Value of cuproptosis gene LIPT1 in diagnosis and prognosis of brain gliomas
LIN Yanchen1, XU Wei2, YING Cao3, LIU Rui3, ZHANG Yuyu1, DAI Erqing1, LI Jingjing4
1. Department of Rehabilitation Medicine, 2. Department of Pathology, Characteristics Medical Center of Chinese People's Armed Police Force, Tianjin 300162, China; 3. Graduate School of Tianjin University of Traditional Chinese Medicine, Tianjin 314000, China; 4. Department of Pharmacy, the Fourth Central Hospital of Tianjin, Tianjin 300140, China
摘要目的 探讨铜死亡基因脂肪酸转移酶-1(LIPT1)在胶质瘤诊断及预后判断中的潜在价值。方法 利用TCGA数据库挖掘胶质瘤中10种铜死亡基因表达。采用IHC和qRT-PCR 检测18例胶质瘤患者中LIPT1的表达。采用qRT-PCR检测LIPT1在HEB、U87和U251细胞中的表达。采用ROC曲线预测LIPT1在胶质瘤中的诊断效能。采用Kaplan-Meier生存曲线和Cox分析探索LIPT1的预后价值。采用ssGSEA数据库评估LIPT1对胶质瘤免疫浸润的影响。探索胶质瘤中LIPT1与免疫学检查点、DNA甲基化位点和DNA错配修复基因之间的关系。对LIPT1相关基因进行GO、KEGG和GSEA功能富集分析。Animal TFDB3预测LIPT1相关转录因子。利用GDSC数据库预测针对LIPT1和hub基因的潜在药物。结果 LIPT1在胶质瘤中的表达高于正常组织(3.03 vs. 2.28, P<0.05)。Ⅱ、Ⅲ、Ⅳ级胶质瘤的免疫评分存在差异[(1.47±0.51) vs. (4.94±1.25) vs. (9.88±1.65),P<0.05]。HEB、U87和U251细胞LIPT1的mRNA水平存在差异[(0.22±0.03) vs. (0.45±0.04) vs. (0.58±0.05),P<0.05]。LIPT1在胶质瘤诊断中的AUC值为0.87。LIPT1高表达组较低表达组OS、DSS、PFI缩短,差异有统计学意义(P<0.05)。 LIPT1表达与免疫浸润改变及免疫检查点、DNA甲基化位点和DNA错配修复基因的表达存在相关性(P<0.05)。FOXG1等转录因子可能与LIPT1结合。多西他赛等药物可能通过靶向调控LIPT1相关hub基因治疗胶质瘤。结论 LIPT1可能是胶质瘤的潜在生物标志物,与胶质瘤不良预后有关。
Abstract:Objective To investigate the potential value of the cuproptosis gene LIPT1 in the diagnosis and prognosis of glioma. Methods The expression of 10 cuproptosis genes in glioma was mined using TCGA database. The expression of LIPT1 in 18 glioma patients was detected by IHC and qRT-PCR. The expression of LIPT1 in HEB, U87 and U251 cells was detected by qRT-PCR. ROC curve was used to predict the diagnostic efficacy of LIPT1 in glioma. Kaplan-Meier survival curve and Cox analysis were used to explore the prognostic value of LIPT1. ssGSEA database was used to evaluate the effect of LIPT1 on immune invasion of glioma. To explore the relationship between LIPT1 and immunological checkpoints, DNA methylation site and DNA mismatch repair genes in gliomas, fFunctional enrichment of LIPT1-associated genes was performed with GO, KEGG, and GSEA. Animal TFDB3 predicts LIPT1-associated transcription factors. The GSCA database was utilized to predict potential drugs targeting LIPT1 and hub genes. Results LIPT1 expression in glioma was higher than in normal tissues (3.03 vs. 2.28, P<0.05). The immune scores of grade Ⅱ, Ⅲ and Ⅳ gliomas were different [(1.47±0.51) vs. (4.94±1.25) vs. (9.88±1.65), P<0.05]. The mRNA levels of LIPT1 in HEB, U87 and U251 cells were different [(0.22±0.03) vs. (0.45±0.04) vs. (0.58±0.05), P<0.05]. The AUC value of LIPT1 in glioma diagnosis was 0.87. The OS, DSS, and PFI in the high LIPT1 expression group were shorter than those in the low LIPT1 expression group (P<0.05). The LIPT1 expression was correlated with immune infiltration changes and the expression of immune checkpoint, DNA methylation site and DNA mismatch repair gene (P<0.05). Transcription factors such as FOXG1 were predicted to possibly bind to LIPT1. Drugs such as docetaxel might treat gliomas by targeting and regulating LIPT1-related hub genes. Conclusions LIPT1 may be a potential biomarker and may be associated with poor prognosis of glioma.
