CN111187839A - Application of m5C methylation related regulatory gene in liver cancer prognosis prediction - Google Patents
Application of m5C methylation related regulatory gene in liver cancer prognosis prediction Download PDFInfo
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Abstract
The invention belongs to the technical field of biological medicines, and particularly discloses application of m5C methylation related regulatory genes in liver cancer prognosis prediction. The m5C methylation related regulatory genes comprise NSUN4 gene and ALYREF gene, and the reagent for detecting the expression levels of the NSUN4 gene and the ALYREF gene can be used for preparing liver cancer prognosis prediction products. By detecting the expression levels of the NSUN4 gene and the ALYREF gene, the survival period of a liver cancer patient can be effectively distinguished, so that a new way is provided for the judgment of liver cancer prognosis prediction, and a reference basis is provided for a clinician to analyze the condition of liver cancer.
Description
Technical Field
The invention belongs to the field of biological medicine, and particularly relates to application of m5C methylation related regulatory genes in liver cancer prognosis prediction.
Background
Hepatocellular carcinoma (HCC) is the most common disease in adult liver malignancies, the third most common cause of cancer-related death worldwide (8.2% of all cases) and is a serious public health problem. Despite the great development of the treatment strategy, the five-year survival rate of the advanced hepatocellular carcinoma is still low due to late diagnosis, easy metastasis and frequent postoperative recurrence of the cancer. Therefore, studying the molecular mechanisms of the occurrence and progression of hepatocellular carcinoma is crucial to the identification of novel biomarkers and effective therapeutic targets for early detection.
There is increasing evidence that post-transcriptional modification of RNA plays a crucial role in different cancers. Genetic and epigenetic changes in RNA and histones have been extensively studied in tumor progression, laying the foundation for the development of new therapeutic modalities. Although many RNA modifications have been reported, due to the limitations of accurate localization techniques throughout the genome, the understanding of the regulation and function of RNA modifications remains limited. Common sites of RNA methylation include 5-methylcytosine (m5C), m6A, and the like.
Reversible m5C methylation modification is one of the most common post-transcriptional modifications of RNA, playing a crucial role in regulating alternative splicing, export, stable localization and translation of mRNA. An increasing number of studies have shown that the broad effect of m5C methylation on tRNA, rRNA and mRNA began to be revealed. m5C methylation is involved in a range of regulatory proteins including m5C methyltransferase, demethylase and reader. Methyltransferases (methyltransferases transfer a methyl group to cytosine C to form 5-methylcytosine with S-adenosylmethionine as the methyl donor) increase methylation at RNA C5, and then reader proteins recognize and bind methylated mRNA, while demethylases reverse m5C modifications by degrading methylation. That is, m5C methylation modification involves a reader of recognition and binding of adenosine methyltransferases as writers, demethylases as erasers, and proteins. The m5C writer proteins include NSUN1, NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT1, DNMT2, DNMT3A, and DNMT3B, the m5C eraser includes TET2, and the m5C reader includes ALYREF. There is increasing evidence that m5C modifications play a key regulatory role in a variety of important biological processes, including mRNA structure, stability and translation. However, the relationship between the m5C methylation-associated regulator gene and HCC is poorly understood, and the value of the m5C methylation-associated regulator gene in the diagnosis and prognosis prediction of HCC is still unknown.
Disclosure of Invention
The invention aims to provide application of m5C methylation related regulatory genes in liver cancer prognosis prediction. Another objective of the invention is to provide a kit for predicting liver cancer prognosis.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
the invention analyzes RNA expression levels corresponding to 13 relevant regulatory genes (NSUN1, NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT1, DNMT2, DNMT3A, DNMT3B, TET2 and ALYRF) involved in m5C methylation of The hepatocellular carcinoma by utilizing a Cancer gene map (TCGA) data set, extracts clinical information (clinical TNM stage and survival time of a patient) of The relevant hepatocellular carcinoma patient, analyzes The correlation among The clinical TNM stage, The survival time and The expression of The 13 relevant regulatory genes of The hepatocellular carcinoma patient, and finally discovers that The high expression of NSREF UN4 and The ALYRF is related to key biological processes of The hepatocellular carcinoma. Furthermore, the results of Lasso regression analysis show that high expression of NSUN4 and ALYREF is related to poor prognosis of patients with hepatocellular carcinoma.
