Expression and clinical significance of geranylgeranyl diphosphate synthase1 (GGPS1) in lung squamous cell carcinoma

doi:10.3969/j.issn.1674-8115.2024.03.003

Abstract

Abstract:

Objective ·To investigate the expression and clinical significance of geranylgeranyl diphosphate synthase1 (GGPS1) in lung squamous cell carcinoma (LUSC) by bioinformatics and immunohistochemistry. Methods ·Firstly, the transcriptome data of LUSC tissues and paired normal tissues were downloaded from UCSC Xena platform. The data were standardized and differentially expressed by R language, and verified by UALCAN database. UALCAN and LinkedOmics databases were used to analyze the relationship between GGPS1 expression and clinicopathological features in LUSC patients. The Kaplan-Meier Plotter database was used to explore the effect of GGPS1 expression on prognosis in LUSC patients. The least absolute shrinkage and selection operator (LASSO) regression analyses were applied to screen gene correlation coefficients and risk scores. The diagnostic value of GGPS1 for LUSC was evaluated by nomogram and calibration curve. The protein-protein interaction (PPI) network of GGPS1 was constructed by using STRING and GeneMANIA databases. R language was used to select the differential genes related to GGPS1, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. The expression of GGPS1 in LUSC patients was detected by immunohistochemistry, and its correlation with clinicopathological features and prognosis was analyzed. Results ·Through the TIMER2.0 database, it was found that GGPS1 expression was increased in most tumors and was highly expressed in LUSC. The expression of GGPS1 in LUSC was higher than that in adjacent tissues in UCSC Xena and UALCAN databases (both P<0.05). It was found that the expression level of GGPS1 was higher in patients with late stage in UALCAN and LinkedOmics databases, and the overall survival (OS) of LUSC patients with high expression of GGPS1 was shorter (P<0.05) in the Kaplan-Meier Plotter database. Assessment of LUSC patients based on LASSO regression had good risk prediction efficacy. Constructing an individualised prediction model for LUSC patients has the best prediction accuracy. The results of GO and KEGG showed that GGPS1-related genes were mainly related to protein metabolism, regulation of lipid and cholesterol metabolism, nicotine addiction, phosphatidylinositol3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR), peroxisome proliferator-activated receptors (PPAR) signaling pathway and so on. The metabolic function of GGPS1 may promote tumorigenesis. The results of immunohistochemistry showed that GGPS1 was mainly located in the cytoplasm, and the expression of GGPS1 in LUSC tissues was higher than that in adjacent tissues (P<0.05). The high expression of GGPS1 was related to tumor size, lymph node metastasis and TNM stage of LUSC patients (all P<0.05), and the OS of patients with high expression of GGPS1 was significantly shorter than that of patients with low expression (P=0.000). Multivariate Cox regression analysis suggested that GGPS 1 could be used as an independent prognostic factor for LUSC. Conclusion ·Compared with normal lung tissue, the expression of GGPS1 in LUSC is significantly increased, especially in patients with large tumor volume, positive lymph node metastasis and advanced stage. GGPS1 overexpression is an independent predictor of poor prognosis in LUSC patients. GGPS1 is expected to become a new molecular target for the diagnosis, treatment and prevention of LUSC.

Key words: geranylgeranyl diphosphate synthase 1 (GGPS1), lung squamous cell carcinoma (LUSC), bioinformatics, immunohistochemistry, clinical significance

CLC Number:

R734.2

WANG Xin, WANG Xiaoxia, LI Yanqing, ZHENG Yongxin, WU Jie, REN Meng, JIA Xiangdong, XU Tianxiang. Expression and clinical significance of geranylgeranyl diphosphate synthase1 (GGPS1) in lung squamous cell carcinoma[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(3): 312-324.