林彦琛, 徐葳, 英草, 刘瑞, 张玉钰, 代二庆, 李晶晶. 铜死亡基因LIPT1在脑胶质瘤诊断及预后判断中的价值[J]. 武警医学, 2024, 35(4): 277-285.
LIN Yanchen, XU Wei, YING Cao, LIU Rui, ZHANG Yuyu, DAI Erqing, LI Jingjing. Value of cuproptosis gene LIPT1 in diagnosis and prognosis of brain gliomas. Med. J. Chin. Peop. Armed Poli. Forc., 2024, 35(4): 277-285.
Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins[J]. Science, 2022, 375(6586):1254-1261.
[1]
Nicholson J G, Fine H A. Diffuse glioma heterogeneity and its therapeutic implications[J]. Cancer Discov, 2021, 11(3):575-590.
[2]
Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins[J]. Science, 2022, 375(6586):1254-1261.
[3]
Zhou J, Chen D, Zhang S, et al. Identification of two molecular subtypes and a novel prognostic model of lung adenocarcinoma based on a cuproptosis-associated gene signature[J]. Front Genet, 2023, 13(1039983):1-14.
[3]
Zhou J, Chen D, Zhang S, et al. Identification of two molecular subtypes and a novel prognostic model of lung adenocarcinoma based on a cuproptosis-associated gene signature[J]. Front Genet, 2023, 13(1039983):1-14.
[4]
Yang S, Zhao J, Cui X, et al. TCA-phospholipid-glycolysis targeted triple therapy effectively suppresses ATP production and tumor growth in glioblastoma[J]. Theranostics, 2022, 12(16):7032-7050.
[5]
Ni M, Solmonson A, Pan C, et al. Functional assessment of lipoyltransferase-1 deficiency in cells, mice, and humans[J]. Cell Rep, 2019, 27(5):1376-1386.e6.
[4]
Yang S, Zhao J, Cui X, et al. TCA-phospholipid-glycolysis targeted triple therapy effectively suppresses ATP production and tumor growth in glioblastoma[J]. Theranostics, 2022, 12(16):7032-7050.
[6]
Yan C, Niu Y, Ma L, et al. System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma[J]. J Transl Med, 2022, 20(1): 452-470.
[5]
Ni M, Solmonson A, Pan C, et al. Functional assessment of lipoyltransferase-1 deficiency in cells, mice, and humans[J]. Cell Rep, 2019, 27(5):1376-1386.e6.
[7]
Bindea G, Mlecnik B, Tosolini M, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer[J]. Immunity, 2013, 39(4):782-795.
[6]
Yan C, Niu Y, Ma L, et al. System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma[J]. J Transl Med, 2022, 20(1): 452-470.
[8]
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data[J]. BMC Bioinform, 2013, 14(7):1-15.
[7]
Bindea G, Mlecnik B, Tosolini M, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer[J]. Immunity, 2013, 39(4):782-795.
[9]
Wang Y, Zhang H, Liu C, et al. Immune checkpoint modulators in cancer immunotherapy: recent advances and emerging concepts[J]. J Hematol Oncol, 2022, 15(1): 1-53.
[8]
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data[J]. BMC Bioinform, 2013, 14(7):1-15.
[10]
Nabais M F, Gadd D A, Hannon E, et al. An overview of DNA methylation-derived trait score methods and applications[J]. Genome Biol, 2023, 24(1): 1-23.
[9]
Wang Y, Zhang H, Liu C, et al. Immune checkpoint modulators in cancer immunotherapy: recent advances and emerging concepts[J]. J Hematol Oncol, 2022, 15(1): 1-53.
[11]
Chien C H, Hsueh W T, Chuang J Y, et al. Dissecting the mechanism of temozolomide resistance and its association with the regulatory roles of intracellular reactive oxygen species in glioblastoma[J]. J Biomed Sci, 2021, 28(18): 1-10.
[10]
Nabais M F, Gadd D A, Hannon E, et al. An overview of DNA methylation-derived trait score methods and applications[J]. Genome Biol, 2023, 24(1): 1-23.
