| Liver cancer(LC)is the third most common malignancy in the world after lung and colorectal cancers.It is the fifth and seventh most common cancer in men and women.The global distribution of liver cancer varies by country,with it being the most widely prevalent cancer in Asia or Africa.There are multiple risk factors that increase the risk of developing liver cancer,like hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin exposure,non-alcoholic fatty liver disease and autoimmune cases.Liver cancer has no obvious symptoms in the early stage and has entered the middle to late stage once discomfort is felt.After treatment patients overall survival rate rises modestly(5-year incidence up to more than 70%,with survival rates of only 47%-53%)and recurrence and metastasis rates remain high.Therefore,the key to address the late diagnosis,poor prognosis and high mortality of liver cancer is extensive screening.In this study,we address the clinical need for liver cancer screening as well as prognostic assessment.At the cellular and serum levels,laser tweezers Raman spectroscopy(LTRS)and surface-enhanced Raman spectroscopy(SERS)were used to investigate the early screening and prognostic assessment of liver cancer.The main research of this paper is shown below.1.Discriminant analysis of LC cells of multiple species and different degrees of differentiation using laser tweezers Raman spectroscopy system.In this study,LC cells(hepatocytes,Low and high differentiated LC cell lines)were captured by using a laser light tweezer system built in the laboratory.The Raman spectral signals of the cells were detected in real time without affecting the cell activity.Firstly,the differences of biochemical components between different species of LC cells and cells with various differentiation levels were analyzed by cellular Raman spectral peak attribution.Then,the diagnostic sensitivity and specificity were obtained by Deep neural networks(DNN)deep learning algorithm for in-depth analysis and discrimination,with mean values of 99.22%and 99.84%,respectively.As a result,LTRS-DNN coupling can provide more accurate feedback of biochemical information at the cellular molecular level.It can provide a fast and convenient methodological reference for in single-cell assay studies.2.Perform dual-mode in situ detection of circular non-coding RNAs(circ RNAs)in in vivo and in vitro models using labeled surface enhanced Raman spectroscopy technology.To investigate the therapeutic role of this novel nanoprobe for cancer treatment.To investigate the therapeutic role of this novel nanoprobe for cancer treatment.A composite probe(Au RFs)composed of double-signal-labeled DNA duplexes and Au nanoparticles(Au NPs)successfully identified circ RNAs.In addition,the ratiometric detection of double-labeled c SMARCA5 performed well in the actual sample assay(R~2=0.98566)reaching an ultra-low detection limit of 4.50 f M.Au RFs promote the expression of c SMARCA5 and mi R-17.Inhibited migration and invasion of liver cancer cells in an in vitro model.Inhibited metastasis of liver cancer transplants in a zebrafish in vivo model.The study suggests that this novel composite probe may provide a promising approach for medical diagnosis and cancer treatment with small molecule tumor suppressors.3.Quantitative detection and cellular in situ detection of liver cancer-associated long non-coding RNA(lnc RNA)using a SERS sensor with a self-calibrating bimetallic nano-DNA probe.And preliminary investigation of the biological function of this lnc RNA.Nanoprobes were formed with a relatively large volume of silver-coated gold rods and a relatively small volume of gold spheres linked to DNA,and the dual probes were used to co-recognize the lnc RNA.Assembly of both nanoprobes was promoted by Lnc2 targeting.Reliable quantification of lnc RNA was achieved under internal standard calibration(R~2=0.9923)and an ultra-low detection limit of 5.33 a M was reached.SERS imaging showed that Lnc2 was highly expressed in liver cancer cells and mainly distributed in the cytoplasm.Besides,Lnc2 promotes the migration and invasion of liver cancer by inhibiting the expression of death protein kinase.This technique enables reliable and ultra-sensitive detection of liver cancer-associated lnc RNAs and preliminary investigation of the mechanism of liver carcinogenesis.This provides a potential technical tool for the development of SERS-based technology for the study of liver carcinogenesis and progression.4.Development of a reliable and self-calibrating SERS sensor with cyclic amplification.To simultaneously quantify circ RNA and lnc RNA tumor markers in serum with high accuracy.Identification and cyclic amplification of target RNA by a catalytic hairpin assembly(CHA)device to obtain capture DNA.The captured DNA assembles a two-dimensional SERS substrate with self-alignment function and a core-shell nanoparticle with stable signal enhancement to form an enhanced“hot spot”.This allows for the simultaneous ultra-sensitive quantification of circ RNA(R~2=0.994)and Lnc RNA(R~2=0.997)in serum with internal standard calibration.It reached the ultra-low detection limit of 7.84 a M(c SMARCA5)and 52.35 a M(Lnc2).It is promising for the clinical application of SERS technology in the diagnosis and prognosis assessment of liver cancer.5.Using a laboratory-made high-throughput SERS assay platform combined with machine learning algorithms to analyze the sera of liver cancer patients for prognostic assessment.In this study,serum samples from 146 patients with recurrent liver cancer and149 patients with non-recurrent liver cancer and 295 healthy individuals were tested for SERS.There were significant differences in the serum SERS spectral characteristics of patients with liver cancer whose biological fractions in serum are altered after treatment.A machine learning algorithm(Support vector machine,SVM)was used to triple-classify the three groups of healthy individuals,recurrent and non-recurrent liver cancer.It was possible to achieve 100%differentiation among healthy individuals with an average sensitivity of 84.6%,an average specificity of 95.0%and an accuracy of 88.8%for discrimination.The results suggest that the serum-SERS-SVM technique can be used as a simple and rapid tool.It will have great potential in the field of screening and prognosis of liver cancer.This paper addresses the urgent need for the development and progression of liver cancer,early screening and prognostic assessment.It is conducted at the cellular,humoral and animal model levels in combination with SERS technology.Which will play an important role in the diagnosis,disease course monitoring and prognosis of liver cancer.It will have important theoretical significance and scientific value.The LTRS and SERS technologies used are also expected to be extended for basic cancer research,early screening,efficacy follow-up and prognostic evaluation.This would have potential applications in clinical and basic biological research. |
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