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中华乳腺病杂志(电子版)

中华乳腺病杂志(电子版) ›› 2023, Vol. 17 ›› Issue (04) : 218 -228. doi: 10.3877/cma.j.issn.1674-0807.2023.04.004

论著

基于癌症基因组图谱构建乳腺癌中WAC反义RNA1的加权基因共表达网络和临床预测模型
汪彦阳, 周远洋, 魏雪菲, 陶志嵩, 龚海燕†()   
  1. 210008　南京大学医学院附属鼓楼医院核医学科
    210008　南京大学医学院附属鼓楼医院健康管理中心
  • 收稿日期:2022-08-03 出版日期:2023-08-01
  • 通信作者: 龚海燕
  • 基金资助:
    南京市医学科技发展项目卫医药卫生科研课题(YKK21065)

Construction of WGCNA network and clinical prediction model of lncRNA WAC-AS1 in breast cancer based on TCGA database

Yanyang Wang, Yuanyang Zhou, Xuefei Wei, Zhisong Tao, Haiyan Gong†()   

  1. Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
    Medical Examination Center, Nanjing Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
  • Received:2022-08-03 Published:2023-08-01
  • Corresponding author: Haiyan Gong
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引用本文：

汪彦阳, 周远洋, 魏雪菲, 陶志嵩, 龚海燕. 基于癌症基因组图谱构建乳腺癌中WAC反义RNA1的加权基因共表达网络和临床预测模型[J]. 中华乳腺病杂志(电子版), 2023, 17(04): 218-228.

Yanyang Wang, Yuanyang Zhou, Xuefei Wei, Zhisong Tao, Haiyan Gong. Construction of WGCNA network and clinical prediction model of lncRNA WAC-AS1 in breast cancer based on TCGA database[J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(04): 218-228.

目的

基于癌症基因组图谱(TCGA)和基因组织表达(GTE)数据库探索长链非编码RNA(lncRNA)WAC反义RNA1(WAC-AS1)在乳腺癌中的生物学功能以及对乳腺癌预后的影响。

方法

基于TCGA和GTE数据库的WAC-AS1表达数据，比较WAC-AS1在乳腺癌组织和正常乳腺组织中的表达差异；以WAC-AS1表达中位值为标准，将乳腺癌患者分为WAC-AS1高表达和低表达2组，比较2组患者的OS、无进展生存期(PFS)、疾病特异性生存期(DSS)和DFS以及癌组织中浸润的免疫细胞差异；使用肿瘤体细胞突变检测工具VarScan分析WAC-AS1相关的基因突变；采用基因集差异分析(GSVA)以及基因集富集分析(GSEA)寻找WAC-AS1在乳腺癌中可能参与的信号通路；采用加权基因共表达网络分析(WGCNA)构建乳腺癌预后的临床预测模型，并用受试者操作特征(ROC)曲线以及3年、5年OS的校准曲线分别评价模型的预测效能以及准确性。采用实时荧光定量PCR实验检测MCF-10A、MCF-7和MDA-MB-231细胞中WAC-AS1的表达。

结果

(1)WAC-AS1在乳腺癌中相对正常组织呈明显高表达[4.17(3.91，4.41)比3.70(3.37，4.09)，Z＝3.880，P<0.001]。(2)WAC-AS1高表达与低表达组的中位OS分别为10年和17.2年，组间比较差异有统计学意义(HR＝1.680，95%CI：1.208~2.338，P＝0.002)。2组患者的PFS、DSS和DFS比较差异无统计学意义(HR＝1.105，95%CI：0.798~1.529，P＝0.548；HR＝1.303，95%CI：0.846~2.008，P＝0.230；HR＝1.092，95%CI：0.711~1.678，P＝0.687)。高表达WAC-AS1的乳腺癌组织中有4种免疫细胞数量增加，包括幼稚B细胞、CD8+T细胞、滤泡辅助性T细胞和激活树突状细胞，而静息CD4+记忆T细胞和静息肥大细胞数量减少(P均<0.050)。(3)WAC-AS1相关突变频率最高的基因为TP53、PIKCA和TTN。(4)GSVA与GSEV结果显示WAC-AS1与DNA修复、MYC靶点V2、MYC靶点V1、E2F靶点、mTORC1信号通路正相关。(5)WGCNA分析发现WAC-AS1与黄绿色模块中91个与小分子生物合成相关的基因相关性最高(r＝0.270, P<0.001)。(6)单因素分析结果显示，年龄(HR＝1.034，95%CI：1.021~1.047，P<0.001)、性别(HR＝1.388，95%CI：0.188~9.929，P＝0.870)、临床分期(Ⅱ、Ⅲ、Ⅳ期分别与Ⅰ期比较，HR＝1.457，95%CI：1.043~2.022，P＝0.017；HR＝4.022，95%CI：2.804~5.739，P<0.001；HR＝16.130，95%CI：9.413~27.630，P<0.001)以及WAC-AS1表达量(HR＝1.032，95%CI：1.005~1.061，P＝0.020)是患者OS的影响因素。多因素分析结果表明：WAC-AS1的表达量与乳腺癌患者OS相关(HR＝1.377，95%CI：1.021~1.872，P＝0.039)。(7)所构建的临床预测模型的C-指数为0.759，ROC曲线下面积(AUC)为0.626(95%CI：58.0%~67.3%，P<0.001)，敏感度为76.9%，特异度为46.8%。预测曲线接近于理想曲线，拟合度较高。(8)PCR检测WAC-AS1在3种细胞株MCF-10A、MCF-7和MDA-MB-231的CT值分别为32.39±0.10、30.55±0.25和30.82±0.07，组间比较差异有统计学意义(F＝30.310，P<0.001)。进一步两两比较分析显示，乳腺癌细胞株MCF-7和MDA-MB-231的WAC-AS1表达均高于正常乳腺上皮细胞株MCF-10A(t＝7.916、7.431，P均<0.001)。

