{"doi":"10.1002/hon.70096_472","title":"472 | THE HETEROGENEITY AND RELATED PROGNOSTIC FACTORS OF PRIMARY CENTRAL NERVOUS SYSTEM DIFFUSE LARGE B‐CELL LYMPHOMA BASED ON SINGLE‐CELL SEQUENCING TECHNOLOGY","abstract":"J. Youchao, H. Guiliang, and T. Chang equally contributing author. Introduction: Primary central nervous system lymphoma (PCNSL) is a special type of non-Hodgkinlymphoma. We used single cell RNA (scRNA-seq) technology, combined with a variety of bioinformatics analysis techniques to explore the intratumor heterogeneity in PCNSL, and combined with DLBCL data in the Gene Expression Omnibus (GEO) to explore the reasons for poor prognosis of PCNSL. Method: We collected tumor tissues from 5 patients with PCNSL and 2 patients with SCNSL for single-cell sequencing. Also we combined the studies, which included single-cell data from 9 patients with PCNSL and 17 patients with DLBCL. First, all samples were subjected to single-cell transcriptome sequencing and quality control, and then the data were integrated, debatch, and dimensionality reduction clustering. The processed data were used to identify the cell population according to its characteristic expressed genes, and the intratumoral heterogeneity of PCNSL was determined by copy number variation, enrichment analysis, and quasi-time sequence analysis. Combined with DLBCL scRNA-seq data in the GEO database, the differences of cell subsets and cell interactions between PCNSL and DLBCL were studied by clustering, enrichment analysis, cell communication and other analysis methods. PCNSL bulkRNA sequencing data containing clinical information in GEO database were combined to analyze the prognosis of differential genes. Results: 1. According to the characteristics of copy number variation, malignant cells were further divided into 9 malignant B cell subsets. Most malignant B cell subsets were enriched in pathways related to viral infection and neurological diseases. 2. In PCNSL, AEBP1 expression was found to be lower in tumor cells than in normal B cells and correlated with poor prognosis. 3. T cells in the tumor microenvironment are mostly exhausted, and there is heterogeneity among T cell exhaustion (Tex). Tex was distributed in different stages of T cell development. The expression of immune enhancement-related genes was lower than that of normal T cells, and the immune function was decreased. 4. After integration and clustering of PCNSL and DLBCL scRNA-seq malignant B celldata in GEO database, malignant B cells were divided into 14 subsets. We found high expression of FBLN2 present in subgroup 8 of PCNSL cells, and this gene is associated with PCNSL prognosis, with lower expression indicating better prognosis. Conclusion: 1. The intratumoral heterogeneity of PCNSL is large, and there are differences between malignant B cell subsets. Compared with normal B cells, AEBP1 expression is lower, which may be associated with poor prognosis of tumor. 2. In PCNSL, T cells are in an exhausted state and the immune function is decreased, and the functions of each exhausted T cell subset are different. 3. Compared with DLBCL, there were differential gene FBLN2 and significantly enriched signaling pathway MIF- (CD74+CXCR4) in PCNSL, which were related to the prognosis of tumor. Keywords: bioinformatics; computational and systems biology; genomics, epigenomics, and other -omics; diagnostic and prognostic biomarkers No potential sources of conflict of interest.","journal":"Hematological Oncology","year":2025,"id":568520,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9531,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1472790,"name":"Han Guiliang","orcid":"0000-0003-0564-5169","position":1,"is_corresponding":false},{"id":1354956,"name":"Ting Chang","orcid":"0000-0002-7546-8017","position":2,"is_corresponding":false},{"id":1473355,"name":"Z. Yuanxue","orcid":null,"position":3,"is_corresponding":false},{"id":299129,"name":"Jenny P.‐Y. Ting","orcid":"0000-0002-7846-0395","position":4,"is_corresponding":false},{"id":1473356,"name":"Z. Yuwei","orcid":null,"position":5,"is_corresponding":false},{"id":1473357,"name":"W. Xiaofang","orcid":null,"position":6,"is_corresponding":false},{"id":1473358,"name":"Z. Guofa","orcid":null,"position":7,"is_corresponding":false},{"id":1473359,"name":"Y. Xiaomei","orcid":null,"position":8,"is_corresponding":false},{"id":1473354,"name":"J. Youchao","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:56:52.212268Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}