PDF(5153 KB)
从结直肠癌细胞和组织样本释放的外泌体中检测肿瘤相关抗原自身抗体的多路生物传感诊断平台展示了对结直肠癌的高诊断能力
Ana Montero-Calle, Itziar Aranguren-Abeigon, María Garranzo-Asensio, Carmen Poves, María Jesús Fernández-Aceñero, Javier Martínez-Useros, Rodrigo Sanz, Jana Dziaková, Javier Rodriguez-Cobos, Guillermo Solís-Fernández, Eloy Povedano, Maria Gamella, Rebeca MagnoliaTorrente-Rodríguez, Miren Alonso-Navarro, Vivian de los Ríos, J. Ignacio Casal, Gemma Domínguez, Ana Guzman-Aranguez, Alberto Peláez-García, José Manuel Pingarrón, Susana Campuzano, Rodrigo Barderas
工程（英文） ›› 2021, Vol. 7 ›› Issue (10) : 1393-1412.
PDF(5153 KB)
PDF(5153 KB)
从结直肠癌细胞和组织样本释放的外泌体中检测肿瘤相关抗原自身抗体的多路生物传感诊断平台展示了对结直肠癌的高诊断能力
Multiplexed Biosensing Diagnostic Platforms Detecting Autoantibodies to Tumor-Associated Antigens from Exosomes Released by CRC Cells and Tissue Samples Showed High Diagnostic Ability for Colorectal Cancer
结直肠癌（CRC）是全球导致癌症相关死亡的第二大原因。CRC患者的5年生存率与诊断分期息息相关。CRC早期诊断患者的5年生存率高于80%，CRC晚期诊断患者的5年生存率低于10%。大量研究表明，患者血清中特定CRC自身抗原[肿瘤相关抗原（TAA）]对应的自身抗体有助于早期诊断。因此，本文旨在识别CRC特异性自身抗体的自身抗原靶点，借助液体活检技术有效筛查CRC患者和健康个体。为此，我们从CRC患者体内提取5个CRC细胞系和组织样本，通过免疫沉淀-质谱分析法测量其分泌的外泌体的蛋白质含量，共鉴定出103种蛋白质为潜在的CRC特异性自身抗原。采用生物信息学技术和荟萃分析，我们选定了15种与实际CRC自身抗原类似度更高的蛋白质，以便后续借助蛋白质印迹法（WB）和免疫组织化学技术（IHC）评估它们在CRC预后中的作用。结果发现，在患者的组织和血浆外泌体样本中，有11种蛋白质发生蛋白质水平失调，有9种蛋白质与CRC预后有关。经验证发现，除一例外，所有研究均显示出具有统计学意义的高诊断能力，采用荧光Halotag磁珠，或者借助多路生物传感平台（磁性微载体为固相载体，由用于CRC细胞检测的共价固定Halotag融合蛋白修饰），能够有效筛查区分CRC患者、癌前病变个体与健康个体。综上所述，本文的研究结果突出了此研究方法在识别慢性疾病特征TAA时的有效性；此外，本文采用的测量血浆中TAA对应的自身抗体水平的方法可以应用到具有高诊断能力的CRC检测即时医疗（POC）设备中。
Colorectal cancer (CRC) is the second leading cause of cancer related death worldwide. The 5-year survival rate of CRC patients depends on the stage at diagnosis, being higher than 80% when CRC is diagnosed in the early stages but lower than 10% when CRC is diagnosed in advanced stages. Autoantibodies against specific CRC autoantigens (tumor-associated antigens (TAAs)) in the sera of patients have been widely demonstrated to aid in early diagnosis. Thus, we herein aim to identify autoantigens target of autoantibodies specific to CRC that possess a significant ability to discriminate between CRC patients and healthy individuals by means of liquid biopsy. To that end, we examined the protein content of the exosomes released by five CRC cell lines and tissue samples from CRC patients by means of immunoprecipitation coupled with mass spectrometry analysis. A total of 103 proteins were identified as potential autoantigens specific to CRC. After bioinformatics and meta-analysis, we selected 15 proteins that are more likely to be actual CRC autoantigens in order to evaluate their role in CRC prognosis by Western blot (WB) and immunohistochemistry (IHC). We found dysregulation at the protein level for 11 of these proteins in both tissue and plasma exosome samples from patients, along with an association of nine of these proteins with CRC prognosis. After validation, all but one showed a statistically significant high diagnostic ability to distinguish CRC patients and individuals with premalignant lesions from healthy individuals, either by luminescence Halotag-based beads, or by a multiplexed biosensing platform involving the use of magnetic microcarriers as solid support modified with covalently immobilized Halotag fusion proteins constructed for CRC detection. Taken together, our results highlight the usefulness of the approach defined here to identify the TAAs specific to chronic diseases; they also demonstrate that the measurement of autoantibody levels in plasma against the TAAs identified here could be integrated into a point-of-care (POC) device for CRC detection with high diagnostic ability.
