Marker for predicting prognosis of colon cancer and application of marker

A colon cancer, marker technology, applied in the field of genetic technology and medicine, can solve the problem of ignoring clinical pathology and risk factors

Pending Publication Date: 2020-12-04
NANYANG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In addition, studies have reported the use of one or several single types of RNA as a marker for colon cancer prognosis (for example, patent document CN105463070A discloses the use of a lncRNA-UCA1 as a marker for colon cancer diagnosis / prognosis, and another document " Expression and Significance of Beclin1 and Livin in Colon Cancer and Colon Adenoma" Publication of mRNA Beclin1 and Livin combined detection is helpful for the prognosis of colon cancer), these are only based on the correlation between RNA levels and clinical prognosis, just ignore the clinical pathology and risk factors

Method used

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  • Marker for predicting prognosis of colon cancer and application of marker
  • Marker for predicting prognosis of colon cancer and application of marker
  • Marker for predicting prognosis of colon cancer and application of marker

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] Example 1 Screening of autophagy-related genes for prognosis prediction of colorectal cancer

[0046] Gene expression datasets and clinical information of COAD patient samples (41 normal samples and 473 tumor samples) were used from the publicly available TCGA dataset. The validation set used 124 colon cancer samples from the GEO dataset GSE72970. Autophagy genes were screened from the human autophagy database HADb to obtain 231 differentially expressed autophagy genes. Univariate Cox regression analysis was used in R software to evaluate whether autophagy-related RNAs were associated with OS. mRNAs with |log2FC|>1 and p2.5 and p1 were defined as risk genes.

[0047] Screening results: Differentially expressed autophagy-related mRNA heatmaps are shown in the accompanying drawings figure 1 As shown in A, the forest plot of the prognosis of autophagy-related mRNA is shown in figure 1 Shown in B; the heat map of differentially expressed autophagy-related lncRNAs is s...

Embodiment 2

[0048] Example 2 Establishing an autophagy-related prognostic risk model and using the risk model to predict the prognostic risk of colon cancer patients

[0049] Risk models were constructed separately for prognostic-related autophagy mRNAs and lncRNAs using multivariate Cox regression. The model analyzes each patient's risk score by incorporating the expression values ​​for each specific RNA, weighted by its estimated regression coefficient. According to the risk score formula, patients were divided into low-risk group and high-risk group with the median risk as the cut-off point. Survival differences between the two groups were assessed by KM and compared using the log-rank statistical method. Multivariate Cox regression analysis and stratified analysis were used to examine the role of the risk score in predicting patient outcomes.

[0050] The autophagy-related mRNA risk model (model 1) was constructed using the TCGA colon cancer dataset (colon cancer; n=514: 41normal sa...

Embodiment 3

[0066] Embodiment 3 establishes a nomograph based on a risk model (obtained by constructing a COX model)

[0067] Construction of a risk scoring system incorporating mRNA, NOMO of age and tumor stage figure 1 ;

[0068] Construction of a risk scoring system combining 14 lncRNAs, nomo of age and tumor stage figure 2 ;

[0069] Construct a risk scoring system combining 6 mRNAs and 14 lncRNAs and a nomo of age and tumor stage image 3 ;

[0070] According to the clinical test results, the scores are listed (Nomo Figure 1-3 The "risk score" in is calculated according to the formulas of the above models 1-3) to predict the 1-year, 3-year, 5-year or 10-year survival rate of each patient.

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Abstract

The invention belongs to the field of gene technology and medicine, and particularly relates to a marker for predicting prognosis of colon cancer and application of the marker. The marker for predicting prognosis of the colon cancer comprises autophagy-related lncRNA and/or autophagy-related mRNA markers, wherein the autophagy-related lncRNA is CASC9, PCAT6, AP006621.2, GS1-124K5.4, MIR4435-2HG, AL354993.2, AC048344.4, AC010973.2, AL590483.1, AL137782.1, STAG3L5P-PVRIG2P-PILRB, LINC00513, SNHG16 and AP001554.1; and the autophagy-related mRNA is GRID1, DAPK1, RAB7A, PELP1, ULK3 and WIPI2. The marker for predicting the prognosis of the colon cancer can be used for predicting the prognosis risk and survival rate of the colon cancer, provides a guidance basis for prognosis and health management of patients, and realizes individual accurate treatment.

Description

technical field [0001] The invention belongs to the field of gene technology and medicine, in particular to a marker for predicting the prognosis of colon cancer and its application. Background technique [0002] Colon cancer is the most common malignant epithelial tumor and one of the leading causes of death worldwide. Despite advances in diagnostic and therapeutic strategies, the 5-year survival rate for patients with colon cancer remains below 50%. Therefore, it is still imperative to study the basic molecular mechanism of colon cancer pathogenesis, determine molecular biomarkers, and assess the survival of colon cancer patients. [0003] Autophagy is the main catabolic process in the body to maintain metabolic balance and cells under metabolic stress (such as energy deficiency and starvation) and physiological and pathological conditions (such as aging, apoptosis and cancer). Autophagy is complex and differs in various types of cancer, acting as a tumor suppressor or a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886C12N15/11G16B5/00
CPCC12Q1/6886G16B5/00C12Q2600/158C12Q2600/178C12Q2600/118
Inventor 徐茜郭玉刚李丹丹王梦真姚伦广阚云超
Owner NANYANG NORMAL UNIV
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