Prognosis prediction model for squamous cell carcinoma and application thereof

A predictive model, technology for lung squamous cell carcinoma, applied in the field of biomedicine

Inactive Publication Date: 2021-05-18
JINSHAN HOSPITAL FUDAN UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

About a kind of lung squamous cell carcinoma prognosis prediction model of the present invention and its application, there is no report yet

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  • Prognosis prediction model for squamous cell carcinoma and application thereof
  • Prognosis prediction model for squamous cell carcinoma and application thereof
  • Prognosis prediction model for squamous cell carcinoma and application thereof

Examples

Experimental program
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Effect test

Embodiment 1

[0031] Example 1 Model Construction and Effect Verification

[0032] 1. Method

[0033] 1.1 Obtain the RNA sequencing data and clinical data of 326 cases of lung squamous cell carcinoma from the TCGA database, obtain the expression profile of autophagy-related genes (Autophagy-Related-Genes, ARGs), KM survival analysis screens out the ARGs related to prognosis, and draws the Kaplan-Meier survival P-values ​​were calculated from curves and log-rank tests.

[0034] 1.2 The random forest machine learning method screened survival-related sARGs to obtain four prediction genes most related to survival, and then built a prediction model based on these four genes. (Cox regression hazard ratio model formula: Risk Score=1.313*RGS19+1.161*PINK1+1.037*CTSD+1.098*CFLAR) Patients were divided into low-risk group and high-risk group according to the risk score obtained by the model. The model was validated by receiver operating characteristic curve ROC analysis, log-rank test of KM surviva...

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Abstract

The invention relates to a prognosis prediction model for squamous cell carcinoma and application thereof. The prognosis prediction model is composed of biomarkers CFLAR, RGS19, PINK1, CTSD and related solutions. The invention also comprises application of a reagent for detecting the expression quantity of the biomarkers in preparation of a kit for evaluating the anti-tumor immunotherapy reactivity and prognosis survival of squamous cell carcinoma. According to the invention, screening and construction are carried out after whole transcriptome sequencing and machine learning of squamous cell carcinoma samples of large-sample anti-tumor immunotherapy, so that the reactivity of squamous cell carcinoma patients in anti-tumor immunotherapy can be efficiently and accurately predicted, effective guidance is provided for clinicians to make treatment decisions on squamous cell carcinoma patients, and the occurrence of invalid treatment is reduced, thereby reducing the treatment cost and discomfort experience of patients.

Description

technical field [0001] The invention relates to the technical field of biomedicine, in particular to a lung squamous cell carcinoma prognosis prediction model and its application. Background technique [0002] Lung cancer is the most common cause of cancer-related death in the world today, and 80% of it is non-small cell lung cancer (NSCLC). TNM staging is a generally accepted clinical staging system, which is used to predict the prognosis and guide the treatment of patients with non-small cell lung cancer. However, the current TNM staging system is far from adequate to accurately predict the prognosis of NSCLC patients. For example, for lung cancer patients, even in clinical stage I, the recurrence rate of lung cancer is as high as 35-50%. In addition, a considerable number of patients can be cured only by surgery, and these patients should be able to avoid the extremely strong side effects of adjuvant chemotherapy based on the current TNM system. [0003] Squamous cell ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886A61K45/00A61P35/00G16H50/20G16H50/50G16B35/00G16B30/00G16B40/00
CPCC12Q1/6886A61K45/00A61P35/00G16H50/20G16H50/50G16B35/00G16B30/00G16B40/00C12Q2600/158C12Q2600/118C12Q2600/136
Inventor 乔田奎罗露梦武多娇庄喜兵
Owner JINSHAN HOSPITAL FUDAN UNIV
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