Autoantibodies / Diagnosis / Colorectal cancer / Exosomes / Tumor microenviroment / Humoral immune response / Point of care / Biosensors
| [1] |
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68(6):394–424.
|
| [2] |
Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin 2020;70(3):145–64.
|
| [3] |
www.cancer.org [Internet]. Atlanta: American Cancer Society; 2020 [cited 2021 Feb 5]. Available from: https://www.cancer.org/cancer/colon-rectalcancer.
|
| [4] |
Balchen V, Simon K. Colorectal cancer development and advances in screening. Clin Interv Aging 2016;11:967–76.
|
| [5] |
Lech G, Słotwin´ ski R, Słodkowski M, Krasnode˛bski IW. Colorectal cancer tumour markers and biomarkers: recent therapeutic advances. World J Gastroenterol 2016;22(5):1745–55.
|
| [6] |
Kuipers EJ, Grady WM, Lieberman D, Seufferlein T, Sung JJ, Boelens PG, et al. Colorectal cancer. Nat Rev Dis Primers 2015;1(1):15065.
|
| [7] |
Ignacio Casal J, Barderas R. Identification of cancer autoantigens in serum: toward diagnostic/prognostic testing? Mol Diagn Ther 2010;14(3):149–54.
|
| [8] |
Anderson KS, LaBaer J. The sentinel within: exploiting the immune system for cancer biomarkers. J Proteome Res 2005;4(4):1123–33.
|
| [9] |
Barderas R, Villar-Vázquez R, Fernández-Aceñero MJ, Babel I, Peláez-García A, Torres S, et al. Sporadic colon cancer murine models demonstrate the value of autoantibody detection for preclinical cancer diagnosis. Sci Rep 2013;3 (1):2938.
|
| [10] |
Macdonald IK, Parsy-Kowalska CB, Chapman CJ. Autoantibodies: opportunities for early cancer detection. Trends Cancer 2017;3(3):198–213.
|
| [11] |
Barderas R, Babel I, Casal JI. Colorectal cancer proteomics, molecular characterization and biomarker discovery. Proteomics Clin Appl 2010;4 (2):159–78.
|
| [12] |
Barderas R, Villar-Vázquez R, Casal JI. Colorectal cancer circulating biomarkers. In: Preedy V, Patel V, editors. Biomarkers in cancer. Amsterdam: Springer; 2015. p. 1–21.
|
| [13] |
Babel I, Barderas R, Díaz-Uriarte R, Martínez-Torrecuadrada JL, SánchezCarbayo M, Casal JI. Identification of tumor-associated autoantigens for the diagnosis of colorectal cancer in serum using high density protein microarrays. Mol Cell Proteomics 2009;8(10):2382–95.
|
| [14] |
Garranzo-Asensio M, San Segundo-Acosta P, Povés C, Fernández-Aceñero MJ, Martínez-Useros J, Montero-Calle A, et al. Identification of tumor-associated antigens with diagnostic ability of colorectal cancer by in-depth immunomic and seroproteomic analysis. J Proteomics 2020;214:103635.
|
| [15] |
Colombo M, Raposo G, Théry C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol 2014;30(1):255–89.
|
| [16] |
Vader P, Breakefield XO, Wood MJ. Extracellular vesicles: emerging targets for cancer therapy. Trends Mol Med 2014;20(7):385–93.
|
| [17] |
Wee I, Syn N, Sethi G, Goh BC, Wang L. Role of tumor-derived exosomes in cancer metastasis. Biochim Biophys Acta Rev Cancer 2019;1871(1):12–9.
|
| [18] |
Steinbichler TB, Dudás J, Riechelmann H, Skvortsova II. The role of exosomes in cancer metastasis. Semin Cancer Biol 2017;44:170–81.
|
| [19] |
Syn N, Wang L, Sethi G, Thiery JP, Goh BC. Exosome-mediated metastasis: from epithelial–mesenchymal transition to escape from immunosurveillance. Trends Pharmacol Sci 2016;37(7):606–17.
|
| [20] |
Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D, Burton O, et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med 2018;24(8):1277–89.
|
| [21] |
Vogiazi V, de la Cruz A, Mishra S, Shanov V, Heineman WR, Dionysiou DD. A comprehensive review: development of electrochemical biosensors for detection of cyanotoxins in freshwater. ACS Sens 2019;4(5):1151–73.
