Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

Wang, Yanfeng; Yang, Yan; Sun, Junwei; Wang, Lidong; Song, Xin; Zhao, Xueke

doi:10.3389/fgene.2020.595638

Cited by 4 publications

(1 citation statement)

References 37 publications

(33 reference statements)

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“…Lower degrees of differentiation are associated with higher malignancy and lower survival rates. [ 22 ] The CLSM imaging revealed a gradual increase in fluorescence intensity of FAM in GES‐1, NCI‐N87, SGC‐7901, and MKN‐45 cells, indicating a higher concentration of circHIPK3 as cell malignancy increased ( Figure 4 a,b ). Conversely, the fluorescence intensity of ROX decreased in NCI‐N87, SGC‐7901, and MKN‐45 cells, suggesting a lower concentration of miR‐107 as cell malignancy increased.…”

Section: Resultsmentioning

confidence: 99%

DNA Framework‐Based Programmable Atom‐Like Nanoparticles for Non‐Coding RNA Recognition and Differentiation of Cancer Cells

Zhu,

Yang,

Ouyang

et al. 2024

Advanced Science

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The cooperative diagnosis of non‐coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework‐based programmable atom‐like nanoparticles (PANs) are designed to develop molecular classifiers for intra‐cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs‐based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high‐fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non‐amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs‐based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression.

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Section: Resultsmentioning

confidence: 99%

DNA Framework‐Based Programmable Atom‐Like Nanoparticles for Non‐Coding RNA Recognition and Differentiation of Cancer Cells

Zhu,

Yang,

Ouyang

et al. 2024

Advanced Science

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Prognostic Staging System for Esophageal Cancer Using Lasso, Cox and CS-SVM

Liu

Zhang

Sun

et al. 2022

Communications in Computer and Information Science

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Survival Prediction of Esophageal Squamous Cell Carcinoma Based on the Prognostic Index and Sparrow Search Algorithm-Support Vector Machine

Sun

Wang

Zhang

et al. 2023

CBIO

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Aim: Esophageal squamous cell carcinoma (ESCC) is one of the highest incidence and mortality cancers in the world, and recent studies show that the incidence of ESCC is on the rise, and the mortality rate remains high. An effective survival prediction model can assist physicians in treatment decisions and improve the quality of patient survival. Introduction: In this study, ESCC prognostic index and survival prediction model based on blood indicators and TNM staging information are developed, and their effectiveness is analyzed. Method: Kaplan-Meier survival analysis and COX regression analysis are used to find influencing factors that are significantly associated with patient survival. The binary logistic regression method is utilized to construct a prognostic index (PI) for esophageal squamous cell carcinoma (ESCC). Based on the sparrow search algorithm (SSA) and support vector machine (SVM), a survival prediction model for patients with ESCC is established. Results: Eight factors significantly associated with patient survival are selected by Kaplan-Meier survival analysis and COX regression analysis. PI is divided into four stages, and the stages can reasonably reflect the survival condition of diverse patients. Compared with the other four existing models, the sparrow search algorithm-support vector machine (SSA-SVM) proposed in this paper has higher prediction accuracy. Conclusion: In order to accurately and effectively predict the five-year survival rate of patients with ESCC, a survival prediction model based on Kaplan-Meier survival analysis, COX regression analysis, binary logistic regression and support vector machine is proposed in this paper. The results show that the method proposed in this paper can accurately predict the five-year survival rate of ESCC patients.

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Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

Cited by 4 publications

References 37 publications

DNA Framework‐Based Programmable Atom‐Like Nanoparticles for Non‐Coding RNA Recognition and Differentiation of Cancer Cells

DNA Framework‐Based Programmable Atom‐Like Nanoparticles for Non‐Coding RNA Recognition and Differentiation of Cancer Cells

Prognostic Staging System for Esophageal Cancer Using Lasso, Cox and CS-SVM

Survival Prediction of Esophageal Squamous Cell Carcinoma Based on the Prognostic Index and Sparrow Search Algorithm-Support Vector Machine

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