结论

本研究表明lncRNA WAC-AS1与乳腺癌的肿瘤微环境以及免疫浸润相关，高表达WAC-AS1影响乳腺癌患者的预后。WAC-AS1可能成为今后乳腺癌治疗中的一个潜在靶点及预后标志。

关键词: 乳腺肿瘤, 预后, 免疫, 临床预测模型, WAC反义RNA1
Objective

To explore the biological function of long non-coding RNA (lncRNA) WAC antisense RNA1(WAC-AS1) in breast cancer and its impact on the prognosis of breast cancer based on the Cancer Genome Atlas (TCGA) and Gene Tissue Expression (GTE) databases.

Methods

Based on the data of lncRNA WAC-AS1 expression in the TCGA and GTE databases, the expression of WAC-AS1 in breast cancer tissue was analyzed and compared with normal breast tissues. The breast cancer patients were divided into high expression group and low expression group according to the median value of WAC-AS1 expression. The OS, progression-free survival (PFS), disease-specific survival (DSS), DFS, and proportion of immune cells infiltrated in cancer tissues were compared between the two groups. WAC-AS1 related gene mutations were analyzed using the tumor somatic mutation detection tool VarScan. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also performed to explore WAC-AS1-involved signaling pathways in breast cancer. Finally, weighted gene correlation network analysis (WGCNA) was performed and a clinical prognostic model was constructed in breast cancer. The receiver operating characteristic (ROC) curve and the calibration curve of 3-year and 5-year OS were used to evaluate the prediction efficiency and accuracy of the model. The expression of WAC-AS1 was detected by fluorescence-based quantitative real-time PCR in MCF-10A, MCF-7 and MDA-MB-231 cells.

