment in DLBCL"
id: "frontiers-in-immunology-8-a-palmitoylation-related-signature-predicts-prognosis-and-immune"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-8-a-palmitoylation-related-signature-predicts-prognosis-and-immune"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1907503"
published_at: "2026-09-22T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Palmitoylation-related Signature Predicts Prognosis and Immune Microenvironment in DLBCL
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-8-a-palmitoylation-related-signature-predicts-prognosis-and-immune
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1907503)
- **Published At:** 2026-09-22T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The source page contains navigation and journal information but does not include the body text of the research article titled “A palmitoylation-related signature predicts prognosis and immune microenvironment features in diffuse large B-cell lymphoma.” - From the article title, the study likely links **palmitoylation-related** molecular features to prognosis and to the **immune microenvironment** in diffuse large B-cell lymphoma (DLBCL), but the source did not provide methods, results, or conclusions. - No details on the signature composition (genes/proteins), cohorts, statistical performance, or validation were reported on the provided source page. - No clinical implications, treatment associations, survival metrics, or immune-cell profiling data were available in the source content. - Key study elements such as sample size, datasets used, analytic approach, and independent validation status were not reported in the source material. - Readers seeking the full article, datasets, or supporting figures should access the journal’s article page or contact the authors, because the source page here contains only Frontiers in Immunology navigation elements and not the article text.
## Clinical Analysis & Structured Key Points
Frontiers | A palmitoylation-related signature predicts prognosis and immune microenvironment features in diffuse large B-cell lymphoma ORIGINAL RESEARCH article Front. Immunol. , 22 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1907503 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Editor & Reviewers Edited by M C Myrna Candelaria Reviewed by H W Haina Wang W Z Wenzhuo Zhuang Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Figure 7 View in article Figure 8 View in article Figure 9 View in article Figure 10 View in article Figure 11 View in article ORIGINAL RESEARCH article Front. Immunol. , 22 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1907503 A palmitoylation-related signature predicts prognosis and immune microenvironment features in diffuse large B-cell lymphoma J Z Jie Zhang 1 X M Xiaobing Miao 2 H Y Hao Yang 3 Y Z Ying Zhou 1 Y J Yijing Jiang 3 * Q S Qian Shen 1 * X X Xiaohong Xu 1 * 1. Department of Oncology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China 2. Department of Pathology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China 3. Department of Pathophysiology, School of Medicine, Nantong University, Nantong, Jiangsu, China See more Article metrics View details Abstract Background: Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive lymphoma and remains highly heterogeneous in prognosis. Palmitoylation, an important post-translational modification, has been implicated in tumor biology, but its role in DLBCL, particularly in relation to the immune microenvironment, remains unclear. Methods: Transcriptomic and clinical data from 706 patients with DLBCL were obtained from the Gene Expression Omnibus database. Prognosis-associated palmitoylation-related genes were identified by differential expression and univariate Cox regression analyses. An optimal prognostic model was developed by comparing 101 machine learning algorithm combinations and was further integrated with clinical variables to construct a nomogram. The tumor immune microenvironment was evaluated using immune infiltration, immune function, immune checkpoint, and Tumor Immune Dysfunction and Exclusion analyses. Weighted gene co-expression network analysis (WGCNA) and Mendelian randomization (MR) were performed to identify key genes, followed by experimental validation. Results: A 33-gene palmitoylation-related signature (PRS) was established and evaluated across multiple cohorts. PRS was an independent prognostic factor of DLBCL and showed consistent prognostic performance across datasets. Low-risk patients showed higher immune cell infiltration and more active immune function, whereas high-risk patients showed a greater predicted potential to respond to immune checkpoint inhibitor therapy. A nomogram integrating PRS with clinical characteristics also showed favorable predictive accuracy. In addition, two palmitoylation-related molecular subtypes with distinct transcriptomic features, survival outcomes, and immune microenvironment characteristics were identified in DLBCL. CEACAM3 was prioritized as a key candidate gene based on integrated WGCNA and MR analyses, and functional experiments showed that CEACAM3 knockdown suppressed proliferation and invasion and promoted apoptosis, whereas CEACAM3 overexpression enhanced proliferation and invasion, reduced apoptosis, and promoted tumor growth. Conclusion: The PRS captures prognostic heterogeneity and distinct immune microenvironment features in DLBCL. PRS may provide a useful tool for risk stratification, and CEACAM3 may serve as a potential biomarker and therapeutic target in DLBCL. Introduction Diffuse large B-cell lymphoma (DLBCL) is the most common pathological subtype of non-Hodgkin lymphoma, accounting for approximately 40% of lymphoid malignancies and exhibiting marked morphological and molecular heterogeneity ( 1 , 2 ). The standard first-line treatment is chemoimmunotherapy, most commonly the R-CHOP regimen ( 2 , 3 ). Although targeted therapies have been developed and many patients respond well to treatment, approximately 30%-40% relapse and about 10% of newly diagnosed patients remain difficult to treat ( 4 , 5 ). These differences in treatment outcome are closely related to the high heterogeneity of DLBCL. Therefore, a deeper understanding of its molecular heterogeneity is essential for precise treatment and improved prognosis. With advances in sequencing technologies and molecular biology, multiple abnormalities in key signaling pathways involved in DLBCL tumorigenesis, metastasis, and chemotherapy response have been identified. In this context, multigene expression-based risk models have shown prognostic value and may provide a basis for individualized