中国药学杂志

Component	t_R /min	m/z (Q1)	Optimization of MS parameters
Citric acid	1.098	190.9	70	111.0¹⁾	15	87.0	15
cis-Aconitic acid	1.996	172.9	70	129.0¹⁾	2	85.1	5
Isocitric acid	0.819	190.9	70	111.0	10	85.1¹⁾	10
α-Ketoglutaric acid	1.557	145.0	60	101.0 ¹⁾	2	57.2	5
Succinic acid	1.298	117.0	70	99¹⁾	5	73.1	5
Fumaric acid	1.777	115.0	60	71.1¹⁾	2
Malic acid	0.680	133.0	70	115.0¹⁾	5	71.1	10
Oxaloacetic acid	0.498	131.0	60	87.0 ¹⁾	2	43.2	10
Acetaminophen (internal standard)	2.482	150.0	110	107.0	30	118.0	15

Component

t_R
/min

m/z
(Q1)

Optimization of MS parameters

Fragmentation/V

m/z(Q3¹)

CE¹⁾/eV

m/z(Q3²)

CE²⁾/eV

Citric acid

1.098

190.9

111.0¹⁾

87.0

cis-Aconitic acid

1.996

172.9

129.0¹⁾

85.1

Isocitric acid

0.819

190.9

111.0

85.1¹⁾

α-Ketoglutaric acid

1.557

145.0

101.0 ¹⁾

57.2

Succinic acid

1.298

117.0

99¹⁾

73.1

Fumaric acid

1.777

115.0

71.1¹⁾

Malic acid

0.680

133.0

115.0¹⁾

71.1

Oxaloacetic acid

0.498

131.0

87.0 ¹⁾

43.2

Acetaminophen (internal standard)

2.482

150.0

110

107.0

118.0

Component	t_R /min	m/z (Q1)	Optimization of MS parameters
Citric acid	1.098	190.9	70	111.0¹⁾	15	87.0	15
cis-Aconitic acid	1.996	172.9	70	129.0¹⁾	2	85.1	5
Isocitric acid	0.819	190.9	70	111.0	10	85.1¹⁾	10
α-Ketoglutaric acid	1.557	145.0	60	101.0 ¹⁾	2	57.2	5
Succinic acid	1.298	117.0	70	99¹⁾	5	73.1	5
Fumaric acid	1.777	115.0	60	71.1¹⁾	2
Malic acid	0.680	133.0	70	115.0¹⁾	5	71.1	10
Oxaloacetic acid	0.498	131.0	60	87.0 ¹⁾	2	43.2	10
Acetaminophen (internal standard)	2.482	150.0	110	107.0	30	118.0	15

Component

t_R
/min

m/z
(Q1)

Optimization of MS parameters

Fragmentation/V

m/z(Q3¹)

CE¹⁾/eV

m/z(Q3²)

CE²⁾/eV

Citric acid

1.098

190.9

111.0¹⁾

87.0

cis-Aconitic acid

1.996

172.9

129.0¹⁾

85.1

Isocitric acid

0.819

190.9

111.0

85.1¹⁾

α-Ketoglutaric acid

1.557

145.0

101.0 ¹⁾

57.2

Succinic acid

1.298

117.0

99¹⁾

73.1

Fumaric acid

1.777

115.0

71.1¹⁾

Malic acid

0.680

133.0

115.0¹⁾

71.1

Oxaloacetic acid

0.498

131.0

87.0 ¹⁾

43.2

Acetaminophen (internal standard)

2.482

150.0

110

107.0

118.0

Component	Regression equation	Linear range /μmol·L^-1	r	LOD /μmol·L^-1	LLOQ /μmol·L^-1	QC /μmol·L^-1	Accuracy /%	Precision
Citric acid	y=0.100 4x-0.025 63	0.5-125	0.997 3	0.1	0.5	0.5	94.57	12.72	12.32
						1.5	103.53	8.23	8.41
						50	97.37	5.08	5.15
						100	101.03	4.72	4.77
cis-Aconitic acid	y=0.100 2x-0.049 86	0.5-250	0.994 2	0.1	0.5	0.5	95.19	10.76	11.85
						1.5	96.98	9.74	10.42
						100	103.55	9.03	9.11
						200	98.42	7.56	8.44
Isocitric acid	y=0.001 615x-0.004 234	5-250	0.994 7	0.5	5	5	104.85	9.41	11.47
						15	103.9	7.42	7.31
						100	102.69	6.06	6.15
						200	98.52	5.10	5.77
α-Ketoglutaric acid	y=0.005 797x-0.000 195 4	1-150	0.999 1	0.2	1	1	94.18	10.73	11.55
						3	95.81	9.09	10.67
						50	102.27	7.79	8.71
						120	97.17	5.78	6.12
Succinic acid	y=0.009 153x-0.000 452 4	0.1-75	0.998 8	0.02	0.1	0.1	98.64	9.25	9.76
						0.3	99.70	8.06	8.62
						30	102.41	6.71	7.02
						60	100.82	4.07	5.14
Fumaric acid	y=0.002 386x+0.002 617	0.5-150	0.9995	0.1	0.5	0.5	94.10	6.98	7.49
						1.5	98.24	6.77	7.11
						50	101.30	5.92	6.35
						120	102.92	4.49	5.22
Malic acid	y=0.085 06x+0.004 496	0.5-150	0.996 9	0.05	0.5	0.5	97.23	8.05	8.19
						1.5	101.82	6.17	6.26
						50	99.38	5.41	5.97
						120	99.09	4.15	4.97

