Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer.

IF 3.4 2区医学 Q2 ONCOLOGY

BMC Cancer Pub Date : 2024-11-01 DOI:10.1186/s12885-024-13114-8

Biao Zhang, Bolin Zhang, Tingxin Wang, Bingqian Huang, Lijun Cen, Zhizhou Wang

{"title":"Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer.","authors":"Biao Zhang, Bolin Zhang, Tingxin Wang, Bingqian Huang, Lijun Cen, Zhizhou Wang","doi":"10.1186/s12885-024-13114-8","DOIUrl":null,"url":null,"abstract":"Background: Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.Methods: Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.Results: A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.Conclusion: This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":"24 1","pages":"1347"},"PeriodicalIF":3.4000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531184/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12885-024-13114-8","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.

Methods: Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.

Results: A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.

Conclusion: This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

查看原文本刊更多论文

综合体细胞和单细胞图谱分析胰腺癌的鞘脂代谢特征。

背景：鞘脂代谢（SM）异常与癌症发病率密切相关。然而，SM在胰腺癌（PC）中的作用仍不清楚。本研究旨在探讨鞘磷脂在胰腺癌的预后、免疫微环境和治疗中的意义：方法：通过 TCGA 和 GEO 数据库获取 PC 的单细胞和大体转录组数据。方法：通过 TCGA 和 GEO 数据库获取 PC 的单细胞和大容量转录组数据，通过 MSigDB 数据库获取 SM 相关基因（SMRGs）。利用共识聚类构建SM相关分子亚型。利用 LASSO 和 Cox 回归建立 SM 相关预后特征。采用ESTIMATE和CIBERSORT算法评估肿瘤免疫微环境。OncoPredict 软件包用于预测药物敏感性。通过CCK-8、划痕和透孔实验分析了ANKRD22在PC细胞系PANC-1和BxPC-3中的功能：结果：共获得153个SMRG，其中48个与PC患者的预后有关。在 PC 中发现了两种 SM 相关亚型（SMRGcluster A 和 B）。免疫分析显示，SMRG集群B的免疫和基质评分以及CD8 + T细胞丰度较高，而SMRG集群A的肿瘤纯度评分以及M0巨噬细胞和活化树突状细胞丰度较高。SMRG集群B的PC对吉西他滨、紫杉醇和奥沙利铂更敏感。随后建立的SM相关预后模型（包括ANLN、ANKRD22和DKK1）具有很好的预测效果。单细胞分析显示，在PC微环境中，巨噬细胞、上皮细胞和内皮细胞的SM活性相对较高。ANKRD22、DKK1和ANLN在上皮细胞中的表达水平相对较高。高表达 ANKRD22、DKK1 和 ANLN 的细胞亚群具有更活跃的 SM 活性。体外实验表明，敲除 ANKRD22 可抑制 PC 细胞的增殖、迁移和侵袭：该研究揭示了SM在PC中的重要意义，并确定了与SM相关的分子亚型和预后模型，为PC患者的分层、预后预测和精准治疗提供了新的视角。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

BMC Cancer 医学-肿瘤学

CiteScore

6.00

自引率

2.60%

发文量

1204

审稿时长

6.8 months

期刊介绍： BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.