treatment strategies ( 6 – 8 ). However, their systematic construction, validation, and clinical application in DLBCL remain limited. In addition to tumor-intrinsic molecular alterations, accumulating evidence indicates that the tumor immune microenvironment (TIME), a key component of the broader tumor microenvironment (TME), plays a critical role in DLBCL progression, therapeutic response, and clinical outcomes. The composition and functional state of infiltrating immune cells vary substantially among patients and may contribute to immune escape, treatment resistance, and survival heterogeneity. Therefore, integrating molecular stratification with immune microenvironmental features may provide a more comprehensive framework for understanding DLBCL heterogeneity and improving risk assessment. Palmitoylation is a widespread and dynamic post-translational modification, and its reverse process is termed depalmitoylation ( 9 , 10 ). It has been shown to regulate the membrane localization, stability, and interactions of key oncogenic proteins, thereby contributing to tumor initiation and progression ( 11 ). In prostate and colorectal cancers, palmitoylation promotes tumor proliferation and invasion by regulating pathways such as Wnt/β-catenin and p38/MAPK ( 12 – 14 ). In breast cancer, it is also associated with chemoresistance ( 15 ). Notably, palmitoylation has been implicated in the development and drug resistance of DLBCL ( 16 , 17 ). Therefore, constructing a risk prediction model based on palmitoylation-related genes (PRGs) is of clear clinical and scientific interest. Notably, emerging studies suggest that palmitoylation is also involved in the regulation of immune signaling, membrane receptor function, and cell-cell interactions, indicating that palmitoylation-associated molecular states may influence not only tumor behavior but also the immune contexture of the TME. Moreover, the prognostic relevance of palmitoylation-related signatures (PRSs) has been reported in several malignancies, including glioblastoma ( 18 ), hepatocellular carcinoma ( 19 ), and gastric cancer ( 20 ). Nevertheless, whether PRS can be used for prognostic stratification in DLBCL, and how they relate to the immune landscape of this disease, remain insufficiently characterized. In this study, we systematically investigated the role of PRGs in DLBCL and constructed a PRG-based prognostic risk model by integrating 10 machine learning algorithms into 101 model combinations. We also prioritized CEACAM3 as a key candidate gene through integrated weighted gene co-expression network analysis (WGCNA) and Mendelian randomization (MR) analyses, analyzed its biological and prognostic significance in DLBCL, and further validated its function experimentally. Collectively, our findings may provide new strategies for risk stratification and individualized treatment in DLBCL and characterize the immunological heterogeneity associated with PRS. Materials and methods Data collection and processing Transcriptome and clinical data of DLBCL were obtained from the Gene Expression Omnibus (GEO) database, including four datasets: GSE56315, GSE10846, GSE11318, and GSE53786. Among these, GSE56315 (55 tumor samples and 33 normal samples) was used to identify differentially expressed genes (DEGs) between tumor and normal tissues. GSE10846 was used as the training cohort, whereas GSE11318 and GSE53786 served as validation cohorts for prognosis model development and validation. To minimize potential confounding from early deaths unrelated to tumor progression, only patients with an overall survival of >30 days were included in the construction and validation of the prognostic model, resulting in 399 patients from GSE10846, 193 from GSE11318, and 114 from GSE53786. Microarray probes were mapped to gene symbols according to the corresponding platform annotation files. Probes without gene annotation were removed, and when multiple probes mapped to the same gene, their expression values were averaged to obtain a single gene-level expression value. Transcriptome data within each dataset were normalized, and batch effects among these four datasets were adjusted using the ComBat function implemented in the “ sva ” R package ( 21 ) ( Supplementary Figures S1A, B ). In addition, 3,680 PRGs were retrieved from the GeneCards database using the keyword “Palmitoylation” ( https://www.genecards.org/ ) ( Supplementary Table 1 ). This GeneCards-derived gene set was used as a broad discovery set and included genes associated with palmitoylation based on database annotation and literature evidence. DEGs identification and screening of PRGs for prognosis In GSE56315, DEGs between DLBCL and normal tissues were identified using the “ limma ” R package ( 22 ). After multiple testing correction, genes with an FDR 0.585 were considered significantly differentially expressed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the identified DEGs were subsequently performed using the “ Hs.eg.db ” and “ clusterProfiler ” R packages ( 23 ), with the top 5 GO terms and top 20 significant KEGG pathways presented, respectively. The intersection between DEGs and the PRG set was defined as palmitoylation-related differentially expressed genes (PDGEs). In the training cohort, univariate Cox regression analysis was then performed to investigate the association between PDGE expression and overall survival in patients with DLBCL. PDGEs significantly associated with prognosis at P 10,000 kb). This threshold was used to increase the availability of IVs and enable exploratory MR analysis across a broader range of PDGEs. The F-statistic was calculated for each SNP, and only SNPs with an F-statistic > 10 were retained. Steiger filtering was further performed to ensure that the retained SNPs were consistent with the hypothesized causal direction from gene expression to DLBCL. Five methods, including MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode, were used for comprehensive analysis to explore the potential causal relationship between PDGEs and DLBCL. The IVW method was used as the primary MR analysis, with nominal P < 0.05 used for initial screening. Benjamini-Hochberg FDR correction was subsequently applied across the tested PDGEs to account for multiple comparisons. To ensure the reliability of MR estimates, we assessed heterogeneity using Cochran’s Q test and directional horizontal pleiotropy using the MR-Egger intercept test. Potential pleiotropic outliers were further identified using MR-PRESSO. Cell lines and cultures The human DLBCL cell lines SU-DHL-4 and TMD8 were obtained from Shanghai Zhong
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