[12]
Chen L, Zhang Y H, Lu G, et al. Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways[J]. Artif Intell Med, 2017, 76: 27-36.
[11]
Chien C H, Hsueh W T, Chuang J Y, et al. Dissecting the mechanism of temozolomide resistance and its association with the regulatory roles of intracellular reactive oxygen species in glioblastoma[J]. J Biomed Sci, 2021, 28(18): 1-10.
[13]
Yu G, Wang L G, Han Y, et al. Cluster profiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
[12]
Chen L, Zhang Y H, Lu G, et al. Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways[J]. Artif Intell Med, 2017, 76: 27-36.
[14]
Szklarczyk D, Morris J H, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible[J]. Nucleic Acids Res, 2017, 45(D1):D362-D368.
[13]
Yu G, Wang L G, Han Y, et al. Cluster profiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
[15]
Yang W, Soares J, Greninger P, et al. Genomics of drug sensitivity in cancer GDSC;: a resource for therapeutic biomarker discovery in cancer cells[J]. Nucleic Acids Res, 2013, 41(Database issue):D955-961.
[14]
Szklarczyk D, Morris J H, Cook H, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible[J]. Nucleic Acids Res, 2017, 45(D1):D362-D368.
[16]
Rooj A K, McNicholas C M, Bartoszewski R, et al. Glioma-specific cation conductance regulates migration and cell cycle progression[J]. J Biol Chem, 2012, 287(6):4053-4065.
[15]
Yang W, Soares J, Greninger P, et al. Genomics of drug sensitivity in cancer GDSC;: a resource for therapeutic biomarker discovery in cancer cells[J]. Nucleic Acids Res, 2013, 41(Database issue):D955-961.
[16]
Rooj A K, McNicholas C M, Bartoszewski R, et al. Glioma-specific cation conductance regulates migration and cell cycle progression[J]. J Biol Chem, 2012, 287(6):4053-4065.
[17]
Lin C, Chen J, Su Z, et al. A calcium-related immune signature in prognosis prediction of patients with glioma[J]. Front Cell Dev Biol, 2021, 9(723103):1-17.
[18]
Yan Y, Jiang Y. RACK1 affects glioma cell growth and differentiation through the CNTN2-mediated RTK/Ras/MAPK pathway[J]. Int J Mol Med, 2016, 37(1): 251-257.
[17]
Lin C, Chen J, Su Z, et al. A calcium-related immune signature in prognosis prediction of patients with glioma[J]. Front Cell Dev Biol, 2021, 9(723103):1-17.
[19]
Hambardzumyan D, Gutmann D H, Kettenmann H. The role of microglia and macrophages in glioma maintenance and progression[J]. Nat Neurosci, 2016, 19(1): 20-27.
[18]
Yan Y, Jiang Y. RACK1 affects glioma cell growth and differentiation through the CNTN2-mediated RTK/Ras/MAPK pathway[J]. Int J Mol Med, 2016, 37(1): 251-257.
[20]
Guo X, Qiu W, Wang J, et al. Glioma exosomes mediate the expansion and function of myeloid-derived suppressor cells through microRNA-29a/Hbp1 and microRNA-92a/Prkar1a pathways[J]. Int J Cancer, 2019, 144(12): 3111-3126.
[19]
Hambardzumyan D, Gutmann D H, Kettenmann H. The role of microglia and macrophages in glioma maintenance and progression[J]. Nat Neurosci, 2016, 19(1): 20-27.
[21]
Shyr C, Blackford A L, Huang T, et al. A validation of models for prediction of pathogenic variants in mismatch repair genes[J]. Genet Med, 2022, 24(10):2155-2166.
[20]
Guo X, Qiu W, Wang J, et al. Glioma exosomes mediate the expansion and function of myeloid-derived suppressor cells through microRNA-29a/Hbp1 and microRNA-92a/Prkar1a pathways[J]. Int J Cancer, 2019, 144(12): 3111-3126.
[22]
Deris Zayeri Z, Tahmasebi Birgani M, Mohammadi Asl J, et al. A novel infram deletion in MSH6 gene in glioma: conversation on MSH6 mutations in brain tumors[J]. J Cell Physiol, 2019, 234(7):11092-11102.
