Cancer Med. 2024;13(18):e70238.

Unraveling the impact of lysosomal dysfunction on myeloproliferative neoplasm.

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제1저자 윤현동 · 교신저자 윤성수, 고영일

PMID 39320136 PMC11423461 DOI 10.1002/cam4.70238 IF 3.5 혈액종양내과

소속 (12)

Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea.
Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea.
BK21 FOUR Intelligence Computing, Seoul National University, Seoul, Republic of Korea.
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.
Division of Hematology/Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
AIGENDRUG Co., Ltd, Seoul, Republic of Korea.
MOGAM Institute for Biomedical Research, Green Cross Corp, Yongin, Republic of Korea.
Department of Genomic Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Center for Medical Innovation, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.

Abstract

BACKGROUND Lysosomal dysfunction (LD) impacts cytokine regulation, inflammation, and immune responses, influencing the development and progression of cancer. Inflammation is implicated in the pathogenesis of myeloproliferative neoplasm (MPN). With a hypothesis that LD significantly contributes to MPN carcinogenesis by inducing abnormal inflammation, our objective was to elucidate the pathophysiological mechanisms of MPN arising from an LD background.

METHODS Genotyping of the LD background was performed in a cohort of MPN patients (n = 190) and healthy controls (n = 461). Logistic regression modeling, utilizing genotype data, was employed to estimate the correlation between LD and MPN. Whole transcriptome sequencing (WTS) (LD carriers = 8, non-carriers = 6) and single-cell RNA sequencing data (LD carriers = 2, non-carriers = 2, healthy controls = 2) were generated and analyzed.

RESULTS A higher variant frequency of LD was observed in MPN compared to healthy controls (healthy, 4.9%; MPN, 7.8%), with the highest frequency seen in polycythemia vera (PV) (odds ratio = 2.33, p = 0.03). WTS revealed that LD carriers exhibited upregulated inflammatory cytokine ligand-receptor genes, pathways, and network modules in MPNs compared to non-carriers. At the single-cell level, there was monocyte expansion and elevation of cytokine ligand-receptor interactions, inflammatory transcription factors, and network modules centered on monocytes. Notably, Oncostatin-M (OSM) consistently emerged as a candidate molecule involved in the pathogenesis of LD-related PV.

CONCLUSIONS In summary, an LD background is prevalent in MPN patients and leads to increased cytokine dysregulation and inflammation. OSM, as one of the potential molecules, plays a crucial role in PV pathogenesis by impairing lysosomal function.

Keywords

Oncostatin‐Mcytokine dysregulationgermline variantlysosomal storage diseasemyeloproliferative neoplasm

Figures

**FIGURE 1** Regulation changes of inflammatory cytokine LR genes, pathways, and transcription factors at the bulk RNA level. (A) Number of upregulated and downregulated LR genes in MPN total and subtype diseases with an LD background. (B) Venn diagram depicting overlapping inflammatory cytokine LR genes in each group with an LD background. (C) Pathways that are upregulated or downregulated in each group with an LD background. (D) Transcription factors that are upregulated or downregulated in each group with an LD background (highlighted in red represent inflammatory transcription factors). LD, lysosomal dysfunction; LR, ligand‐receptor; MPN, myeloproliferative neoplasm.

**FIGURE 2** Differential gene correlation network and regulated pathways and transcription factors in the LD background. (A) Differential gene correlation network observed in the LD background group (cytokine‐mediated and OSM signaling highlighted in red). (B) Enhanced pathways in the differential network. (C) Enriched transcription factors in the differential network. LD, lysosomal dysfunction; OSM, Oncostatin‐M.

**FIGURE 3** Cell proportion and differential expression cytokine LR genes in single‐cell transcriptome data. (A) UMAP visualization of single‐cell RNA from integrated patients with PV (n = 4; two LD carriers and two non‐carriers) and healthy donors (n = 2). (B) Relative cell population across groups. (C) Statistical testing result of differential cell population between groups. (D) Differential cytokine LR gene expression across the entire cell type between groups with and without LD background, with cytokine LR genes (Target Cytokine LR genes were previously duplicated in the bulk RNA data labeled). LD, lysosomal dysfunction; LR, ligand–receptor; PV, polycythemia vera; UMAP, Uniform Manifold Approximation and Projection.

**FIGURE 4** Regulated intracellular interaction and pathways in single‐cell transcriptome data. (A) Comparison of intercellular interaction strength of duplicated target cytokine LR genes in target cell types within each group. (B) Pathways regulated by patients with LD background across the entire cell type compared between patients with and without LD background. LD, lysosomal dysfunction; LR, ligand–receptor.

**FIGURE 5** Identified regulated network modules and hub genes in the differential gene correlation network of monocytes and enriched network pathways. (A) Visualization of the gene correlation network modules identified in monocytes using UMAP. (B) Volcano plot illustrating the differentially regulated network modules in monocytes. (C) Hub genes comprise network module 9, upregulated in monocytes (OSM highlighted in red). (D) Enriched pathways associated with the upregulated network module. OSM, Oncostatin‐M; UMAP, Uniform Manifold Approximation and Projection.

**FIGURE 6** Gene regulatory network (GRN) at the single‐cell level. (A) Transcription factor activity scores in each cell group (inflammatory target transcription factors highlighted in red). (B) Comparison of target transcription factor expression at the single‐cell level (healthy cells highlighted in blue, PV patients without LD background highlighted in yellow, PV patients with LD background highlighted in red). (C) Ranking of transcription factor activity in each group (labeled with target transcription factors). (D) Comparison of downstream target gene expression values of target transcription factors in each group (highlighted in red for expression in monocytes in each group). LD, lysosomal dysfunction; PV, polycythemia vera.

그림 출처: PubMed Central 공개 논문 (PMC11423461)

Conflict of interest statement

TKK has received a research funding from Nextcure TKK is a consultant for Agenus and Immunobiome. These are not relevant to this manuscript. The authors affirm that the research was carried out without any potential conflict of interest arising from commercial or financial relationships.

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