Component

Regression
equation

Linear range
/μmol·L^-1

LOD
/μmol·L^-1

LLOQ
/μmol·L^-1

QC
/μmol·L^-1

Accuracy
/%

Precision

Intra-day CV/%

Inter-day CV/%

Citric acid

y=0.100 4x-0.025 63

0.5-125

0.997 3

0.1

0.5

94.57

12.72

12.32

1.5

103.53

8.23

8.41

97.37

5.08

5.15

100

101.03

4.72

4.77

cis-Aconitic acid

y=0.100 2x-0.049 86

0.5-250

0.994 2

0.1

0.5

95.19

10.76

11.85

1.5

96.98

9.74

10.42

100

103.55

9.03

9.11

200

98.42

7.56

8.44

Isocitric acid

y=0.001 615x-0.004 234

5-250

0.994 7

0.5

104.85

9.41

11.47

103.9

7.42

7.31

100

102.69

6.06

6.15

200

98.52

5.10

5.77

α-Ketoglutaric acid

y=0.005 797x-0.000 195 4

1-150

0.999 1

0.2

94.18

10.73

11.55

95.81

9.09

10.67

102.27

7.79

8.71

120

97.17

5.78

6.12

Succinic acid

y=0.009 153x-0.000 452 4

0.1-75

0.998 8

0.02

0.1

98.64

9.25

9.76

0.3

99.70

8.06

8.62

102.41

6.71

7.02

100.82

4.07

5.14

Fumaric acid

y=0.002 386x+0.002 617

0.5-150

0.9995

0.1

0.5

94.10

6.98

7.49

1.5

98.24

6.77

7.11

101.30

5.92

6.35

120

102.92

4.49

5.22

Malic acid

y=0.085 06x+0.004 496

0.5-150

0.996 9

0.05

0.5

97.23

8.05

8.19

1.5

101.82

6.17

6.26

99.38

5.41

5.97

120

99.09

4.15

4.97

Component	Regression equation	Linear range /μmol·L^-1	r	LOD /μmol·L^-1	LLOQ /μmol·L^-1	QC /μmol·L^-1	Accuracy /%	Precision
Citric acid	y=0.100 4x-0.025 63	0.5-125	0.997 3	0.1	0.5	0.5	94.57	12.72	12.32
						1.5	103.53	8.23	8.41
						50	97.37	5.08	5.15
						100	101.03	4.72	4.77
cis-Aconitic acid	y=0.100 2x-0.049 86	0.5-250	0.994 2	0.1	0.5	0.5	95.19	10.76	11.85
						1.5	96.98	9.74	10.42
						100	103.55	9.03	9.11
						200	98.42	7.56	8.44
Isocitric acid	y=0.001 615x-0.004 234	5-250	0.994 7	0.5	5	5	104.85	9.41	11.47
						15	103.9	7.42	7.31
						100	102.69	6.06	6.15
						200	98.52	5.10	5.77
α-Ketoglutaric acid	y=0.005 797x-0.000 195 4	1-150	0.999 1	0.2	1	1	94.18	10.73	11.55
						3	95.81	9.09	10.67
						50	102.27	7.79	8.71
						120	97.17	5.78	6.12
Succinic acid	y=0.009 153x-0.000 452 4	0.1-75	0.998 8	0.02	0.1	0.1	98.64	9.25	9.76
						0.3	99.70	8.06	8.62
						30	102.41	6.71	7.02
						60	100.82	4.07	5.14
Fumaric acid	y=0.002 386x+0.002 617	0.5-150	0.9995	0.1	0.5	0.5	94.10	6.98	7.49
						1.5	98.24	6.77	7.11
						50	101.30	5.92	6.35
						120	102.92	4.49	5.22
Malic acid	y=0.085 06x+0.004 496	0.5-150	0.996 9	0.05	0.5	0.5	97.23	8.05	8.19
						1.5	101.82	6.17	6.26
						50	99.38	5.41	5.97
						120	99.09	4.15	4.97

