Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs.
Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs.
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DOI：
10.1021/acsanm.2c01986
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发表时间：
2022-09-23
影响因子：
5.9
通讯作者：
Xie, Ya-Hong
中科院分区：
文献类型：
--
作者：
Liu, Zirui;Li, Tieyi;Wang, Zeyu;Liu, Jun;Huang, Shan;Min, Byoung Hoon;An, Ji Young;Kim, Kyoung Mee;Kim, Sung;Chen, Yiqing;Liu, Huinan;Kim, Yong;Wong, David T. W.;Huang, Tony Jun;Xie, Ya-Hong
关键词：
胃癌（GC）是最常见和致命的癌症类型之一，影响超过100万人，仅在2020年全球就导致768，793人死亡。提高生存率的关键在于可靠的筛查和早期诊断。现有的技术，包括钡餐胃荧光照相术和上消化道内窥镜检查可能是昂贵和耗时的，因此是不切实际的人口筛查。相反，我们寻找大小为30-150 nm的小细胞外囊泡（sEV，目前也称为外泌体）作为候选物。在过去的十年或二十年中，sEV由于其在疾病诊断和治疗中的潜力而引起了显著更高水平的关注。在这里，我们报告说，通过表面增强拉曼光谱（Sers）获得的人类供体的sEV内的集体拉曼活性键的组成信息持有非侵入性GC检测的潜力。Sers是由我们以前开发的金纳米锥阵列的基底触发的。开发了一种基于机器学习的光谱特征分析算法，用于客观区分癌症来源的sEV与非癌症亚群的sEV。收集并分析来自GC患者和非GC参与者的组织、血液和唾液的sEV（各n = 15）。据报道，该算法的预测准确率分别为90%、85%和72%。进一步进行了“留一对样本”验证，以测试临床潜力。在组织、血液和唾液中，每个受试者工作特征曲线的曲线下面积分别为0.96、0.91和0.65。此外，通过比较单个囊泡的Sers指纹，我们提供了一种可能的方法来追踪患者特异性sEV从组织到血液再到唾液的生物发生途径。本研究所涉及的方法有望适用于非侵入性检测GC以外的疾病。
Gastric cancer (GC) is one of the most common and lethal types of cancer affecting over one million people, leading to 768,793 deaths globally in 2020 alone. The key for improving the survival rate lies in reliable screening and early diagnosis. Existing techniques including barium-meal gastric photofluorography and upper endoscopy can be costly and time-consuming and are thus impractical for population screening. We look instead for small extracellular vesicles (sEVs, currently also referred as exosomes) sized ⌀ 30–150 nm as a candidate. sEVs have attracted a significantly higher level of attention during the past decade or two because of their potentials in disease diagnoses and therapeutics. Here, we report that the composition information of the collective Raman-active bonds inside sEVs of human donors obtained by surface-enhanced Raman spectroscopy (SERS) holds the potential for non-invasive GC detection. SERS was triggered by the substrate of gold nanopyramid arrays we developed previously. A machine learning-based spectral feature analysis algorithm was developed for objectively distinguishing the cancer-derived sEVs from those of the non-cancer sub-population. sEVs from the tissue, blood, and saliva of GC patients and non-GC participants were collected (n = 15 each) and analyzed. The algorithm prediction accuracies were reportedly 90, 85, and 72%. “Leave-a-pair-of-samples out” validation was further performed to test the clinical potential. The area under the curve of each receiver operating characteristic curve was 0.96, 0.91, and 0.65 in tissue, blood, and saliva, respectively. In addition, by comparing the SERS fingerprints of individual vesicles, we provided a possible way of tracing the biogenesis pathways of patient-specific sEVs from tissue to blood to saliva. The methodology involved in this study is expected to be amenable for non-invasive detection of diseases other than GC.
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影响因子：
16.6
作者：
Haldavnekar R;Venkatakrishnan K;Tan B
通讯作者：
Tan B
影响因子：
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作者：
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Soares Costa, Sandra Santana
DOI：
10.1039/c5an01610j
发表时间：
2016-01-21
期刊：
The Analyst
影响因子：
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通讯作者：
Issadore D
影响因子：
7.4
作者：
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通讯作者：
Chiu, Daniel T.