Results

(1) WAC-AS1 expression in breast cancer tissue was significantly higher compared with normal breast tissues [4.17 (3.91, 4.41) vs 3.70 (3.37, 4.09), Z=3.880, P<0.001]. (2) The median OS in WAC-AS1 high expression group and low expression group was 10.0 years and 17.2 years, respectively, indicating a significant difference (HR=1.680, 95%CI: 1.208-2.338, P=0.002). There was no significant difference in PFS, DSS and DFS between those two groups(HR=1.105, 95%CI: 0.798-1.529, P=0.548; HR=1.303, 95%CI: 0.846-2.008, P=0.230; HR=1.092, 95%CI: 0.711-1.678, P=0.687). In breast cancer tissues with high expression of WAC-AS1, 4 kinds of immune cells (naive B cells, CD8+ T cells, follicular helper T cells and activated dendritic cells) were increased, while resting CD4+ memory T cells and resting mast cells were decreased (all P<0.050). (3)The genes with the highest frequency of WAC-AS1 related mutations were TP53, PIKCA and TTN. (4) The results of GSVA and GSEV showed a positive correlation between WAC-AS1 and DNA repair, MYC target V2, MYC target V1, E2F target, and mTORC1 signaling pathway. (5) WGCNA analysis found that WAC-AS1 had the highest correlation with 91 genes related to small molecule biosynthesis in the yellow green module (r=0.270, P<0.001). (6) The results of univariate analysis showed that age (HR=1.034, 95%CI: 1.021-1.047, P<0.001), gender (female vs male, HR=1.388, 95%CI: 0.188-9.929, P=0.870), clinical stage (Phase Ⅱ vs Phase Ⅰ, HR=1.457, 95%CI: 1.043-2.022, P=0.017; Phase Ⅲ vs Phase Ⅰ, HR=4.022, 95%CI: 2.804-5.739, P<0.001; and Phase Ⅳ vs Phase I, HR=16.130, 95%CI: 9.413-27.630, P<0.001) and WAC-AS1 expression (HR=1.032, 95%CI: 1.005-1.061, P=0.020) were influencing factors for OS. Multivariate analysis showed that the expression of WAC-AS1 was correlated with OS in breast cancer patients (HR=1.377, 95% CI: 1.021-1.872, P=0.039). (7) The C-index of the constructed clinical prediction model was 0.759, the area under the ROC curve (AUC) was 0.626 (95%CI: 0.58.0-0.673, P<0.001), the sensitivity was 76.9%, and the specificity was 46.8%. The predicted curve fit well with the ideal curve. (8) The CT values of WAC-AS1 detected by PCR in three cell lines MCF-10A, MCF-7, and MDA-MB-231 were 32.39±0.10, 30.55±0.25, and 30.82±0.07, respectively, indicating a significant difference (F=30.310, P<0.001). Pairwise comparison showed that WAC-AS1 expression in MCF-7 and MDA-MB-231 cells was significantly higher than that in MCF-10A cells (t=7.916, 7.431, both P<0.001).

Conclusions

The lncRNA WAC-AS1 is related to the tumor microenvironment and immune infiltration in breast cancer. The high expression of WAC-AS1 affects the prognosis of breast cancer pateints. WAC-AS1 may be a potential target and prognostic marker in breast cancer treatment.