Component

Regression
equation

Linear range
/μmol·L^-1

LOD
/μmol·L^-1

LLOQ
/μmol·L^-1

QC
/μmol·L^-1

Accuracy
/%

Precision

Intra-day CV/%

Inter-day CV/%

Citric acid

y=0.100 4x-0.025 63

0.5-125

0.997 3

0.1

0.5

94.57

12.72

12.32

1.5

103.53

8.23

8.41

97.37

5.08

5.15

100

101.03

4.72

4.77

cis-Aconitic acid

y=0.100 2x-0.049 86

0.5-250

0.994 2

0.1

0.5

95.19

10.76

11.85

1.5

96.98

9.74

10.42

100

103.55

9.03

9.11

200

98.42

7.56

8.44

Isocitric acid

y=0.001 615x-0.004 234

5-250

0.994 7

0.5

104.85

9.41

11.47

103.9

7.42

7.31

100

102.69

6.06

6.15

200

98.52

5.10

5.77

α-Ketoglutaric acid

y=0.005 797x-0.000 195 4

1-150

0.999 1

0.2

94.18

10.73

11.55

95.81

9.09

10.67

102.27

7.79

8.71

120

97.17

5.78

6.12

Succinic acid

y=0.009 153x-0.000 452 4

0.1-75

0.998 8

0.02

0.1

98.64

9.25

9.76

0.3

99.70

8.06

8.62

102.41

6.71

7.02

100.82

4.07

5.14

Fumaric acid

y=0.002 386x+0.002 617

0.5-150

0.9995

0.1

0.5

94.10

6.98

7.49

1.5

98.24

6.77

7.11

101.30

5.92

6.35

120

102.92

4.49

5.22

Malic acid

y=0.085 06x+0.004 496

0.5-150

0.996 9

0.05

0.5

97.23

8.05

8.19

1.5

101.82

6.17

6.26

99.38

5.41

5.97

120

99.09

4.15

4.97

基于三羧酸循环相关代谢物的靶向代谢组学探究HER2阳性乳腺癌曲妥珠单抗耐药生物标志物

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魏棒棒 ¹ , 曹怿玮 ¹ , 董春涛 ¹ , 李德邦 ¹ , 许飞飞 ² , 司鑫鑫 ¹^,^* , 陈芸 ²

中国药学杂志 | 论著 2024,59(4): 302-310

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中国药学杂志 | 论著 2024, 59(4): 302-310

基于三羧酸循环相关代谢物的靶向代谢组学探究HER2阳性乳腺癌曲妥珠单抗耐药生物标志物

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魏棒棒¹, 曹怿玮¹, 董春涛¹, 李德邦¹, 许飞飞², 司鑫鑫¹^,^*, 陈芸²

作者信息

¹ 江苏海洋大学药学院, 江苏连云港 222005

² 南京医科大学药学院, 南京 211100

魏棒棒,男,硕士研究生研究方向:靶向代谢组学质谱分析

通讯作者:

*司鑫鑫,女,副教授,硕士生导师研究方向:肿瘤细胞耐药的机制 Tel:(0518)85895791

Exploring Trastuzumab Resistance Biomarkers for HER2-positive Breast Cancer Based on Targeted Metabolomics of TCA-Related Metabolites

WEI Bangbang¹, CAO Yiwei¹, DONG Chuntao¹, LI Debang¹, XU Feifei², SI Xinxin¹^,^*, CHEN Yun²

Affiliations

¹ School of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China

² School of Pharmacy, Nanjing Medical University, Nanjing 211100, China

出版时间: 2024-02-22 doi: 10.11669/cpj.2024.04.003

文章导航

摘要

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目的探究三羧酸循环(tricarboxylic acid cycle,TCA)相关代谢物在人表皮生长因子受体2(human epidermal growth factor receptor 2,HER2)阳性乳腺癌曲妥珠单抗耐药和敏感细胞中的水平变化,并筛选出与曲妥珠单抗耐药性相关的潜在生物标志物。方法本研究采用基于超高效液相色谱串联质谱(ultra-high-performance liquid chromatography-mass spectrometry,UPLC-MS/MS)的靶向代谢组学方法检测曲妥珠单抗耐药细胞和敏感细胞中TCA相关代谢物(枸橼酸、顺式-乌头酸、异枸橼酸、α-酮戊二酸、琥珀酸、富马酸、苹果酸和草酰乙酸)含量水平,并对该检测方法的线性、准确度和精密度进行考察。使用正交偏最小二乘法判别分析(orthogonal partial least-squares discrimination analysis,OPLS-DA)法比较耐药和敏感细胞中TCA相关代谢物的表达差异,以P<0.05和差异倍数(fold change,FC)>1.5或<0.67为标准筛选出差异代谢物,并基于支持向量机(support vector machine,SVM)算法建立多变量耐药分类模型,利用受试者工作曲线(receiver operating characteristic curve,ROC)对差异代谢物和SVM模型的分类精度进行评估。结果三羧酸循环相关代谢物的标准曲线相关系数r均大于0.994,所建立的方法准确度为94%~105%,精密度变异系数(coefficient of variation,CV)均小于15%。OPLS-DA模型中,敏感组和耐药组细胞分离趋势明显。在曲妥珠单抗耐药细胞中,苹果酸、琥珀酸、富马酸的含量较曲妥珠单抗敏感细胞显著升高(P<0.05)。ROC分析显示琥珀酸、富马酸和苹果酸的曲线下面积(area under curve,AUC)均大于0.9,联合3种差异代谢物建立SVM耐药诊断模型的AUC为1.000。结论 TCA相关代谢物在曲妥珠单抗耐药和敏感细胞中呈现不同的表达水平,琥珀酸、富马酸和苹果酸可作为生物标志物对HER2阳性乳腺癌曲妥珠单抗耐药的诊断具有潜在价值。