The invention firstly provides application of a reagent for detecting the expression level of m5C methylation related regulatory genes in preparation of liver cancer prognosis products.
The m5C methylation-associated regulatory gene includes the NSUN4 gene.
The m5C methylation-associated regulatory gene is the ALYREF gene.
The m5C methylation-associated regulatory genes are the NSUN4 gene and the ALYREF gene.
Further, the product can detect the expression levels of NSUN4 gene and ALYREF gene through RT-PCR, real-time quantitative PCR, in-situ hybridization or a high-throughput sequencing platform.
Further, a product for detecting the expression levels of the NSUN4 gene and the ALYREF gene by RT-PCR comprises a pair of primers for specifically amplifying the NSUN4 gene and a pair of primers for specifically amplifying the ALYREF gene.
Further, the product for detecting the expression levels of the NSUN4 gene and the ALYREF gene by real-time quantitative PCR comprises a pair of primers for specifically amplifying the NSUN4 gene and a pair of primers for specifically amplifying the ALYREF gene.
Further, a product for detecting the expression level of NSUN4 gene and ALYREF gene by in situ hybridization comprises: a probe that hybridizes to the nucleotide sequence of the NSUN4 gene, and a probe that hybridizes to the nucleotide sequence of the ALYREF gene.
Further, the nucleotide sequence of the primer for specifically amplifying the NSUN4 gene is shown as SEQ ID NO.1 and SEQ ID NO. 2; the nucleotide sequence of the primer for specifically amplifying the ALYREF gene is shown as SEQ ID NO.3 and SEQ ID NO. 4.
Further, the product comprises a chip, a preparation or a kit.
The invention also provides a liver cancer prognosis prediction kit, which comprises a reagent for detecting the NSUN4 gene expression level.
Further, the reagent for detecting the expression level of the NSUN4 gene is a pair of primers for specifically amplifying the NSUN4 gene.
Further, the nucleotide sequence of the primer for specifically amplifying the NSUN4 gene is shown as SEQ ID NO.1 and SEQ ID NO. 2.
Further, the kit also comprises a reagent for detecting the expression level of the ALYREF gene.
Further, the reagent for detecting the expression level of the ALYREF gene is a pair of primers for specifically amplifying the ALYREF gene.
Further, the nucleotide sequence of the primer for specifically amplifying the ALYREF gene is shown as SEQ ID NO.3 and SEQ ID NO. 4.
Compared with the prior art, the invention has the following positive beneficial effects:
(1) the invention analyzes the RNA expression quantity corresponding to 13 relevant regulatory genes (NSUN1, NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT1, DNMT2, DNMT3A, DNMT3B, TET2 and ALYRF) which participate in the methylation of the hepatocellular carcinoma m5C by using a cancer gene map TCGA dataset, simultaneously extracts the clinical information (clinical TNM stage and the survival time of the patient) of the relevant hepatocellular carcinoma patient, and carries out the analysis of the correlation between the clinical TNM stage and the survival time of the patient and the expression of the 13 relevant regulatory genes of the hepatocellular carcinoma patient (namely, the relation between the expression of the m5C methylation relevant regulatory gene and the clinical TNM stage and the clinical prognosis of the patient of the hepatocellular carcinoma patient is researched from the RNA expression level), and discovers the high expression of the NSUN4 gene and the ALREF gene for the first time and the clinical TNM stage and the prognosis of the hepatocellular carcinoma patient, and discovers the relevant biological processes of the liver cancer 4 and the relevant genes of the hepatocellular carcinoma for the patient, and a double-gene prognosis prediction model of the NSUN4 gene and the ALYREF gene is established, and the prognosis of the HCC patient can be effectively predicted by detecting the expression levels of the NSUN4 gene and the ALYREF gene.
(2) According to the invention, the prognosis of the patient with the hepatocellular carcinoma can be effectively distinguished by detecting the expression levels of the NSUN4 gene and the ALYREF gene, a new way is provided for the judgment of prognosis prediction of the hepatocellular carcinoma, and a reference basis is provided for a clinician to analyze the condition of the hepatocellular carcinoma.
