CHEMICAL ENGINEERING JOURNAL
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2025
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IF 13.2
Luyun Xu,Yongshi Shen,Zhizhong Lin,Jianqing Ye,Lingna Wang,Xueliang Lin,Shuhang Liu,Duo Lin,Yuanji Xu,Zongyang Yu,Rongcai Zhang,Shangyuan Feng
Lung cancer remains the leading cause of cancer-related deaths worldwide, mainly due to challenges in early detection that often result in poor patient outcomes. In this study, we developed a magnetic biosensing platform based on the coupling of CHA and SERS technologies for one-step detection of lung cancer-related circRNAs. Our approach capitalizes on forming “hot spots” between core–shell SERS nanoprobes and magnetic nanoparticles with precisely defined functionalities, generating a robust Raman signal. This signal is further amplified through magnetic enrichment and calibrated with an internal standard molecule, achieving a remarkably sensitive detection limit of 5.5 aM. Notably, our circRNA detection method significantly surpasses conventional tumor markers’ sensitivity. Validation using human serum samples demonstrated considerably higher circRNA levels in lung cancer patients than healthy controls. Moreover, the core–satellite structured magnetic SERS platform successfully distinguished unique circRNA concentration profiles across early-stage (IA and IB) lung cancers and their subtypes (IA1, IA2, IA3). Collectively, these results underscore the potential of our magnetic SERS platform as a liquid biopsy-based prognostic tool, enhancing early screening sensitivity and thereby facilitating more accurate diagnosis and improved patient stratification.
研究领域:
医学诊断
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纳米技术
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癌症研究
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产品关联
DOI
高影响因子
JOURNAL OF PROTEOME RESEARCH
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2021
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IF 4.466
Xinrong Jiang,Xiaoming Chen,Zhao Chen,Jiekai Yu,Haizhou Lou,Jianmin Wu
Lung cancer (LC) is a widespread cancer that is the cause of the highest mortality rate accounting for 25% of all cancer deaths. To date, most LC patients are diagnosed at the advanced stage owing to the lack of obvious symptoms in the early stage and the limitations of current clinical diagnostic techniques. Therefore, developing a high throughput technique for early screening is of great importance. In this work, we established an effective and rapid salivary metabolic analysis platform for early LC diagnosis and combined metabolomics and transcriptomics to reveal the metabolic fluctuations correlated to LC. Saliva samples were collected from a total of 150 volunteers including 89 patients with early LC, 11 patients with advanced LC, and 50 healthy controls. The metabolic profiling of noninvasive samples was investigated on an ultralow noise TELDI-MS platform. In addition, data normalization methods were screened and assessed to overcome the MS signal variation caused by individual difference for biomarker mining. For untargeted metabolic profiling of saliva samples, around 264 peaks could be reliably detected in each sample. After multivariate analysis, 23 metabolites were sorted out and verified to be related to the dysfunction of the amino acid and nucleotide metabolism in early LC. Notably, transcriptomic data from online TCGA repository were utilized to support findings from the salivary metabolomics experiment, including the disorder of amino acid biosynthesis and amino acid metabolism. Based on the verified differential metabolites, early LC patients could be clearly distinguished from healthy controls with a sensitivity of 97.2% and a specificity of 92%. The ultralow noise TELDI-MS platform displayed satisfactory ability to explore salivary metabolite information and discover potential biomarkers that may help develop a noninvasive screening tool for early LC.
研究领域:
医学
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生物化学
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肿瘤学
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DOI
高影响因子
MICROCHEMICAL JOURNAL
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2024
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IF 4.9
Bohan Zhang,Qingling Nie,Xin Yan,Qiaojun Jiang,Junjie Ren,Peipei Xu,Dechan Lu,Ruiyun You,Lizhi Li,Yudong Lu
Lung cancer is extremely lethal and early screening can be an effective treatment for lung cancer. Liquid biopsies allow for rapid, non-destructive testing and are promising for early screening. Plasma is rich in biomarkers and has been widely used for cancer diagnosis. Exosomes derived from plasma are considered a more promising biomarker. This paper reports a label-free detection strategy for plasma and exosomes of cancer-free (HC), adenocarcinoma in situ (AIS) and early-stage invasive adenocarcinoma (IAC) using surface-enhanced Raman spectroscopy (SERS). SERS spectra of plasma and exosomes were trained one to each other using four machine learning algorithms. Notably, this work breaks new ground by proposing a SERS “Combined Spectrum” of plasma combined with exosomes. The SERS “Combined Spectrum” synthesizes SERS spectra of plasma and exosomes from the same case sample into a completely new spectrum for training purposes. Calculated by each of the four machine learning algorithms, the plasma and exosome SERS “Combined Spectrum” were calculated with an accuracy higher than 97 % and an AUC value higher than 0.9500. Based on these results, the combined SERS “Combined Spectrum” of plasma and exosome can be used for the early detection of lung cancer, and it also has a promising future for application in the detection of other diseases.
研究领域:
肿瘤学
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医学诊断
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生物物理学
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产品关联
DOI
高影响因子
CHEMICAL ENGINEERING JOURNAL
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2024
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IF 13.3
Dechan Lu,Yanping Chen,Longfeng Ke,Zufang Huang,Yudong Lu,Jing Wang
DNA methylation is a promising biomarker for early cancer detection; however, traditional detection methods require bisulfite treatment and nucleic acid amplification, which are costly and impractical for point-of-care use. We propose a facile method that leverages immunoassays and surface-enhanced Raman scattering (SERS) readouts for the multiplex, sensitive, and portable detection of lung cancer-related DNA methylation biomarkers. This approach employs 5-methylcytosine antibody-modified magnetic probes to enrich methylated DNA sequences and uses core–shell nanoparticles with Probe DNA as SERS nanotags for multiplex detection, eliminating the need for bisulfite treatment and nucleic acid amplification. The multiplex SERS immunoassay detects synthetic methylated SHOX2 and RASSF1A with detection limits of 0.52 pM and 0.66 pM, respectively. It shows a strong correlation with quantitative PCR results in analyzing methylated SHOX2 and RASSF1A from cell lines and formalin-fixed paraffin-embedded tissue samples (n = 35). Additionally, the random forest analysis based on methylated SHOX2 and RASSF1A expression distinguishes lung cancer from benign lung diseases with a clinical sensitivity of 96 %, specificity of 90 %, and an AUC value of 0.972. This method offers a significant advancement for DNA methylation research and clinical applications.
研究领域:
癌症生物学
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分子诊断学
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纳米技术
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生物化学
✓ 已验证
产品关联
DOI
高影响因子
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