关键词

HER2阳性乳腺癌 / 曲妥珠单抗耐药 / 三羧酸循环相关代谢物 / 靶向代谢组学 / 生物标志物

Abstract

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OBJECTIVE To investigate the levels of tricarboxylic acid cycle (TCA)-related metabolites in human epidermal growth factor receptor 2 (HER2)-positive breast cancer trastuzumab-resistant and trastuzumab-sensitive cells, as well as to screen potential biomarkers of trastuzumab resistance. METHODS Targeted metabolomics was adopted to detect TCA-related metabolite levels including citrate, cis-aconitate, isocitrate, alpha-ketoglutarate, succinic acid,fumaric acid,malic acid and oxaloacetic acid based on ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) in trastuzumab-resistant and trastuzumab-sensitive cells. The linearity, accuracy and precision of the method were validated. Then, orthogonal partial least-squares discrimination analysis (OPLS-DA) was used to compare the relative differential expression in trastuzumab-resistant and trastuzumab-sensitive cells. Differentially expressed metabolites were screened according to P<0.05, fold change (FC)>1.5 or FC<0.67. Finally, a multivariable model based on SVM and receiver operating characteristic (ROC) curve analysis was used to assess the classification accuracy of each metabolite. RESULTS For all TCA-related metabolites, the standard curves were linear, the correlation coefficients (r) were greater than 0.994, the established method accuracy was 94%-105% and the precision coefficient of variation (CV, %) was less than 15%. Trastuzumab-resistant and trastuzumab-sensitive cells were clearly separated from each other in the OPLS-DA model. Malic acid, succinic acid and fumaric acid were significantly upregulated in trastuzumab-resistant cells (P<0.05) compared to trastuzumab-sensitive cells. The areas under the curves (AUCs) of malic acid, succinic acid and fumaric acid were greater than 0.9 and the SVM model yielded the highest AUC of 1.000. CONCLUSION TCA-related metabolites are differentially expressed in trastuzumab-resistant and trastuzumab-sensitive cells. Succinic acid, fumaric acid and malic acid can serve as biomarkers that may provide potential value for the diagnosis of trastuzumab resistance in HER2-positive breast cancer.

Key words

HER2-positive breast cancer / trastuzumab-resistance / TCA-related metabolity / targeted metabolomic / biomarker

引用本文

魏棒棒, 曹怿玮, 董春涛, 李德邦, 许飞飞, 司鑫鑫, 陈芸. 基于三羧酸循环相关代谢物的靶向代谢组学探究HER2阳性乳腺癌曲妥珠单抗耐药生物标志物. 中国药学杂志, 2024 , 59 (4) : 302 -310 . DOI: 10.11669/cpj.2024.04.003

WEI Bangbang, CAO Yiwei, DONG Chuntao, LI Debang, XU Feifei, SI Xinxin, CHEN Yun. Exploring Trastuzumab Resistance Biomarkers for HER2-positive Breast Cancer Based on Targeted Metabolomics of TCA-Related Metabolites[J]. Chinese Pharmaceutical Journal, 2024 , 59 (4) : 302 -310 . DOI: 10.11669/cpj.2024.04.003

正文

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全球乳腺癌的负担日益上升,乳腺癌因其高死亡率和发病率而成为女性的主要健康问题^[1]。大约15%~20%乳腺癌患者的人表皮生长因子受体2(human epidermal growth factor receptor 2, HER2)过表达^[2]。HER2阳性乳腺癌作为一种复发性高、侵袭强的恶性肿瘤,相对于管腔A型、管腔B型和三阴性乳腺癌,HER2阳性乳腺癌患者预后较差,生存期较短^[3]。曲妥珠单抗作为HER2阳性乳腺癌患者的一线治疗标准^[4],通过特异性结合HER2胞外结构域,抑制胞内域磷酸化阻滞下游促癌通路^[5],从而改善患者的总生存期^[6-7]。然而,以曲妥珠单抗为治疗方案的HER2阳性乳腺癌患者中有30%的患者在治疗一年内就出现了耐药性^[8-9],并且由于HER2蛋白表达的区域异质性,HER2状态的临床评估存在不准确^[10],往往诊断时病灶已出现转移,导致患者错过最佳治疗窗和医疗资源浪费^[11-12]。因此,临床上亟需HER2阳性乳腺癌曲妥珠单抗耐药的诊断生物标志物。尽管基因组学、转录组学和蛋白组学揭示了耐药发生的生物分子机制和干预靶点,然而难以应用于临床耐药性早期诊断和逆转耐药性的治疗^[13-15]。因而从代谢层面挖掘潜在的生物标志物对于曲妥珠单抗耐药性的发生机制和诊断提供了新思路^[16]。

HER2阳性乳腺癌作为一种代谢性疾病,其自身可以发生代谢重编程。肿瘤细胞可通过加快能量代谢为自身的增殖提供动力和物质基础^[17]。三羧酸循环(tricarboxylic acid cycle, TCA)作为能量代谢的核心通路,葡萄糖初步氧化所产生的丙酮酸进入线粒体实现葡萄糖的完全有氧氧化,循环过程产生ATP和多种中间代谢产物,见图1。研究指出,TCA相关代谢物通过诱导缺氧诱导因子的产生和稳定、表观遗传改变和活性氧水平改变等方式促进肿瘤疾病进展和耐药性的发生^[18]。琥珀酸的蓄积引起的代谢重编程使得肿瘤坏死因子受体相关蛋白1上调,导致琥珀酸脱氢酶的抑制和核因子E2相关因子2(nuclear factor-E2-related factor 2, NRF2)通路激活^[19]。最近的研究表明,琥珀酸通过抑制环GMP-AMP合酶(cyclic GMP-AMP synthase, cGAS)-干扰素-β(interferon-β, IFN-β)通路限制CD8 T细胞转运到肿瘤微环境,结直肠癌患者表现出抗程序性死亡受-1(programmed cell death protein 1, PD-1)免疫治疗^[20]。富马酸通过靶向第10号染色体同源缺失性磷酸酶-张力蛋白(phosphatase and tensin homologue deleted on chromosome 10, PTEN)的第211位半胱氨酸残基琥珀酸化从而激活磷脂酰肌醇3-激酶(phosphati dylinositol 3 kinase, PI3K)/磷酸化丝氨酸/苏氨酸激酶(serine/threonine kinase, AKT)信号传导并促进2型乳头状肾细胞癌增殖和对舒尼替尼治疗的耐药性^[21]。在多柔比星耐药的乳腺癌细胞中,药物外排蛋白的高表达引起苹果酸和柠檬酸的在水平上差异,苹果酸被认为是化疗耐药的独特生物标志物^[22],然而在曲妥珠单抗耐药细胞和敏感细胞中很少进行有关于TCA相关代谢物的系统研究。

