蛋白质组学RPPA技术筛选乳腺癌患者预后相关血清分子标志物的研究

doi:10.3877/cma.j.issn.1674-3946.2021.05.024

目的

探究影响乳腺癌患者预后相关的血清分子标志物。

方法

选择2013年6月至2015年2月经病理确诊的乳腺癌患者126例作为研究对象，患者术后随访5年，采用蛋白质组学反向蛋白阵列(RPPA)技术筛选患者预后相关的血清分子标志物。使用统计学软件SPSS 22.0进行统计学处理，采用Pearson相关分析探究筛选出的蛋白质与患者临床资料间的相关性；采用单因素Cox回归分析筛选影响患者预后的相关蛋白，多因素Cox回归分析探究筛选出的蛋白、临床资料与患者预后间的相关性，得到影响患者预后的独立危险因素，P<0.05表示有统计学意义。

结果

通过RPPA技术对37种血清蛋白质的检测，单因素回归分析，丝氨酸羟甲基转移酶2(SHMT2)、丙氨酸-丝氨酸-半胱氨酸转运载体2(ASCT2)高表达是影响患者5年总生存率以及无复发生存率的显著相关因素。SHMT2、ASCT2高表达与患者肿瘤大小、TNM分期、病理分级以及雌激素受体(ER+)、孕激素受体(PR+)之间均具有显著的正相关性。多因素Cox回归分析，SHMT2、ASCT2高表达也是影响患者预后的独立危险因素。

结论

高水平ASCT2和SHMT2表达与乳腺癌患者不良预后有关，可能作为乳腺癌潜在的治疗靶点。

关键词: 乳腺肿瘤, 蛋白质组学, 反向蛋白阵列, 预后, 存活率分析

Objective

To explore the prognosis molecular markers in serum of patients with breast neoplasms.

Methods

126 patients diagnosed as breast cancer from June 2013 to February 2015 were enrolled and were followed up for 5 years after the operation. The proteomics reverse phase protein array (RPPA) technology was used to screen the biomarkers associated with the prognosis of the patients. Statistical analysis were performed by using SPSS 22.0 software. The correlation of the candidate proteins and the clinical data were analyzed by using Pearson correlation analysis; Univariate Cox analyses were used to evaluate the proteins that affect the prognosis of patients, then multivariate Cox analyses were used to analyze the correlation of the candidate proteins, clinical data and the prognosis of the patients, to obtain the independent risk factors affecting the prognosis of the patients. A P value of <0.05 was considered as statistically significant difference.

Results

37 serum proteins were detected by RPPA , univariate Cox analyses showed that the high expression of serine hydroxymethyl transferase 2(SHMT2) and alanine-serine-cysteine transporter 2 (ASCT2) were significantly associated with the 5 years recurrence-free survival and overall survival. The high expression of SHMT2 and ASCT2 had a significant positive correlation with the tumor size, TNM stage, pathological grade, estrogen receptor (ER+ ) and progesterone receptor (PR+ ). Multivariate Cox analyses showed that the high expression of SHMT2 and ASCT2 were also independent risk factors affecting the prognosis of patients.

Conclusion

High levels of ASCT2 and SHMT2 expression are related to the poor prognosis of breast neoplasms, which might be the potential therapeutic targets for breast cancer.

Key words: Breast neoplasms, Proteomics, Reverse phase protein array, Prognosis, Survival analysis

表1 RPPA筛选的影响乳腺癌患者术后5年总生存率的抗体

变量	B	SE	Wald	HR(95%CI)	P值
SHMT2	0.519	0.605	11.631	2.43(1.62~5.68)	<0.001
ASCT2	0.881	0.294	8.052	2.17(1.42~4.81)	<0.001

表1 RPPA筛选的影响乳腺癌患者术后5年总生存率的抗体

变量	B	SE	Wald	HR(95%CI)	P值
SHMT2	0.519	0.605	11.631	2.43(1.62~5.68)	<0.001
ASCT2	0.881	0.294	8.052	2.17(1.42~4.81)	<0.001