|
| [22] |
Cui F, Zhou Z, Zhou HS. Measurement and analysis of cancer biomarkers based on electrochemical biosensors. J Electrochem Soc 2020;167(3):037525.
|
| [23] |
Campuzano S, Barderas R, Pedrero M, Yáñez-Sedeño P, Pingarrón JM. Electrochemical biosensing to move forward in cancer epigenetics and metastasis: a review. Anal Chim Acta 2020;1109:169–90.
|
| [24] |
Morikawa K, Walker SM, Nakajima M, Pathak S, Jessup JM, Fidler IJ. Influence of organ environment on the growth, selection, and metastasis of human colon carcinoma cells in nude mice. Cancer Res 1988;48(23):6863–71.
|
| [25] |
Morikawa K, Walker SM, Jessup JM, Fidler IJ. In vivo selection of highly metastatic cells from surgical specimens of different primary human colon carcinomas implanted into nude mice. Cancer Res 1988;48(7):1943–8.
|
| [26] |
Barderas R, Babel I, Díaz-Uriarte R, Moreno V, Suárez A, Bonilla F, et al. An optimized predictor panel for colorectal cancer diagnosis based on the combination of tumor-associated antigens obtained from protein and phage microarrays. J Proteomics 2012;75(15):4647–55.
|
| [27] |
Wis´niewski JR, Gaugaz FZ. Fast and sensitive total protein and peptide assays for proteomic analysis. Anal Chem 2015;87(8):4110–6.
|
| [28] |
Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 2007;4(11):923–5.
|
| [29] |
Mellacheruvu D, Wright Z, Couzens AL, Lambert JP, St-Denis NA, Li T, et al. The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods 2013;10(8):730–6.
|
| [30] |
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 2019;47(D1):D607–13.
|
| [31] |
Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al. DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007;35:W169–75.
|
| [32] |
Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003;4(9):R60.
|
| [33] |
Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, PonceRodriguez I, Chakravarthi BVSK, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 2017;19(8):649–58.
|
| [34] |
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2(5):401–4.
|
| [35] |
Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 2010;28 (12):1248–50.
|
| [36] |
Uhlen M, Fagerberg L, Hallstrom BM, Lindskog C, Oksvold P, Mardinoglu A, et al. Tissue-based map of the human proteome. Science 2015;347 (6220):1260419.
|
| [37] |
Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 2019;47(W1):W556–60.
|
| [38] |
Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017;45(1):W98–102.
|
| [39] |
Garranzo-Asensio M, San Segundo-Acosta P, Martínez-Useros J, Montero-Calle A, Fernández-Aceñero MJ, Häggmark-Månberg A, et al. Identification of prefrontal cortex protein alterations in Alzheimer’s disease. Oncotarget 2018;9(13):10847–67.
|
| [40] |
Seiler CY, Park JG, Sharma A, Hunter P, Surapaneni P, Sedillo C, et al. DNASU plasmid and PSI:biology-materials repositories: resources to accelerate biological research. Nucleic Acids Res 2014;42(D1):D1253–60.
|
| [41] |
Garranzo-Asensio M, Guzman-Aranguez A, Povés C, Fernández-Aceñero MJ, Torrente-Rodríguez RM, Ruiz-Valdepeñas Montiel V, et al. Toward liquid biopsy: determination of the humoral immune response in cancer patients using HaloTag fusion protein-modified electrochemical bioplatforms. Anal Chem 2016;88(24):12339–45.
|
| [42] |
Garranzo-Asensio M, Guzmán-Aránguez A, Povedano E, Ruiz-Valdepeñas Montiel V, Poves C, Fernandez-Aceñero MJ, et al. Multiplexed monitoring of a novel autoantibody diagnostic signature of colorectal cancer using HaloTag technology-based electrochemical immunosensing platform. Theranostics 2020;10(7):3022–34.
|
| [43] |
Montero-Calle A, San Segundo-Acosta P, Garranzo-Asensio M, Rábano A, Barderas R. The molecular misreading of APP and UBB induces a humoral immune response in Alzheimer’s disease patients with diagnostic ability. Mol Neurobiol 2020;57(2):1009–20.
|
| [44] |
Garranzo-Asensio M, Guzmán-Aránguez A, Povés C, Fernández-Aceñero MJ, Montero-Calle A, Ceron MÁ, et al. The specific seroreactivity to DNp73 isoforms shows higher diagnostic ability in colorectal cancer patients than the canonical p73 protein. Sci Rep 2019;9(1):13547.