Drawings
FIG. 1 is a graph showing the results of analyzing the expression levels of m5C methylation-associated regulatory genes by different mutations;
FIG. 2 is a graph showing the results of comparing the expression levels of m5C methylation-associated regulatory genes in HCC patients staged for different clinical TNM;
FIG. 3 is a graph showing the results of a correlation analysis between m5C methylation-associated regulatory genes and prognostic prediction in HCC patients; wherein A is the survival curve of different clinical TNM staged case prognoses of HCC patients; b is the expression of m5C methylation-related regulatory genes at different TNM stages; c is the prognosis of the Kaplan-Meier curve of SNV and HCC patients; d is the prognosis of the CNV Kaplan-Meier curve and HCC patients;
FIG. 4 is a graph showing the results of a relational analysis between the expression of the ALYREF gene and NSUN4 gene and the prognosis of HCC patients; wherein A is ALYREF gene differential expression and a predicted Kaplan-Meier curve in an HCC patient; b is the differential expression and the prognosis Kaplan-Meier curve of the NSUN4 gene in HCC patients; c is a Kaplan-Meier curve of ALYREF and NSUN4 double gene expression and prognosis; d is a ROC curve based on prognostic signatures expressed by ALYREF and NSUN4, with 1, 3 and 5 year survival;
FIG. 5 is a graph showing the results of enrichment of GSEA by NSUN4 gene and ALYREF gene; wherein A and B are GSEA enrichment for NSUN4 expression levels; c and D are GSEA enrichment for ALYREF expression levels.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the scope of the present invention is not limited thereto.
1. Materials and methods
Acquisition and processing of hepatocellular carcinoma datasets
All HCC Copy Number Variation (CNV), mrnas and all corresponding clinical data used in the present invention were downloaded from the TCGA data portal (http:// gdc-portal. nc. nih. gov /). We obtained a transcriptome of 423 HCC samples, of which 377 had available clinical information data. A total of 319 HCC specimens were available for further analysis after excluding HCC specimens with survival times less than 90 days from 377 specimens.
2. Statistical analysis
Statistical analysis was performed using SPSS 23.0 software (IBM Corp, usa) and R language. The survival time of patients was analyzed using Kaplan-Meier and log rank test. The chi-square test was used to assess the association of CNV with m5C methylation-associated regulatory genes and clinical TNM staging. P values less than 0.05 are considered statistically significant.
Example 1: effect of m5C methylation-related regulatory Gene CNV on expression of m5C methylation-related regulatory Gene
The effect of CNV on the expression of m5C methylation-associated regulatory genes was first explored. By analyzing transcriptome data of 423 HCC samples, 11 m5C methylation-associated regulatory genes were detected, these 11 associated regulatory genes being NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT1, DNMT3A, DNMT3B, TET2, and ALYREF, respectively. To explore the 11 m5C methylation-associated regulatory gene expression relationships with CNVs, we performed t-tests between different types of CNVs and gene expression, the results of which are shown in fig. 1. As can be seen from fig. 1, CNV affected mRNA expression of 9 genes (9 genes, NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT3A, TET2, and ALYREF, respectively) (as shown in a to I in fig. 1), but CNV had no significant difference in the mRNA expression of the other 2 genes (DNMT1 and NSUN3B) (as shown in J, K in fig. 1). Thus, it was suggested that CNVs of 9 of the m5C methylation-associated regulatory genes (9 genes NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT3A, TET2, and ALYREF, respectively) may affect the expression of regulatory molecules and thus the HCC progression. Furthermore, the expression of both the eraser gene (TET2) and the reader gene (ALYREF) were significantly correlated with CNV, suggesting that these genes play an important role in m5C regulation.
