Application of lncRNA in diagnosis and prognostic prediction of liver cancer

A technology of liver cancer, CTD-2574D22.4, applied in the field of biomedicine

Inactive Publication Date: 2018-03-30
FUDAN UNIV SHANGHAI CANCER CENT
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the research on lncRNA molecules in the regulation mechanism of liver cancer is still in its infancy

Method used

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  • Application of lncRNA in diagnosis and prognostic prediction of liver cancer
  • Application of lncRNA in diagnosis and prognostic prediction of liver cancer
  • Application of lncRNA in diagnosis and prognostic prediction of liver cancer

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Example 1 QPCR detection of lncRNA expression level in patients with liver cancer

[0047] 1. Sample collection

[0048] The cancer tissues and paracancerous tissues of 84 liver cancer patients were collected, and the patients gave informed consent, and all the above specimens were obtained with the consent of the organizational ethics committee.

[0049] 2. RNA extraction

[0050] The tissue RNA extraction kit of Invitrogen was used to extract the tissue RNA, and the operation was performed according to the specific steps in the manual.

[0051] 3. Reverse transcription:

[0052] Use 25μl reaction system, take 1μg total RNA for each sample as template RNA, and add the following components to PCR tubes: DEPC water, 5× reverse transcription buffer, 10mM dNTP, 0.1mM DTT, 30μM Oligo dT, 200U / μl M-MLV, template RNA. Incubate at 42°C for 1 hour, then centrifuge briefly at 72°C for 10 minutes.

[0053] QPCR amplification test

[0054] Primers were designed according to ...

Embodiment 2

[0078] Example 2 Detection of the impact of IncRNA on the survival period of patients with liver cancer

[0079] 1. Sample

[0080]From the TCGA database, 364 cases of liver cancer tissues and paracancerous tissues of consecutive pathologically diagnosed and surgically resected liver cancer cases were screened and randomly divided into a training group (182 cases) and a verification group (182 cases). The clinical information of the included patients included age. , gender, tumor grade, tumor stage, new tumors, and survival information.

[0081] 2. Data analysis

[0082] The lncRNA expression and survival time data were extracted, and the coxph function of the survival package was used to perform univariate Cox regression analysis on the lncRNA data of the training group, and the p value in the univariate Cox regression was screened to obtain a p value <0.01.

[0083] 3. Results

[0084] Through univariate Cox regression analysis, it was found that differentially expressed ...

Embodiment 3

[0085] Example 3 Efficiency Test of RS Model in Predicting the Prognosis of Liver Cancer Patients

[0086] 1. Data analysis

[0087] The prognostic features of each screened lncRNA were analyzed by univariate Cox regression. In the training group, lncRNAs were associated with the overall survival of patients (P<0.01). The contribution of each lncRNA to survival prediction was then calculated using multivariate Cox regression in the R package. A risk scoring model is established to assess the risk of patients, and the scoring formula is as follows:

[0088] risk score Where N represents the number of lncRNAs used for prognosis prediction, Expi represents the expression level of lncRNAi, and Ci represents the regression coefficient of lncRNAi obtained from multivariate Cox regression analysis.

[0089] ROC curves and AUC curves were used to determine the sensitivity and specificity of RS model predictions. Kaplan-Meier survival curves were used to analyze the difference bet...

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Abstract

The invention discloses application of lncRNA in diagnosis and prognostic prediction of liver cancer. The lncRNA is selected from one or multiple of TD-2574D22.4, SERHL, MIR100HG and SNHG20, and experiments prove that lncRNA TD-2574D22.4, SERHL, MIR100HG and SNHG20 present differential expression in patients with liver cancer. The invention further discloses a risk scoring model for predicting prognosis of the liver cancer. The risk scoring model serves as an auxiliary means to predict prognosis of the patients with the liver cancer so as to perform risk evaluation and monitoring on the patients.

Description

technical field [0001] The invention belongs to the field of biomedicine and relates to the application of lncRNA in the diagnosis and prognosis prediction of liver cancer. Background technique [0002] Malignant tumor is the second largest disease that threatens human health. Although the mortality rate of malignant tumors has gradually decreased in recent years, the incidence of liver cancer has increased year by year. As a common malignant tumor, liver cancer ranks fourth in the incidence of malignant tumors in my country and second in the cause of death, especially in East Asia, Southeast Asia, Africa and Southern Europe. The current advanced surgical treatment for liver cancer and the application of targeted drugs such as sorafenib have significantly improved the quality of life of patients with liver cancer. The treatment effect is poor and the prognosis is poor. [0003] Long non-coding RNA (Long non-coding RNA, lncRNA) is a type of non-coding RNA with a length of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886
CPCC12Q1/6886C12Q2600/118C12Q2600/158C12Q2600/178
Inventor 王益林王鲁潘奇张宁毛岸荣林镇海
Owner FUDAN UNIV SHANGHAI CANCER CENT
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