基于液相色谱串联质谱的靶向代谢组学技术已经成为寻找潜在的小分子生物标志物强有力的分析工具,本研究通过超高效液相色谱-质谱联用(ultra-high-performance liquid chromatography-mass spectrometry, UPLC-MS/MS)检测TCA相关代谢物在曲妥珠单抗耐药细胞(HCC195、JIMT-1)和敏感细胞(BT474、SK-BR-3)中的表达水平^[23-24],基于差异代谢物建立支持向量机(support vector machine, SVM)耐药分类模型,并通过受试者工作曲线(receiver operating characteristic curve, ROC)分析其作为曲妥珠单抗耐药诊断生物标志物的诊断效能,以期为曲妥珠单抗耐药性的诊断提供潜在的生物标志物。

1 材料和方法

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1.1 细胞与试剂

人HER2阳性乳腺癌曲妥珠单抗敏感细胞(SK-BR-3、BT-474)和耐药细胞(HCC1954、JIMT-1)(美国细胞培养物收藏中心);Dulbecco's 改良 Eagle 培养基(货号:11960044)、RPMI 1640培养基(货号:11875093)、McCoy' s 5A培养基(货号:16600082)、胎牛血清(货号:10099141C)(美国Gibco公司);色谱级甲醇、甲酸;内标对乙酰氨基苯酚对照品(货号:A105807,98.0%)、枸橼酸(货号:C434173,99.5%)、顺式-乌头酸(货号:A111231,99.0%)、异枸橼酸(货号:I106830,93.0%)、α-酮戊二酸(货号:K105570,99.0%)、琥珀酸(货号:B299883,95.0%)、富马酸(货号:D154295,98.0%)、苹果酸(货号:M101126,99.0%)和草酰乙酸(货号:O107187,98.0%)(中国阿拉丁生化科技股份有限公司)。

1.2 对照品储备液、标准曲线和质控的配制

精密称取8种TCA相关代谢物, 使用体积分数为20%甲醇的水溶液(含100 μmol·L^-1的对乙酰氨基酚)配制成5 mmol·L^-1的单一标准品储配液,保存在-80 ℃冰箱备用。精密量取各标准储备液适量混合以制备混合标准溶液,混合标准溶液中枸橼酸、顺式乌头酸、异枸橼酸、α-酮戊二酸、琥珀酸、富马酸、苹果酸和草酰乙酸的浓度分别为250、500、500、300、250、300、300、500 μmol·L^-1。通过逐级稀释配制成系列浓度的混合标准溶液用于建立标准曲线。7种TCA相关代谢物分别配置定量下限浓度(lower limit of quantitation, LLOQ)、低浓度质控(lower of quality control, LQC)、中浓度质控(medium of quality control,MQC)和高浓度质控(high of quality control, HQC)共4种浓度的质控(quality control,QC),枸橼酸、顺式乌头酸、异枸橼酸、α-酮戊二酸、琥珀酸、富马酸和苹果酸的LLOQ分别为0.5、0.5、5、1、0.1、0.5、0.5 μmol·L^-1,LQC分别为1.5、1.5、15、3、0.3、1.5、1.5 μmol·L^-1,MQC分别为50、100、100、50、30、50、50 μmol·L^-1,HQC分别为100、200、200、120、60、120、120 μmol·L^-1。

1.3 细胞培养

HCC1954和BT-474细胞分别在含10%的胎牛血清的RPMI-1640培养基中培养,SK-BR-3和JIMT-1细胞分别在含15%的胎牛血清的McCoy' s 5A和DMEM培养基中培养,各培养基中均加入1%的青霉素和链霉素。上述4种细胞均于37 ℃,5% CO₂的培养箱中培养。每隔2 d更换新的培养基,生长融合度到90%时,使用0.25%胰蛋白酶消化至细胞悬浮后加入到含有新培养基的培养皿中完成传代。

1.4 细胞代谢物样本制备

使用预冷的0.9% NaCl溶液迅速清洗细胞两次,随后每皿中加入1.5 mL的超纯水,于-80 ℃冰箱中放置2 h后使用刮刀刮取细胞并采用二喹啉甲酸(bicinchoninic acid, BCA)法进行蛋白浓度测定;通过加入 3倍体积的-80 ℃预冷甲醇(含100 μmol·L^-1的对乙酰氨基酚)进行代谢物提取,充分涡旋和高速离心后转移上清液于4 °C旋转蒸发,用100 μL体积分数20%甲醇的水溶液复溶样品后高速离心吸取上清,装质谱瓶。

1.5 UPLC-MS/MS分析

使用Agilent 1260高效液相色谱串联G6460三重四级杆质谱,色谱柱采用Phenomenex Gemini-NX C₁₈ 110 A (2 mm×30 mm, 3 μm),流动相A为体积分数0.2%甲酸的甲醇溶液,流动相B是体积分数0.2%甲酸的水溶液。采用线性梯度洗脱实现目的代谢物有效分离,线性梯度设置为: 0~1 min,1%A;1~2.5 min,1%~90%A;2.5~3 min,90%A; 3~3.5 min,90%~1%A;3.5~4 min,1%A。色谱柱柱温为25 ℃,流速为0.4 mL·min^-1,进样量5 μL。质谱分析采集模式为多反应监测(multiple reaction monitoring, MRM),离子源选择电喷雾离子源,扫描方式设置为负离子模式扫描,离子通道驻留时间为35 ms,干燥气温度为330 ℃,干燥气流速为12 L·min^-1,雾化气压力为241 kPa,电喷雾毛细管电压设置为4 000 V。