Example 2: relational analysis between expression of m5C methylation related regulatory gene in hepatocellular carcinoma and clinical TNM stage of HCC
In order to further study the expression of m5C methylation-related regulatory genes in different stages of clinical TNM of HCC, the invention performs relational analysis between the expression of m5C methylation-related regulatory genes and the stages of clinical TNM of HCC on HCC samples obtained from a TCGA database, and simultaneously, for the convenience of statistical analysis, the stages of clinical TNM of HCC are classified into a low stage (Lowstage) and stages of HCC into a III stage and a IV stage into a High stage (High stage), and the analysis results are shown in FIG. 2. As can be seen from fig. 2, the expression of the DNMT3A, NSUN4, NSUN5, DNMT1, TET2 and ALYREF genes was significantly higher in the high-grade liver cancer tissue than in the low-grade liver cancer tissue (specifically, see a to F in fig. 2); however, no significant difference was found in the expression of the five writer genes (NSUN2, NSUN3, NSUN6, NSUN7 and NSUN3B) in liver cancer tissues of different TNM stages (see in particular G to K in fig. 2). It can be seen that most of the m 5C-related regulatory genes are up-regulated in high-stage liver cancer tissues, indicating that in HCC, there may be a correlation between the expression of m 5C-methylation-related regulatory genes and the clinical TNM stage.
Example 3: relation between m5C methylation related regulatory gene expression and prognosis of hepatocellular carcinoma
To further study the relationship between the expression of m5C methylation-related regulatory gene and the prognosis of hepatocellular carcinoma, 319 HCC samples obtained from the TCGA database were analyzed for the relationship between clinical TNM staging and HCC prognosis, and the relationship between the expression of m5C methylation-related regulatory gene and HCC prognosis, and the analysis results are shown in fig. 3. As can be seen from A and B in FIG. 3, there is a significant correlation between the clinical TNM stage of HCC and patient prognosis. Therefore, the 11 m5C methylation-associated regulatory genes (NSUN2, NSUN3, NSUN4, NSUN5, NSUN6, NSUN7, DNMT1, DNMT3A, DNMT3B, TET2, and ALYREF) detected in example 1 were further analyzed with respect to the survival time of HCC patients, and the results are shown in fig. 3 as C and D, using CNV and SNV (single nucleotide variation) as study subjects. As can be seen from C and D in FIG. 3, no significant difference was observed between the 11 CNV or SNV of the m5C methylation-associated regulatory gene and the survival time of HCC patients.
Considering that there is a positive correlation between the expression of some m5C methylation-associated regulatory genes and CNV, further univariate COX regression analysis was performed to explore the relationship between the expression levels of the 11 m5C methylation-associated regulatory genes detected in the examples and the prognosis of HCC patients, and the results are shown in Table 1. As can be seen from table 1, the expression levels of 8 genes (NSUN4, ALYREF, DNMT3A, DNMT1, DNMT3B, NSUN5, NSUN2, NSUN3) were significantly correlated with the prognosis of the patient (p < 0.05); moreover, the expression levels of 6 of the above 8 genes (NSUN4, ALYREF, DNMT3A, NSUN5, NSUN2, NSUN3) were significantly correlated with their CNV changes.
TABLE 1 COX assay for exploring the role of m5C methylation-associated regulatory gene expression in the prognosis of hepatoma patients
Note: HR-hazard ratio (risk rate).
Example 4: relationship between expression of NSUN4 gene and ALYREF gene and HCC prognosis
To further determine the relationship between the m5C methylation-associated regulatory gene and HCC prognosis, the inventors performed 1000 Lasso regression analyses on 11 m5C methylation-associated regulatory genes detected in example 1, and the analysis results are shown in Table 2. As can be seen from table 2, there are 2 genes in 1000 Lasso regression results with more than 900 replicates and their CNVs have a significant effect on expression levels, a significant single-factor COX analysis result, and a significant correlation between expression levels and clinical TNM stage (p <0.001), which are the NSUN4 gene and the ALYREF gene, respectively.