|
| [45] |
San Segundo-Acosta P, Montero-Calle A, Fuentes M, Rábano A, Villalba M, Barderas R. Identification of Alzheimer’s disease autoantibodies and their target biomarkers by phage microarrays. J Proteome Res 2019;18(7): 2940–53.
|
| [46] |
Guo Y, Ji X, Liu J, Fan D, Zhou Q, Chen C, et al. Effects of exosomes on premetastatic niche formation in tumors. Mol Cancer 2019;18(1):39.
|
| [47] |
Sato K, Masuda T, Hu Q, Tobo T, Kidogami S, Ogawa Y, et al. Phosphoserine phosphatase is a novel prognostic biomarker on chromosome 7 in colorectal cancer. Anticancer Res 2017;37(5):2365–71.
|
| [48] |
Kawaguchi K, Senga S, Kubota C, Kawamura Y, Ke Y, Fujii H. High expression of fatty acid-binding protein 5 promotes cell growth and metastatic potential of colorectal cancer cells. FEBS Open Bio 2016;6(3):190–9.
|
| [49] |
Duan L, Wu R, Ye L, Wang H, Yang X, Zhang Y, et al. S100A8 and S100A9 are associated with colorectal carcinoma progression and contribute to colorectal carcinoma cell survival and migration via Wnt/b-catenin pathway. PLoS ONE 2013;8(4):e62092.
|
| [50] |
Zheng XF, Liu KX, Wang XM, Zhang R, Li X. MicroRNA-192 acts as a tumor suppressor in colon cancer and simvastatin activates miR-192 to inhibit cancer cell growth. Mol Med Rep 2019;19(3):1753–60.
|
| [51] |
Goldenring JR, Nam KT. Rab25 as a tumour suppressor in colon carcinogenesis. Br J Cancer 2011;104(1):33–6.
|
| [52] |
Otsubo T, Iwaya K, Mukai Y, Mizokami Y, Serizawa H, Matsuoka T, et al. Involvement of Arp2/3 complex in the process of colorectal carcinogenesis. Mod Pathol 2004;17(4):461–7.
|
| [53] |
Mendes M, Peláez-García A, López-Lucendo M, Bartolomé RA, Calviño E, Barderas R, et al. Mapping the spatial proteome of metastatic cells in colorectal cancer. Proteomics 2017;17(19):1700094.
|
| [54] |
Chen SW, Chou CT, Chang CC, Li YJ, Chen ST, Lin IC, et al. HMGCS2 enhances invasion and metastasis via direct interaction with PPARa to activate Src signaling in colorectal cancer and oral cancer. Oncotarget 2017;8 (14):22460–76.
|
| [55] |
Woischke C, Blaj C, Schmidt EM, Lamprecht S, Engel J, Hermeking H, et al. CYB5R1 links epithelial–mesenchymal transition and poor prognosis in colorectal cancer. Oncotarget 2016;7(21):31350–60.
|
| [56] |
Feng Y, Ji D, Huang Y, Ji B, Zhang Y, Li J, et al. TGM3 functions as a tumor suppressor by repressing epithelial-to-mesenchymal transition and the PI3K/ AKT signaling pathway in colorectal cancer. Oncol Rep 2020;43(3): 864–76.
|
| [57] |
Radeva MY, Jahns F, Wilhelm A, Glei M, Settmacher U, Greulich KO, et al. Defensin alpha 6 (DEFA 6) overexpression threshold of over 60 fold can distinguish between adenoma and fully blown colon carcinoma in individual patients. BMC Cancer 2010;10(1):588–93.
|
| [58] |
Luo H, Zhao Q, Wei W, Zheng L, Yi S, Li G, et al. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer. Sci Transl Med 2020;12(524):eaax7533.
|
| [59] |
Palmqvist R, Engarås B, Lindmark G, Hallmans G, Tavelin B, Nilsson O, et al. Prediagnostic levels of carcinoembryonic antigen and CA 242 in colorectal cancer: a matched case-control study 2003;46(11):1538–44.
|
| [60] |
Babel I, Barderas R, Diaz-Uriarte R, Moreno V, Suarez A, Salazar R, et al. Identification of MST1/STK4 and SULF1 proteins as autoantibody targets for the diagnosis of colorectal cancer by using phage microarrays 2011;10(3): M110.001784.
|
| [61] |
Povedano E, Montiel VR, Valverde A, Navarro-Villoslada F, Yáñez-Sedeño P, Pedrero M, et al. Versatile electroanalytical bioplatforms for simultaneous determination of cancer-related DNA 5-methyl- and 5-hydroxymethylcytosines at global and gene-specific levels in human serum and tissues. ACS Sens 2019;4(1):227–34.
|
/
| 〈 |
|
〉 |