1.6 数据处理和分析

利用Agilent MassHunter Qualtative Analysis (version B.06.00)软件对TCA相关代谢物的保留时间、峰面积、峰高和信噪比(signal-to-noise ratio, S/N)进行提取分析。通过SIMCA-P (version 14.1)软件实现正交偏最小二乘判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA),使用GraphPad Prism (version 8.3)进行线性回归分析、绘制含量表达图和ROC分析,使用 R studio (version 4.3.0)中pheatmap和ggplot2包进行热图分析和火山图分析,SVM包构建SVM耐药分类模型。组间比较采用独立样本t检验以P<0.05为差异有统计学意义,以差异倍数(fold change, FC)>1.5和<0.67为标准筛选出TCA相关差异代谢物。

2 结果

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2.1 TCA相关代谢物质谱条件优化

利用UPLC-MS/MS确定8种TCA相关代谢物和内标化合物的相关质谱信息,确定各代谢物以及内标化合物的一级母离子(Q1)和二级子离子(Q3)信息,优化离子对的碎裂电压和碰撞能量,建立MRM方法,见图2。通过TCA中间体混合标准溶液确定各自保留时间,在0~4 min之内线性洗脱有效分离出8种代谢物,出峰时间主要集中在0.5~2.5 min,8种TCA相关代谢物和内标化合物的质谱优化信息见表1。

2.2 标准曲线、线性范围、准确度和精密度考察

通过使用加入对乙酰氨基酚作为内标的混合标准溶液,以代谢物与内标峰面积的比值为纵坐标,对应浓度为横坐标进行线性回归分析,草酰乙酸由于其本身具有脱羧的不稳定性^[25],未进行线性验证,其余7种相关代谢物标准曲线的相关性系数r均大于0.994,以S/N等于3计算出检出限(limit of detection, LOD),S/N大于10计算出LLOQ。通过对7种代谢物的LLOQ、LQC、MQC和HQC各平行进样5次,连续进样分析3 d,分别计算准确度、日内以及日间精密度。标准曲线、线性范围、准确度和精密度等信息如表2所示。7种代谢物的准确度在94%~105%,LLOQ的日间和日内精密度变异系数(coefficient of variation, CV)均小于20%,低、中、高浓度质控样品CV小于15%,该方法的准确度和精密度良好。

2.3 OPLS-DA整体差异分析

依据TCA相关代谢物的定量水平构建OPLS-DA模型,四株细胞组内聚集程度高,组间分离趋势明显。其中R²X=0.999,R²Y=0.810,Q²=0.611,模型具有较好的预测能力,见图3A。通过200次置换检验分析结果表明,R²和Q²在Y轴上的截距分别小于0.4和0.05,证明OPLS-DA模型没有过拟合,见图3B。以上结果表明,TCA相关代谢物在曲妥珠单抗耐药细胞和敏感细胞中存在差异表达模式,可用于进一步差异分析。

2.4 细胞样本中TCA相关代谢物的检测结果

8种待测TCA相关代谢物中有6种代谢物含量在线性范围内,浓度范围在0.910 1~104.417 4 μmol·L^-1。经过t检验后,琥珀酸、富马酸和苹果酸具有统计学意义(P<0.05),其余相关代谢物均差异无统计学意义,6种代谢物在耐药组和敏感组中的含量见图4A。通过层次聚类分析各样本之间的关联性,四种细胞的不同生物学重复样本之间聚集为两类,表明组内样本的同质性和组间样本的差异性,见图4B。

2.5 曲妥珠单抗耐药和敏感细胞中的差异代谢物筛选

以P<0.05、FC>1.5和<0.67为标准筛选差异代谢物,结果表明苹果酸、琥珀酸和富马酸在曲妥珠单抗耐药细胞中显著上调,其余代谢物均无统计学意义(P>0.05)。其中富马酸最显著且差异倍数最大,见图5。这些差异代谢物共同揭示曲妥珠单抗耐药细胞TCA通量水平的改变。

2.6 差异代谢物的分类效能分析

对琥珀酸、富马酸和苹果酸进行ROC曲线分析,探索其作为生物标志物的诊断效能。结果显示,琥珀酸、富马酸和苹果酸在区分曲妥珠单抗耐药和敏感细胞的AUC均大于0.9,其中富马酸的AUC=1.000,见图6A。联合3种差异代谢物建立多变量SVM诊断模型,以70%为训练集,30%为测试集,结果显示,多变量诊断模型在训练集和测试集的AUC=1.000。联合指标的诊断性能优于琥珀酸和苹果酸的单变量,能够明显区分耐药组和敏感组细胞,见图6B。

3 讨论

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本研究通过基于质谱的靶向代谢组学技术探究8种TCA中间体代谢物在曲妥珠单抗耐药和敏感细胞中的表达情况,所建立的方法具有较好的线性、准确度和精密度,根据差异筛选标准发现苹果酸、富马酸和琥珀酸存在显著差异。基于所筛选到的差异代谢物通过ROC分析和构建多变量SVM诊断模型可以有效地区分曲妥珠单抗耐药和敏感细胞,AUC均大于0.9。TCA中间体可能是曲妥珠单抗耐药的潜在生物标志物,研究结果对曲妥珠单抗耐药性的诊断具有潜在价值。