TABLE 2 results of Lasso regression analysis of m5C methylation-associated regulatory genes
Considering the correlation between the expression of the NSUN4 gene and the ALYREF gene and the prognosis of HCC patients, the correlation between the expression level of the NSUN4 gene and the expression level of the ALYREF gene and the total survival time of the HCC patients is analyzed by using a Kaplan-Meier curve and a log-rank test, and the correlation between the combination of the NSUN4 gene and the ALYREF gene and the total survival time of the HCC patients is also analyzed, and the result is shown as A, B, C in FIG. 4. As can be seen from A in FIG. 4, the survival time of HCC patients with high-expression ALYRF gene is obviously shorter than that of HCC patients with low-expression ALYRF gene, the difference has statistical difference (P < 0.01), the high-expression of ALYRF gene is closely related to the poor prognosis of HCC, which suggests that the expression level of ALYRF gene can be used as a prognosis marker of HCC, and the detection of the expression of ALYRF gene can be used for evaluating the prognosis of HCC patients and predicting the survival time of patients. As shown in B in FIG. 4, the survival time of the HCC patient with high NSUN4 gene expression is obviously shorter than that of the HCC patient with low NSUN4 gene expression, the difference has statistical difference (P < 0.01), the high NSUN4 gene expression is closely related to the poor prognosis of HCC, which indicates that the NSUN4 gene expression level can be used as a prognosis marker of HCC, and the detection of NSUN4 gene expression can be used for evaluating the prognosis of HCC patients and predicting the survival time of patients. As can be seen from C in FIG. 4, the detection model of the expression levels of ALYREF and NSUN4 can be used for prognosis evaluation of HCC patients, and the high expression of 2 genes is closely related to the poor prognosis of the patients.
The inventor further establishes a double-gene model by combining the ALYREF gene and the NSUN4 gene to carry out stability reliability analysis on the prognosis evaluation of HCC patients, and the analysis result is shown as D in FIG. 4. As can be seen from D in FIG. 4, AUC (1, 3 and 5 years) of NSUN4 gene and ALYREF gene are both greater than 0.6(p <0.001), thus demonstrating that the model established by analyzing the relationship between 2 gene expression levels and survival time is very stable, and the stability and reliability of the two-gene model can be well verified.
Example 5: survival and function enrichment analysis of NSUN4 and ALYREF in hepatocellular carcinoma
Gene Set Enrichment Analysis (GSEA) was used to predict the functional role of NSUN4 and ALYREF in liver cancer progression, the results of which are shown in FIG. 5. As can be seen from fig. 5, high expression of the NSUN4 gene is significantly associated with the methylation and demethylation processes; meanwhile, high expression of the ALYREF gene is associated with cell cycle regulation and mitosis. These findings indicate that the NSUN4 gene and the ALYREF gene play a crucial role in the key biological processes of HCC.
Example 6: kit for HCC prognosis prediction
A kit for HCC prognosis prediction comprises a primer for specifically amplifying the ALYREF gene, wherein the nucleotide sequence of the primer for specifically amplifying the ALYREF gene is shown as SEQ ID NO.3 and SEQ ID NO.4, and the kit also comprises common reagents required by PCR technology, such as Taq enzyme, dNTP mixed solution, PCR reaction buffer solution, deionized water and the like, and the common reagents are well known by a person skilled in the art. The kit adopts a real-time quantitative PCR method to detect the expression of the ALYREF gene, and the detection method specifically comprises the following steps: (1) extracting total RNA of the tissue sample; (2) reverse transcribing the extracted RNA into cDNA; (3) carrying out amplification detection on the ALYREF gene and a reference gene on a fluorescent real-time quantitative PCR instrument; (4) determining a target band through melting curve analysis and electrophoresis, carrying out relative quantification by a delta CT method and the like. The operations of the above steps are all conventional operations in the field.
Example 7: kit for HCC prognosis prediction
A kit for HCC prognosis prediction comprises a primer for specifically amplifying NSUN4 gene and a primer for specifically amplifying NSUN4 gene, wherein the nucleotide sequence of the primer for specifically amplifying NSUN4 gene is shown as SEQ ID NO.1 and SEQ ID NO.2, and the kit also comprises common reagents required by PCR technology, such as Taq enzyme, dNTP mixed solution, PCR reaction buffer solution, deionized water and the like, and the common reagents are well known by a person skilled in the art. The kit adopts a real-time quantitative PCR method to detect the expression of NSUN4 gene, and the detection method specifically comprises the following steps: (1) extracting total RNA of the tissue sample; (2) reverse transcribing the extracted RNA into cDNA; (3) performing amplification detection on the NSUN4 gene and a reference gene on a fluorescent real-time quantitative PCR instrument; (4) determining a target band through melting curve analysis and electrophoresis, carrying out relative quantification by a delta CT method and the like. The operations of the above steps are all conventional operations in the field.