Key words: Breast neoplasms, Prognosis, Immunity, Clinical prediction model, WAC-AS1
表1 Cox多因素分析的变量赋值表
变量 变量类型 赋值
性别 X1 男＝1；女＝2
年龄a X2 实测值
临床分期 X3 Ⅰ期＝1；Ⅱ期＝2；Ⅲ期＝3；Ⅳ期＝4
WAC-AS1表达量a X4 实测值
生存结局 Y 生存＝0；死亡＝1
图1 WAC-AS1基因在33种肿瘤组织和正常组织中的表达差异注：a为肾上腺皮质癌；b为膀胱尿路上皮癌；c为乳腺癌；d为宫颈鳞癌和腺癌；e为胆管癌；f为结肠癌；g为弥漫性大B细胞淋巴瘤；h为食管癌；i为多形性胶质细胞瘤；j为头颈癌；k为肾嫌色细胞癌；l为肾透明细胞癌；m为肾乳头状细胞癌；n为急性髓细胞样白血病；o为脑低级别胶质瘤；p为肝细胞癌；q为肺腺癌；r为肺鳞癌；s为间皮瘤；t为卵巢浆液性囊腺癌；u为胰腺癌；v为嗜铬细胞瘤和副神经节瘤；w为前列腺癌；x为直肠腺癌；y为肉瘤；z为皮肤黑色素瘤；a1为胃癌；b1为睾丸癌；c1为甲状腺癌；d1为胸腺瘤；e1为子宫内膜癌；f1为子宫肉瘤；g1为葡萄膜黑色素瘤；*表示P<0.050；**表示P<0.010；***表示P<0.001；****表示P<0.000 1；ns表示无统计学差异；WAC-AS1为WAC反义RNA1
图2 WAC-AS1高表达组与低表达组乳腺癌患者的生存曲线　a图为总生存曲线；b图为无进展生存曲线；c图为疾病特异性生存曲线；d图为无瘤生存曲线注：总生存率的组间比较，HR＝1.680，95%CI：1.208~2.338，P＝0.002；无进展生存率的组间比较，HR＝1.105，95%CI：0.798~1.529，P＝0.548；疾病特异性生存率的组间比较，HR＝1.303，95%CI：0.846~2.008，P＝0.230；无瘤生存率的组间比较，HR＝1.092，95%CI：0.711~1.678，P＝0.687；WAC-AS1为WAC反义RNA1
图3 WAC-AS1高表达和低表达患者的基因突变差异图谱注：图左侧数值代表每种基因突变在癌症基因组图谱数据库乳腺癌队列中的比例，右侧代表突变的基因名称；WAC-AS1为WAC反义RNA1
图4 WAC-AS1共表达基因分析注：WAC-AS1为WAC反义RNA1
图5 WAC-AS1表达的基因集差异分析结果注：蓝色部分代表WAC-AS1高表达的基因组富集通路，绿色部分代表WAC-AS1低表达的基因组富集通路；MYC为骨髓细胞瘤癌基因；mTORC1为雷帕霉素复合物1机制靶点；β-catenin为β连环蛋白；TNF为肿瘤坏死因子；NFκB为核因子κB；TGFβ为转化生长因子β；KRAS为鼠类肉瘤病毒癌基因；IL：白细胞介素；JAK为Janus激酶；STAT为转录激活因子；PI3K为磷脂酰肌醇-3激酶；AKT为蛋白激酶B；mTOR为哺乳动物类雷帕霉素靶蛋白；GSVA为基因集差异分析；WAC-AS1为WAC反义RNA1
图6 WAC-AS1表达的基因集富集分析注：线条向上的趋势为WAC-AS1高表达组富集的信号通路，线条向下的趋势为WAC-AS1基因低表达组富集的信号通路；WAC-AS1为WAC反义RNA1
图7 加权基因共表达网络分析结果注：ME为模块特征基因；最右边的彩色竖条代表相关性，越接近红色代表正相关，越接近蓝色代表负相关；WAC-AS1为WAC反义RNA1
表2 TCGA数据库中乳腺癌患者总生存率的影响因素分析
变量 单因素分析 多因素分析
HR(95%CI) P值 HR(95%CI) P值
性别        
男(参照) － － － －
女 1.388(0.188~9.929) 0.870 1.942(0.273~14.063) 0.510
年龄 1.034(1.021~1.047) <0.001 1.036(1.023~1.050) <0.001
临床分期        
Ⅰ期(参照) － － － －
Ⅱ期 1.457(1.043~2.022) 0.017 1.778(0.978~3.333) <0.001
Ⅲ期 4.022(2.804~5.739) <0.001 3.778(2.022~7.012) <0.001
Ⅳ期 16.130(9.413~27.630) <0.001 13.563(10.938~16.813) <0.001
WAC-AS1表达量 1.032(1.005~1.061) 0.020 1.377(1.021~1.872) 0.039
图8 基于TCGA数据库构建1 109例乳腺癌患者列线图注：TCGA为癌症基因组图谱；WAC-AS1为WAC反义RNA1
图9 乳腺癌患者预后预测模型的肿瘤总生存评价指标　a、b图分别为3年、5年的肿瘤总生存校正曲线；c图为模型的ROC曲线验证注：OS为肿瘤总生存率，在一个理想的预测模型中，预测值等于真实值，曲线则正好落在45度的对角线上，当校准曲线在对角线之上时，则预测值大于真实值，当校准曲线在对角线之下时，则预测值小于真实值。ROC为受试者操作特征曲线，ROC曲线下面积为0.626(95%CI：58.0%~67.3%，P<0.001)。
[1]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020 [J]. CA Cancer J Clin, 2020, 70(1): 7-30.
[2]
Forbes C, Fayter D, de Kock S, et al. A systematic review of international guidelines and recommendations for the genetic screening, diagnosis, genetic counseling, and treatment of BRCA-mutated breast cancer [J]. Cancer Manag Res, 2019, 11: 2321-2337.
[3]
Kopp F, Mendell JT. Functional classification and experimental dissection of long noncoding RNAs [J]. Cell, 2018, 172(3): 393-407.
[4]
Dong S, Qu X, Li W, et al. The long non-coding RNA, GAS5, enhances gefitinib-induced cell death in innate EGFR tyrosine kinase inhibitor-resistant lung adenocarcinoma cells with wide-type EGFR via downregulation of the IGF-1R expression [J]. J Hematol Oncol, 2015, 8: 43.
[5]
Wang Y, Gong H, Cao Y. LncRNA WAC-AS1 expression in human tumors correlates with immune infiltration and affects prognosis [J]. Hereditas, 2023, 160(1): 26.
[6]
Zheng J, Zhou Z, Qiu Y, et al. A prognostic ferroptosis-related lncRNAs signature associated with immune landscape and radiotherapy response in glioma [J]. Front Cell Dev Biol, 2021, 9: 675 555.
[7]
Cai J, Qiu J, Wang H, et al. Identification of potential biomarkers in ovarian carcinoma and an evaluation of their prognostic value [J]. Ann Transl Med, 2021, 9(18): 1472.
[8]