表2 RPPA筛选影响乳腺癌患者术后5年无复发生存率的抗体

变量	B	SE	Wald	HR(95%CI)	P值
SHMT2	0.685	0.492	12.628	2.67(1.96~7.19)	<0.001
ASCT2	0.761	0.361	10.249	2.33(1.70~5.38)	<0.001
GAPDH	-0.6118	0.559	6.519	1.82(1.44~3.86)	<0.001
PKM2	0.516	0.546	5.046	1.36(1.15~2.46)	0.004
FH	0.884	0.406	6.035	1.58(1.30~3.16)	0.001
CAD	0.623	0.351	17.019	2.82(2.12~9.67)	<0.001

表2 RPPA筛选影响乳腺癌患者术后5年无复发生存率的抗体

变量	B	SE	Wald	HR(95%CI)	P值
SHMT2	0.685	0.492	12.628	2.67(1.96~7.19)	<0.001
ASCT2	0.761	0.361	10.249	2.33(1.70~5.38)	<0.001
GAPDH	-0.6118	0.559	6.519	1.82(1.44~3.86)	<0.001
PKM2	0.516	0.546	5.046	1.36(1.15~2.46)	0.004
FH	0.884	0.406	6.035	1.58(1.30~3.16)	0.001
CAD	0.623	0.351	17.019	2.82(2.12~9.67)	<0.001

表3 SHMT2、ASCT2与乳腺癌患者的基本临床资料相关因素分析

变量	SHMT2		ASCT2
变量	r值	P值	r值	P值
年龄	0.352	0.218	0.346	0.152
肿瘤大小	0.452	0.042	0.401	0.038
TNM分期	0.484	0.031	0.416	0.027
病理分级	0.643	0.038	0.447	0.019
ER	0.506	0.007	0.532	0.003
PR	0.472	0.016	0.016	0.012

表3 SHMT2、ASCT2与乳腺癌患者的基本临床资料相关因素分析

变量	SHMT2		ASCT2
变量	r值	P值	r值	P值
年龄	0.352	0.218	0.346	0.152
肿瘤大小	0.452	0.042	0.401	0.038
TNM分期	0.484	0.031	0.416	0.027
病理分级	0.643	0.038	0.447	0.019
ER	0.506	0.007	0.532	0.003
PR	0.472	0.016	0.016	0.012

表4 影响乳腺癌患者术后5年总生存率的多因素Cox回归分析

变量	B	SE	Wald	HR(95%CI)	P值
年龄	0.256	0.203	7.652	2.41(1.76~5.40)	<0.001
TNM分期	1.925	0.318	6.586	1.75(1.43~2.54)	<0.001
SHMT2	0.815	0.511	4.918	1.54(1.21~2.16)	0.028
ASCT2	0.226	0.184	3.903	1.35(1.11~1.85)	0.041

表4 影响乳腺癌患者术后5年总生存率的多因素Cox回归分析

变量	B	SE	Wald	HR(95%CI)	P值
年龄	0.256	0.203	7.652	2.41(1.76~5.40)	<0.001
TNM分期	1.925	0.318	6.586	1.75(1.43~2.54)	<0.001
SHMT2	0.815	0.511	4.918	1.54(1.21~2.16)	0.028
ASCT2	0.226	0.184	3.903	1.35(1.11~1.85)	0.041

表5 影响乳腺癌患者术后5年无复发生存率的多因素Cox回归分析

变量	B	SE	Wald	HR(95%CI)	P值
年龄	1.205	0.226	7.162	2.01(1.10~6.17)	<0.001
TNM分期	0.816	0.314	5.922	1.60(1.31~3.84)	<0.001
SHMT2	0.552	0.262	4.427	1.49(1.09~3.54)	0.035
ASCT2	0.319	0.176	4.216	1.28(1.11~2.85)	0.046

表5 影响乳腺癌患者术后5年无复发生存率的多因素Cox回归分析

变量	B	SE	Wald	HR(95%CI)	P值
年龄	1.205	0.226	7.162	2.01(1.10~6.17)	<0.001
TNM分期	0.816	0.314	5.922	1.60(1.31~3.84)	<0.001
SHMT2	0.552	0.262	4.427	1.49(1.09~3.54)	0.035
ASCT2	0.319	0.176	4.216	1.28(1.11~2.85)	0.046

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Screening the prognosis-related serum molecular markers of breast cancer based on reverse phase protein array

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Screening the prognosis-related serum molecular markers of breast cancer based on reverse phase protein array