[21]
Shyr C, Blackford A L, Huang T, et al. A validation of models for prediction of pathogenic variants in mismatch repair genes[J]. Genet Med, 2022, 24(10):2155-2166.
[23]
Zhao J, Zhang L, Zheng L, et al. LncRNATCF7 promotes the growth and self-renewal of glioma cells via suppressing the miR-200c-EpCAM axis[J]. Biomed Pharmacother, 2018, 97: 203-208.
[22]
Deris Zayeri Z, Tahmasebi Birgani M, Mohammadi Asl J, et al. A novel infram deletion in MSH6 gene in glioma: conversation on MSH6 mutations in brain tumors[J]. J Cell Physiol, 2019, 234(7):11092-11102.
[24]
Greenberg M V C, Bourc'his D. The diverse roles of DNA methylation in mammalian development and disease[J]. Nat Rev Mol Cell Biol, 2019, 20(10): 590-607.
[23]
Zhao J, Zhang L, Zheng L, et al. LncRNATCF7 promotes the growth and self-renewal of glioma cells via suppressing the miR-200c-EpCAM axis[J]. Biomed Pharmacother, 2018, 97: 203-208.
[24]
Greenberg M V C, Bourc'his D. The diverse roles of DNA methylation in mammalian development and disease[J]. Nat Rev Mol Cell Biol, 2019, 20(10): 590-607.
[25]
Zhang M, Song J, Yuan W, et al. Roles of RNA methylation on tumor immunity and clinical implications[J]. Front Immunol, 2021, 12(641507):1-13.
[25]
Zhang M, Song J, Yuan W, et al. Roles of RNA methylation on tumor immunity and clinical implications[J]. Front Immunol, 2021, 12(641507):1-13.
[26]
Liu H. Pan-cancer profiles of the cuproptosis gene set[J]. Am J Cancer Res, 2022, 12(8):4074-4081.
[27]
Wang H, Zeng Z, Yi R, et al. MicroRNA-3200-3p targeting CAMK2A modulates the proliferation and metastasis of glioma in vitro[J]. Bioengineered, 2022, 13(3):7785-7797.
[26]
Liu H. Pan-cancer profiles of the cuproptosis gene set[J]. Am J Cancer Res, 2022, 12(8):4074-4081.
[27]
Wang H, Zeng Z, Yi R, et al. MicroRNA-3200-3p targeting CAMK2A modulates the proliferation and metastasis of glioma in vitro[J]. Bioengineered, 2022, 13(3):7785-7797.
[28]
Szczepaniak J, Jagiello J, Wierzbicki M, et al. Reduced graphene oxides modulate the expression of cell receptors and voltage-dependent ion channel genes of glioblastoma multiforme[J]. Int J Mol Sci, 2021, 22(2):1-18.
[29]
Chen J, Wu X, Xing Z, et al. FOXG1 expression is elevated in glioma and inhibits glioma cell apoptosis[J]. J Cancer, 2018, 9(5):778-783.
[28]
Szczepaniak J, Jagiello J, Wierzbicki M, et al. Reduced graphene oxides modulate the expression of cell receptors and voltage-dependent ion channel genes of glioblastoma multiforme[J]. Int J Mol Sci, 2021, 22(2):1-18.
[29]
Chen J, Wu X, Xing Z, et al. FOXG1 expression is elevated in glioma and inhibits glioma cell apoptosis[J]. J Cancer, 2018, 9(5):778-783.
[30]
Luiz M T, Viegas J S, Abriata J P, et al. Docetaxel-loaded folate-modified TPGS-transfersomes for glioblastoma multiforme treatment[J]. Mater Sci Eng C Mater Biol Appl, 2021, 124(112033):1-11.
[31]
Saw P E, Xu X, Kang B R, et al. Extra-domain B of fibronectin as an alternative target for drug delivery and a cancer diagnostic and prognostic biomarker for malignant glioma[J]. Theranostics, 2021, 11(2): 941-957.
[30]
Luiz M T, Viegas J S, Abriata J P, et al. Docetaxel-loaded folate-modified TPGS-transfersomes for glioblastoma multiforme treatment[J]. Mater Sci Eng C Mater Biol Appl, 2021, 124(112033):1-11.
[31]
Saw P E, Xu X, Kang B R, et al. Extra-domain B of fibronectin as an alternative target for drug delivery and a cancer diagnostic and prognostic biomarker for malignant glioma[J]. Theranostics, 2021, 11(2): 941-957.