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Method of prognosis and stratification of ovarian cancer

A technology for ovarian cancer and prognosis, applied in the fields of biochemical equipment and methods, pharmaceutical formulations, genetic material components, etc., can solve problems such as ambiguity, complex carcinogenic properties of miRNAs and complex tumor-suppressive properties

Inactive Publication Date: 2015-08-19
AGENCY FOR SCI TECH & RES
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The oncogenic and tumor suppressive properties of specific miRNAs are complex and often ill-defined

Method used

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  • Method of prognosis and stratification of ovarian cancer
  • Method of prognosis and stratification of ovarian cancer
  • Method of prognosis and stratification of ovarian cancer

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0157] Expression patterns of the let-7 family in HG-EOC enable classification of patients into three distinct risk subgroups

[0158] Reporting recommendations for prognostic studies of tumor markers (REMARK; McShane et al., 2005) were used to identify potential biomarkers. We analyzed two independent miRNA expression datasets (TCGA and GSE27290, as discussed above) collected from HG-SOC patients (Tables 2 and 3).

[0159] Table 2 Clinical characteristics (OS: overall survival) of The Cancer Genome Atlas (TCGA) and GSE27290 datasets

[0160]

[0161] Table 3: Number and distribution of cases and relative survival from TCGA data (486 primary tumor samples)

[0162]

[0163]

[0164]

[0165] +The median survival time is calculated based on the information of the deceased patients only

[0166] * Surviving patients with follow-up <5 years or patients with no follow-up information

[0167] After removing outlier samples, 514 profiles of the TCGA dataset and 49 pro...

Embodiment 2

[0176] let-7b as a master regulator of HG-SOC in a dichotomy of pathobiological functions

[0177] Correlation analysis of miRNA expression between let-7 members of the two datasets ( Figure 7 ) indicates that the expression of miR-202 is negatively correlated with other members; this indicates that miR-202 is an outlier in this family. While let-7b and let-7c were significantly positively correlated with each other, their expression levels were less correlated with other let-7 members, which were significantly positively correlated. Sequence analysis and co-expression patterns of let-7b and let-7c indicated that they were grouped in a distinct cluster and suggested that they function similarly in HG-SOC.

[0178] Hierarchical cluster analysis was performed on the correlation coefficients of 141 miRNAs of let-7 present in the TCGA and GSE27290 datasets (Fig. 8). Let-7b and let-7c show a different pattern than other members. Of the 141 miRNAs, 103 miRNAs (73%) were in the s...

Embodiment 3

[0207] Validation and SPS of Prognostic Biomarker Selection

[0208] To validate our biomarker selection process and the computational algorithm used, we randomly generated 999 probe-set lists, each containing 162 probe-sets from the negative control probe-set list, and prepared as previously described Similar DDg analysis and SWVg analysis were performed. Within the same TCGA dataset, our SPS significantly outperformed those probe sets of the negative control (FDR=3E-3, Figure 12 ).

[0209] Next, we validated our SPS and predictive models on three independent datasets—GSE9899, ​​GSE26712, and GSE13876—containing 246 OC samples (90% stage III / IV), There were 185 late-stage HG-OC samples and 157 advanced-stage SOC samples (Fig. 13). Using the predictive model constructed from the TCGA dataset and the 36 SPS genes, each population can be divided into three significantly distinct Risk subgroup ( Figures 13A-13C ). The low-risk subgroup had a 3-year survival rate of 68%-8...

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Abstract

A method for the prognosis of overall survival or prediction of therapeutic outcome for a patient suffering from epithelial ovarian cancer (EOC), comprising: a. providing a sample from the patient, b. determining the expression level of microRNA family lethal-7b (let-7b) in the sample; c. using the expression level of the let-7b to obtain the prognosis of overall survival or prediction of therapeutic outcome for the patient.

Description

technical field [0001] The present disclosure relates to a method and system for ovarian cancer prognosis, and also relates to a system and method for identifying candidate genes for use in prognostic methods and prognostic kits. Background technique [0002] Ovarian cancer is a highly heterogeneous disease lacking reliable diagnostic, prognostic, and predictive clinical biomarkers. Neither traditional clinical biomarkers (stage, grade, mass, etc.) nor molecular biomarkers (CA125, KRAS, p53, etc.) are suitable for early diagnosis, specific diagnosis, prognosis, and prediction of disease outcome in individual patients. The most common type of human ovarian cancer is human epithelial ovarian cancer (EOC). This ovarian cancer is characterized by one of the lowest survival rates of all cancers. [0003] Over the past 30 years, despite considerable efforts to address epithelial ovarian cancer (EOC) disease, mortality from ovarian cancer has remained high (Siegel et al., 2012). ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/68G01N33/48A61K39/395A61K48/00
CPCC12Q2600/178C12N2320/10C12Q1/6886C12N15/111C12Q2600/158C12N2310/141C12Q2600/112C12Q2600/118
Inventor 弗拉基米尔·安德烈耶维奇·库兹涅佐夫唐志群欧锦祥安娜·弗拉基米罗芙娜·艾弗史娜
Owner AGENCY FOR SCI TECH & RES
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