既往的研究指出,TCA周期失调被认为是癌细胞转化的适应机制,TCA相关代谢物为癌症的增殖和转移提供生物合成中间体原料和动力^[26]。本研究中,富马酸差异倍数最显著,ROC分析显示其AUC=1.000,表明其在诊断曲妥珠单抗耐药具有良好的性能。研究指出,富马酸的蓄积可促进缺氧诱导因子(hypoxia inducible factor-1,HIF-1)基因的表达实现表观遗传调控,并维持HIF-1的稳定^[27],这使得高丰度的富马酸有利于癌细胞上皮间充质的转化,子宫肌瘤、皮肤平滑肌瘤和肾细胞癌的患病风险大大增加^[28]。在一项对吉西他滨化疗耐药的胰腺导管癌组织的研究中显示,富马酸在预后不良和预后较好的患者中差异倍数最大,并表现出上调趋势,富马酸对PLS-DA模型具有较大的贡献值^[29]。此外,Liu等^[30]指出在曲妥珠单抗耐药胃癌细胞中富马酸和苹果酸显著上调,其中富马酸通过增加ATP产生和非必需氨基酸的合成有助于细胞增殖和耐药性产生。

本研究中琥珀酸的ROC分析显示其AUC=0.993,可以显著区分耐药组和敏感组细胞,琥珀酸的上调趋势在卵巢癌、胃癌、胰腺癌和乳腺癌等多种癌症中得到证实^[31-34]。过量的琥珀酸使得HIF-1升高,同时还阻止HIF-1脯氨酸残基羟基化降解^[35]。相比较于癌旁组织,在胃癌组织中琥珀酸可通过G蛋白偶联受体-91介导STAT3和ERK1/2活化并上调血管内皮生长因子促进癌细胞增值和转移^[36]。最近的一项研究指出,索拉非尼耐药的肝癌细胞的非靶向代谢组学结果揭示琥珀酸在耐药肝癌细胞中的表达显著上调,通过对凋亡信号通路产生抗性,这使得琥珀酸可能成为耐药生物标志物^[37]。此外,新辅助治疗预后不良的乳腺癌患者血浆外泌体的通路富集分析显示线粒体电子传递链和TCA显著富集,琥珀酸作为电子传递链的电子供体,在预后不良患者中显著上调,作者指出琥珀酸的上调不仅促进和稳定HIF-1,同时还会诱导多种药物外排转运蛋白的表达包括P糖蛋白(P-glycoprotein, P-gp)、多药耐药相关蛋白1(multidrug resistanec-associated protein 1, MRP1)和乳腺癌耐药蛋白(breast cancer resistance protein, BCRP),引发多重耐药性^[38-39]。

本研究发现在耐药细胞中苹果酸显著上调的,Gao等^[40]指出在肝癌细胞中引起苹果酸的蓄积是由于癌细胞对糖酵解的代谢依赖从而引起下游氧化磷酸化抑制,作者通过随机森林和多因素逻辑回归得到包括苹果酸在内的3种生物标志物组成的高特异性诊断模型,这在另一项对腔鳞状细胞癌患者唾液代谢谱研究中也得到证实,苹果酸在内的3种生物标志物组成的诊断模型AUC可达0.91,具有临床应用价值^[41]。Wang等^[42]利用非靶向代谢组学技术对高表达醛脱氢酶 1A1耐药肺癌细胞代谢谱进行单因素和多因素差异分析发现,苹果酸的异常高表达显示TCA通路的代谢重编程,能量代谢增强。

4 结论

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本研究利用基于质谱的靶向代谢组学定量检测HER2阳性乳腺癌曲妥珠单抗耐药和敏感细胞中TCA相关代谢物的水平,所建立的方法具有较好线性、准确度和精密度,差异分析显示富马酸、苹果酸和琥珀酸在耐药细胞中显著上调,并且单变量和SVM多变量模型可以有效区分耐药组和敏感组细胞,靶向代谢组学技术为生物标志物的挖掘提供了新方向,基于所筛选到的生物标志物在曲妥珠单抗耐药性的诊断具有良好的应用前景,这对后续HER2阳性曲妥珠单抗耐药性机制和干预靶标的研究提供了新的见解。

基金

收起

国家自然科学青年基金项目资助(81703557)

参考文献

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文献

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参考文献引证文献

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2024年第59卷第4期

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文章信息

doi: 10.11669/cpj.2024.04.003

接收时间：2023-08-03
首发时间：2025-11-13
出版时间：2024-02-22

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收稿日期：2023-08-03

基金

国家自然科学青年基金项目资助(81703557)

作者信息

¹ 江苏海洋大学药学院, 江苏连云港 222005

² 南京医科大学药学院, 南京 211100

通讯作者:

*司鑫鑫,女,副教授,硕士生导师研究方向:肿瘤细胞耐药的机制 Tel:(0518)85895791

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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RefWorks
TxT

