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Biomarker and method for predicating relapse and mortality risk of renal cell carcinoma

A biomarker, renal cell carcinoma technology, applied in the direction of biochemical equipment and methods, microbial determination/inspection, etc., can solve the problems that have not been reported, and achieve the effect of good management, efficient recurrence and death risk

Active Publication Date: 2018-12-21
SHENZHEN YIKANG BIOTECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Studies have also found that IQGAP1 can promote tumorigenesis, it is overexpressed in many tumors, and there is a close correlation between the overexpression level and the invasion and metastasis of tumor cells, but its role in ccRCC has not been reported yet Therefore, IQGAP1 and other related molecular biological networks have broad application prospects in effectively predicting the risk of recurrence and death in patients with renal clear cell carcinoma

Method used

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  • Biomarker and method for predicating relapse and mortality risk of renal cell carcinoma
  • Biomarker and method for predicating relapse and mortality risk of renal cell carcinoma
  • Biomarker and method for predicating relapse and mortality risk of renal cell carcinoma

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] 1 Implementation samples and methods

[0055] 1.1 Case samples

[0056] Select 533 patients with carcinoma in situ from the TCGA Provisional ccRCC database in the cBioPortal database. All tumors were surgically resected and the RNA expression was obtained by RNA sequencing, as well as 140-month follow-up information and some clinical data. 291 tumors / patients in the TCGA Provisional pRCC database were selected for RNA sequencing, as well as follow-up information and some clinical data. The demographic data of ccRCC patients in TCGA are shown in Table 1, where Q is quartile, NA is not available data (not available), and Tart Mol therapy is targeted molecular therapy:

[0057] Table 1

[0058]

[0059]

[0060] 1.2 Gene enrichment analysis

[0061] The enriched gene sets and pathways were analyzed using the GAGE(49) and Reactome(50) language packages in the R language. The database uses KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (gene ontology) databa...

Embodiment 2

[0090] Used implementation sample, method are identical with embodiment 1;

[0091] Using Sig0.83sub1 and Sig0.83sub2 to predict cRCC mortality risk:

[0092] The HR of SPACA6, TROAP, CENPW and NCOA4 in Sig0.83 is higher, and it is separated as the first subgenome Sig0.83sub1 of Sig0.83, and the DEGs with P value>2.79e-9 are excluded from the remaining genes Elimination, leaving 21 genes to form Sig0.83sub2;

[0093] The sensitivity / specificity / PPV / MMS / P values ​​of Sig0.83sub1, Sig0.83sub2 and Sig0.83cut in predicting the risk of death in ccRCC were 41.1% / 93.6% / 75.8% / 27.2 / 0 (eg Figure 6 D), 62.3% / 83.8% / 65.3% / 36.5 / 0 (such as Figure 6 E) and 50.9% / 90.2% / 71.8% / 31.1 / 0 (as Figure 6 C shown). The tAUCs of Sig0.83sub1 and Sig0.83sub2 for identifying ccRCC mortality risk were greater than 70% and 76% in the four time frames, respectively (eg Figure 6 shown in A).

Embodiment 3

[0095] Used implementation sample, method are identical with embodiment 1;

[0096] Using Sigcmbn to predict ccRCC mortality risk:

[0097] Sigcmbn predicts tAUC of ccRCC high mortality risk as 78.1% / 13.8M, 78.7% / 31M, 77.8% / 48.9M and 81.3% / 70.6M, higher than Sig2.5 or Sig0.83 (eg Figure 11 A and Figure 6 Shown in A); the cut point score of Sigcmbn (such as Figure 11 B), Q1, median, mean and Q3 (as shown in Figure 12 shown) are all related to the decline in overall survival rate. If Q1, median, Q3 and cut point scores are used to predict the risk of death together, the sensitivity / specificity / PPV / MMS / P value can reach the best 90.3% / 89.1 % / 71.2% / 31.1 / 0 (eg Figure 11 C shown);

[0098] Among the 37 constituent genes of Sigcmbn, the cut point scores of 25 genes are all associated with the decrease of overall survival rate (p≤0.092), and the tAUC of the cumulative score predicting the risk of death is 76% / 23.2M, 82.1% / 36.3M , 86.5% / 45.5M and 80.7% / 58.9M (eg Figure 13 As...

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Abstract

The invention discloses a biomarker and a method for predicating relapse and mortality risk of renal cell carcinoma, and belongs to the renal cell monitoring technology. The biomarker is used for predicating the relapse or mortality risk of a patient with renal cell carcinoma after tumor is resected. The biomarker is one of the following characteristic genomes: Sig2.5, Sig0.83, Sig0.83sub1, Sig0.83sub2 and Sigcmbn; the relapse or mortality risk of the patient with renal cell carcinoma can be predicated by detecting the expression condition of the characteristic genomes in the biomarker. The biomarker has the advantages of being capable of effectively estimating the relapse or mortality risk of the patient with renal cell carcinoma after tumor is resected, so that the patient management canbe greatly performed; and novel assistant treatment or targeted treatment can be specifically performed on some patients with high relapse or mortality risk.

Description

technical field [0001] The invention belongs to renal cell carcinoma monitoring technology, and specifically relates to a biomarker that can be used to predict the risk of recurrence and death of renal cell carcinoma, and a method for predicting the risk of recurrence and death of renal cell carcinoma. Background technique [0002] Kidney cancer is the ninth most common cancer in men and the fourteenth most common cancer in women, respectively. Renal cell carcinoma (RCC) accounts for 85% of renal cancers, and the most common subtypes are clear cell carcinoma ccRCC (80%), papillary cell carcinoma pRCC (15%) and chromophobe RCC, (5 %). Among them, ccRCC is the most malignant and causes the most deaths. The main treatment for orthotopic ccRCC is total or partial nephrectomy, and 30-40% of these patients will relapse and metastasize after surgery. At present, ccRCC metastatic cancer is still incurable. Faced with this situation, one of the improvement methods is to classify or...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886
CPCC12Q1/6886C12Q2600/118C12Q2600/158
Inventor 唐大木何立智陈争陈婧林小曾
Owner SHENZHEN YIKANG BIOTECH CO LTD
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