Figures/Tables 13

TrendMD

References 29

1	SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249.
2	国家卫生健康委办公厅. 原发性肺癌诊疗指南(2022年版)[J]. 协和医学杂志, 2022, 13(4): 549-570.
	General Office of National Health Commission. Guidelines for the diagnosis and treatment of primary lung cancer (2022 edition)[J]. Union Medical Journal, 2022, 13(4): 549-570.
3	LAZZARI C, KARACHALIOU N, GREGORC V, et al. Second-line therapy of squamous non-small cell lung cancer: an evolving landscape[J]. Expert Rev Respir Med, 2017, 11(6): 469-479.
4	ERICSSON J, GREENE J M, CARTER K C, et al. Human geranylgeranyl diphosphate synthase: isolation of the cDNA, chromosomal mapping and tissue expression[J]. J Lipid Res, 1998, 39(9): 1731-1739.
5	KUZUGUCHI T, MORITA Y, SAGAMI I, et al. Human geranylgeranyl diphosphate synthase. cDNA cloning and expression[J]. J Biol Chem, 1999, 274(9): 5888-5894.
6	王鑫, 许天祥, 李彦庆等. GGPPS及其抑制剂在肿瘤中的研究进展[J]. 生命的化学, 2023, 43(6): 831-840.
	WANG X, XU T X, LI Y Q, et al. Research progress of GGPPS and its inhibitors in tumors[J]. Chemistry of Life, 2023, 43(6): 831-840.
7	LI T W, FU J X, ZENG Z X, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells[J]. Nucleic Acids Res, 2020, 48(W1): W509-W514.
8	CHANDRASHEKAR D S, KARTHIKEYAN S K, KORLA P K, et al. UALCAN: an update to the integrated cancer data analysis platform[J]. Neoplasia, 2022, 25: 18-27.
9	NAGY Á, LÁNCZKY A, MENYHÁRT O, et al. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets[J]. Sci Rep, 2018, 8(1): 9227.
10	SZKLARCZYK D, KIRSCH R, KOUTROULI M, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest[J]. Nucleic Acids Res, 2023, 51(D1): D638-D646.
11	FRANZ M, RODRIGUEZ H, LOPES C, et al. GeneMANIA update 2018[J]. Nucleic Acids Res, 2018, 46(W1): W60-W64.
12	BADE B C, DELA CRUZ C S. Lung cancer 2020: epidemiology, etiology, and prevention[J]. Clin Chest Med, 2020, 41(1): 1-24.
13	MANASWIYOUNGKUL P, DE ARAUJO E D, GUNNING P T. Targeting prenylation inhibition through the mevalonate pathway[J]. RSC Med Chem, 2020, 11(1): 51-71.
14	KAIYRZHANOV R, PERRY L, ROCCA C, et al. GGPS1-associated muscular dystrophy with and without hearing loss[J]. Ann Clin Transl Neurol, 2022, 9(9): 1465-1474.
15	YU D C, LIU J, CHEN J, et al. GGPPS1 predicts the biological character of hepatocellular carcinoma in patients with cirrhosis[J]. BMC Cancer, 2014, 14: 248.
16	ZHAO D D, YUAN J, CHENG Q, et al. Evidence for a role of geranylgeranylation in renal angiomyolipoma and renal epithelioid angiomyolipoma[J]. Oncol Lett, 2019, 17(2): 1523-1530.
17	WEISSENRIEDER J S, REILLY J E, NEIGHBORS J D, et al. Inhibiting geranylgeranyl diphosphate synthesis reduces nuclear androgen receptor signaling and neuroendocrine differentiation in prostate cancer cell models[J]. Prostate, 2019, 79(1): 21-30.
18	HUANG K, HAN L, XU H M, et al. The prognostic role and metabolic function of GGPS1 in oral squamous cell carcinoma[J]. Front Mol Biosci, 2023, 10: 1109403.
19	HANEY S L, VARNEY M L, CHHONKER Y S, et al. Inhibition of geranylgeranyl diphosphate synthase is a novel therapeutic strategy for pancreatic ductal adenocarcinoma[J]. Oncogene, 2019, 38(26): 5308-5320.
20	HANEY S L, VARNEY M L, WILLIAMS J T, et al. Geranylgeranyl diphosphate synthase inhibitor and proteasome inhibitor combination therapy in multiple myeloma[J]. Exp Hematol Oncol, 2022, 11(1): 5.
21	HANEY S L, FENG D, CHHONKER Y S, et al. Evaluation of geranylgeranyl diphosphate synthase inhibition as a novel strategy for the treatment of osteosarcoma and Ewing sarcoma[J]. Drug Dev Res, 2023, 84(1): 62-74.
22	WANG X X, XU W J, ZHAN P, et al. Overexpression of geranylgeranyl diphosphate synthase contributes to tumour metastasis and correlates with poor prognosis of lung adenocarcinoma[J]. J Cell Mol Med, 2018, 22(4): 2177-2189.
23	BIAN X L, LIU R, MENG Y, et al. Lipid metabolism and cancer[J]. J Exp Med, 2021, 218(1): e20201606.
24	ZHANG C J, ZHU N, LI H F, et al. New dawn for cancer cell death: emerging role of lipid metabolism[J]. Mol Metab, 2022, 63: 101529.
25	胡婵婵, 范毅, 徐源, 等. 脂质代谢在肺癌发生发展及诊疗领域中的研究进展[J]. 上海交通大学学报(医学版), 2022, 42(12): 1766-1771.
	HU C C, FAN Y, XU Y, et al. Lipid metabolism and lung cancer: emerging roles in occurrence, progression, diagnosis and treatment[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2022, 42(12): 1766-1771.
26	ERSHOV P, KALUZHSKIY L, MEZENTSEV Y, et al. Enzymes in the cholesterol synthesis pathway: interactomics in the cancer context[J]. Biomedicines, 2021, 9(8): 895.
27	XU H, WANG Q S, SUN Q, et al. In type 2 diabetes induced by cigarette smoking, activation of p38 MAPK is involved in pancreatic β-cell apoptosis[J]. Environ Sci Pollut Res Int, 2018, 25(10): 9817-9827.
28	SHEN N, GONG T, WANG J D, et al. Cigarette smoke-induced pulmonary inflammatory responses are mediated by EGR-1/GGPPS/MAPK signaling[J]. Am J Pathol, 2011, 178(1): 110-118.
29	CHANG W H, LAI A G. The pan-cancer mutational landscape of the PPAR pathway reveals universal patterns of dysregulated metabolism and interactions with tumor immunity and hypoxia[J]. Ann N Y Acad Sci, 2019, 1448(1): 65-82.