Xia X, Zhang H, Xia P, et al. Identification of glycolysis-related lncRNAs and the novel lncRNA WAC-AS1 promotes glycolysis and tumor progression in hepatocellular carcinoma [J]. Front Oncol, 2021, 11: 733 595.
[9]
Deets KA, Vance RE. Inflammasomes and adaptive immune responses [J]. Nat Immunol, 2021, 22(4): 412-422.
[10]
Verneau J, Sautes-Fridman C, Sun CM. Dendritic cells in the tumor microenvironment: prognostic and theranostic impact [J]. Semin Immunol, 2020, 48：101 410.
[11]
Mathan TS, Figdor CG, Buschow SI. Human plasmacytoid dendritic cells: from molecules to intercellular communication network [J]. Front Immunol, 2013, 4: 372.
[12]
Mariathasan S, Turley SJ, Nickles D, et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells [J]. Nature, 2018, 554(7693): 544-548.
[13]
Schakel K. Dendritic cells——why can they help and hurt us [J]. Exp Dermatol, 2009, 18(3): 264-273.
[14]
Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles [J]. Nat Methods, 2015, 12(5): 453-457.
[15]
Fricke I, Gabrilovich DI. Dendritic cells and tumor microenvironment: a dangerous liaison [J]. Immunol Invest, 2006, 35(3-4): 459-483.
[16]
Wu T, Dai Y. Tumor microenvironment and therapeutic response [J]. Cancer Lett, 2017, 387: 61-68.
[17]
Fu C, Jiang A. Dendritic cells and CD8 T cell immunity in tumor microenvironment [J]. Front Immunol, 2018, 9: 3059.
[18]
Karami Fath M, Azargoonjahromi A, Kiani A, et al. The role of epigenetic modifications in drug resistance and treatment of breast cancer [J]. Cell Mol Biol Lett, 2022, 27(1): 52.
[19]
King MC, Marks JH, Mandell JB, et al. Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2 [J]. Science, 2003, 302(5645): 643-646.
[20]
Dang CV. MYC on the path to cancer [J]. Cell, 2012, 149(1): 22-35.
[21]
Johnson J, Thijssen B, McDermott U, et al. Targeting the RB-E2F pathway in breast cancer [J]. Oncogene, 2016, 35(37): 4829-4835.
[22]
Zheng Y, Jia H, Wang P, et al. Silencing TRAIP suppresses cell proliferation and migration/invasion of triple negative breast cancer via RB-E2F signaling and EMT [J]. Cancer Gene Ther, 2023, 30(1): 74-84.
[23]
Huang L, Zhao S, Frasor JM, et al. An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors [J]. PLoS One, 2011, 6(7): e22274.
[24]
Saxton RA, Sabatini DM. mTOR signaling in growth, metabolism, and disease [J]. Cell, 2017, 168(6): 960-976.
[25]
Miricescu D, Totan A, Stanescu S, et al. PI3K/AKT/mTOR signaling pathway in breast cancer: from molecular landscape to clinical aspects [J]. Int J Mol Sci, 2020, 22(1).
[26]
Tesio M, Trinquand A, Ballerini P, et al. Age-related clinical and biological features of PTEN abnormalities in T-cell acute lymphoblastic leukaemia [J]. Leukemia, 2017, 31(12): 2594-2600.
[27]
Redman-Rivera LN, Shaver TM, Jin H, et al. Acquisition of aneuploidy drives mutant p53-associated gain-of-function phenotypes [J]. Nat Commun, 2021, 12(1): 5184.
[28]
Hassin O, Oren M. Drugging p53 in cancer: one protein, many targets [J]. Nat Rev Drug Discov, 2023, 22(2): 127-144.
[29]
Fedorov AN. Biosynthetic protein folding and molecular chaperons [J]. Biochemistry (Mosc), 2022, 87(Suppl 1): S128-S119.
[30]
Itano N, Iwamoto S. Dysregulation of hexosamine biosynthetic pathway wiring metabolic signaling circuits in cancer [J]. Biochim Biophys Acta Gen Subj, 2023, 1867(1): 130250.
[31]
Chen LL, Wang WJ. p53 regulates lipid metabolism in cancer [J]. Int J Biol Macromol, 2021, 192: 45-54.
[32]
Szwed A, Kim E, Jacinto E. Regulation and metabolic functions of mTORC1 and mTORC2 [J]. Physiol Rev, 2021, 101(3): 1371-1426.
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