Component	t_R /min	m/z (Q1)	Optimization of MS parameters
Citric acid	1.098	190.9	70	111.0¹⁾	15	87.0	15
cis-Aconitic acid	1.996	172.9	70	129.0¹⁾	2	85.1	5
Isocitric acid	0.819	190.9	70	111.0	10	85.1¹⁾	10
α-Ketoglutaric acid	1.557	145.0	60	101.0 ¹⁾	2	57.2	5
Succinic acid	1.298	117.0	70	99¹⁾	5	73.1	5
Fumaric acid	1.777	115.0	60	71.1¹⁾	2
Malic acid	0.680	133.0	70	115.0¹⁾	5	71.1	10
Oxaloacetic acid	0.498	131.0	60	87.0 ¹⁾	2	43.2	10
Acetaminophen (internal standard)	2.482	150.0	110	107.0	30	118.0	15

Component

t_R
/min

m/z
(Q1)

Optimization of MS parameters

Fragmentation/V

m/z(Q3¹)

CE¹⁾/eV

m/z(Q3²)

CE²⁾/eV

Citric acid

1.098

190.9

111.0¹⁾

87.0

cis-Aconitic acid

1.996

172.9

129.0¹⁾

85.1

Isocitric acid

0.819

190.9

111.0

85.1¹⁾

α-Ketoglutaric acid

1.557

145.0

101.0 ¹⁾

57.2

Succinic acid

1.298

117.0

99¹⁾

73.1

Fumaric acid

1.777

115.0

71.1¹⁾

Malic acid

0.680

133.0

115.0¹⁾

71.1

Oxaloacetic acid

0.498

131.0

87.0 ¹⁾

43.2

Acetaminophen (internal standard)

2.482

150.0

110

107.0

118.0

Component	Regression equation	Linear range /μmol·L^-1	r	LOD /μmol·L^-1	LLOQ /μmol·L^-1	QC /μmol·L^-1	Accuracy /%	Precision
Citric acid	y=0.100 4x-0.025 63	0.5-125	0.997 3	0.1	0.5	0.5	94.57	12.72	12.32
						1.5	103.53	8.23	8.41
						50	97.37	5.08	5.15
						100	101.03	4.72	4.77
cis-Aconitic acid	y=0.100 2x-0.049 86	0.5-250	0.994 2	0.1	0.5	0.5	95.19	10.76	11.85
						1.5	96.98	9.74	10.42
						100	103.55	9.03	9.11
						200	98.42	7.56	8.44
Isocitric acid	y=0.001 615x-0.004 234	5-250	0.994 7	0.5	5	5	104.85	9.41	11.47
						15	103.9	7.42	7.31
						100	102.69	6.06	6.15
						200	98.52	5.10	5.77
α-Ketoglutaric acid	y=0.005 797x-0.000 195 4	1-150	0.999 1	0.2	1	1	94.18	10.73	11.55
						3	95.81	9.09	10.67
						50	102.27	7.79	8.71
						120	97.17	5.78	6.12
Succinic acid	y=0.009 153x-0.000 452 4	0.1-75	0.998 8	0.02	0.1	0.1	98.64	9.25	9.76
						0.3	99.70	8.06	8.62
						30	102.41	6.71	7.02
						60	100.82	4.07	5.14
Fumaric acid	y=0.002 386x+0.002 617	0.5-150	0.9995	0.1	0.5	0.5	94.10	6.98	7.49
						1.5	98.24	6.77	7.11
						50	101.30	5.92	6.35
						120	102.92	4.49	5.22
Malic acid	y=0.085 06x+0.004 496	0.5-150	0.996 9	0.05	0.5	0.5	97.23	8.05	8.19
						1.5	101.82	6.17	6.26
						50	99.38	5.41	5.97
						120	99.09	4.15	4.97

Component

Regression
equation

Linear range
/μmol·L^-1

LOD
/μmol·L^-1

LLOQ
/μmol·L^-1

QC
/μmol·L^-1

Accuracy
/%

Precision

Intra-day CV/%

Inter-day CV/%

Citric acid

y=0.100 4x-0.025 63

0.5-125

0.997 3

0.1

0.5

94.57

12.72

12.32

1.5

103.53

8.23

8.41

97.37

5.08

5.15

100

101.03

4.72

4.77

cis-Aconitic acid

y=0.100 2x-0.049 86

0.5-250

0.994 2

0.1

0.5

95.19

10.76

11.85

1.5

96.98

9.74

10.42

100

103.55

9.03

9.11

200

98.42

7.56

8.44

Isocitric acid

y=0.001 615x-0.004 234

5-250

0.994 7

0.5

104.85

9.41

11.47

103.9

7.42

7.31

100

102.69

6.06

6.15

200

98.52

5.10

5.77

α-Ketoglutaric acid

y=0.005 797x-0.000 195 4

1-150

0.999 1

0.2

94.18

10.73

11.55

95.81

9.09

10.67

102.27

7.79

8.71

120

97.17

5.78

6.12

Succinic acid

y=0.009 153x-0.000 452 4

0.1-75

0.998 8

0.02

0.1

98.64

9.25

9.76

0.3

99.70

8.06

8.62

102.41

6.71

7.02

100.82

4.07

5.14

Fumaric acid

y=0.002 386x+0.002 617

0.5-150

0.9995

0.1

0.5

94.10

6.98

7.49

1.5

98.24

6.77

7.11

101.30

5.92

6.35

120

102.92

4.49

5.22

Malic acid

y=0.085 06x+0.004 496

0.5-150

0.996 9

0.05

0.5

97.23

8.05

8.19

1.5

101.82

6.17

6.26

99.38

5.41

5.97

120

99.09

4.15

4.97