Expression	LUSC (n=90)	Normal (n=90)	χ²	P value
GGPS1/n(%)			8.193	0.004
Low	42 (46.7)	61 (67.8)
High	48 (53.3)	29 (32.2)

Expression	LUSC (n=90)	Normal (n=90)	χ²	P value
GGPS1/n(%)			8.193	0.004
Low	42 (46.7)	61 (67.8)
High	48 (53.3)	29 (32.2)

Characteristic	Total/n	GGPS1/n(%)		χ² value	P value
Characteristic	Total/n	Low expression	High expression	χ² value	P value
Gender				1.213	0.271
Male	84	41 (48.8)	43 (51.2)
Female	6	1 (16.7)	5 (83.3)
Age				0.003	0.955
≤64 years old	49	23 (46.9)	26 (53.1)
>64 years old	41	19 (46.3)	22 (53.7)
History of smoking				2.691	0.101
No	41	23 (56.1)	18 (43.9)
Yes	49	19 (38.8)	30 (61.2)
Tumor location				0.984	0.321
Left	40	21 (52.5)	19 (47.5)
Right	50	21 (42.0)	29 (58.0)
Tumor size				10.247	0.001
≤3 cm	26	19 (73.1)	7 (26.9)
>3 cm	64	23 (35.9)	41 (64.1)
Lymph node metastasis				7.626	0.006
No	46	28 (60.9)	18 (39.1)
Yes	44	14 (31.8)	30 (68.2)
TNM stage				8.935	0.003
Ⅰ~Ⅱ	47	29 (61.7)	18 (38.3)
Ⅲ~Ⅳ	43	13 (30.2)	30 (69.8)

Characteristic	Total/n	GGPS1/n(%)		χ² value	P value
Characteristic	Total/n	Low expression	High expression	χ² value	P value
Gender				1.213	0.271
Male	84	41 (48.8)	43 (51.2)
Female	6	1 (16.7)	5 (83.3)
Age				0.003	0.955
≤64 years old	49	23 (46.9)	26 (53.1)
>64 years old	41	19 (46.3)	22 (53.7)
History of smoking				2.691	0.101
No	41	23 (56.1)	18 (43.9)
Yes	49	19 (38.8)	30 (61.2)
Tumor location				0.984	0.321
Left	40	21 (52.5)	19 (47.5)
Right	50	21 (42.0)	29 (58.0)
Tumor size				10.247	0.001
≤3 cm	26	19 (73.1)	7 (26.9)
>3 cm	64	23 (35.9)	41 (64.1)
Lymph node metastasis				7.626	0.006
No	46	28 (60.9)	18 (39.1)
Yes	44	14 (31.8)	30 (68.2)
TNM stage				8.935	0.003
Ⅰ~Ⅱ	47	29 (61.7)	18 (38.3)
Ⅲ~Ⅳ	43	13 (30.2)	30 (69.8)

Variable	Univariate analysis			Multivariate analysis
Variable	HR	95%CI	P value	HR	95%CI	P value
Gender	0.652	0.232‒1.837	0.419
Age	0.631	0.336‒1.185	0.152
History of smoking	2.054	1.054‒4.003	0.034	2.045	1.024‒4.086	0.043
Tumor location	0.878	0.468‒1.648	0.686
Tumor size	2.751	1.151‒6.574	0.023	1.621	0.639‒4.114	0.309
Lymph node metastasis	3.116	1.577‒6.157	0.001	4.039	1.257‒12.983	0.019
TNM stage	2.399	1.246‒4.619	0.009	0.473	0.147‒1.523	0.210
GGPS1 expression	3.872	1.834‒8.176	0.000	2.867	1.316‒6.242	0.008