Example 8: kit for HCC prognosis prediction
A kit for HCC prognosis prediction comprises a primer for specifically amplifying NSUN4 gene and a primer for specifically amplifying NSUN4 gene, wherein the nucleotide sequence of the primer for specifically amplifying NSUN4 gene is shown as SEQ ID NO.1 and SEQ ID NO.2, and the nucleotide sequence of the primer for specifically amplifying ALYREF gene is shown as SEQ ID NO.3 and SEQ ID NO. 4; the kit also contains common reagents required by PCR technology, such as Taq enzyme, dNTP mixed solution, PCR reaction buffer solution, deionized water and the like, and the common reagents are well known to those skilled in the art. The kit adopts a real-time quantitative PCR method to detect the expressions of NSUN4 gene and ALYREF gene, and the detection method specifically comprises the following steps: (1) extracting total RNA of the tissue sample; (2) reverse transcribing the extracted RNA into cDNA; (3) carrying out amplification detection on the NSUN4 gene, the ALYREF gene and a reference gene on a fluorescent real-time quantitative PCR instrument; (4) determining a target band through melting curve analysis and electrophoresis, carrying out relative quantification by a delta CT method and the like. The operations of the above steps are all conventional operations in the field.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention, but rather as the following description is intended to cover all modifications, equivalents and improvements falling within the spirit and scope of the present invention.
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Claims (10)
1. Application of a reagent for detecting expression level of m5C methylation related regulatory genes in preparation of liver cancer prognosis prediction products.
2. The use of claim 1, wherein the m5C methylation associated regulatory gene is the NSUN4 gene.
3. The use of claim 1, wherein the m5C methylation-associated regulatory gene is the ALYREF gene.
4. The use of claim 1, wherein the m5C methylation associated regulatory gene is the NSUN4 gene and the ALYREF gene.
5. The use of claim 4, wherein the product is used for detecting the expression level of NSUN4 gene and ALYREF gene by RT-PCR, real-time quantitative PCR, in situ hybridization or high-throughput sequencing platform.
6. The use of claim 1, said product comprising a chip, a formulation or a kit.
7. A kit for predicting liver cancer prognosis, which is characterized by comprising a reagent for detecting the expression level of NSUN4 gene.
8. The kit for predicting the prognosis of liver cancer according to claim 7, wherein the reagent for detecting the expression level of NSUN4 gene is a pair of primers for specifically amplifying NSUN4 gene, and the nucleotide sequences of the primers are shown as SEQ ID No.1 and SEQ ID No. 2.
9. The kit for predicting liver cancer prognosis as claimed in claim 7, wherein the kit further comprises a reagent for detecting the expression level of the ALYREF gene.
10. The kit for predicting the prognosis of liver cancer according to claim 9, wherein the reagent for detecting the expression level of the ALYREF gene is a pair of primers for specifically amplifying the ALYREF gene, and the nucleotide sequences of the primers are shown as SEQ ID No.3 and SEQ ID No. 4.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111748624A (en) * | 2020-06-08 | 2020-10-09 | 郑州大学第一附属医院 | Biomarker for predicting whether liver cancer is recurrent |
| CN111979322A (en) * | 2020-08-26 | 2020-11-24 | 郑州大学第一附属医院 | Gene markers for hepatocellular carcinoma |
| CN112094909A (en) * | 2020-08-26 | 2020-12-18 | 郑州大学第一附属医院 | Gene marker for pancreatic cancer prognosis diagnosis |
| CN112270992A (en) * | 2020-12-25 | 2021-01-26 | 江苏高美基因科技有限公司 | Construction method of intrahepatic cholangiocellular carcinoma patient prognosis evaluation model |
| CN112725453A (en) * | 2021-02-03 | 2021-04-30 | 复旦大学附属肿瘤医院 | Application of m5c modified regulatory genome in preparation of tumor prognosis evaluation reagent or kit |
| CN113151451A (en) * | 2021-05-28 | 2021-07-23 | 苏州市立医院 | Biomarker for diagnosing maturation of ovum development and application thereof |
| CN114540502A (en) * | 2022-04-12 | 2022-05-27 | 东南大学 | Detection method and kit for gastric cancer chemotherapy drug sensitivity and application of NSUN2 detection |
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-
2020
- 2020-01-17 CN CN202010054569.1A patent/CN111187839B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 20190918: "Role of m5C-Related Regulators in Diagnosis and Prediction of Prognosis in Hepatocellular Carcinoma", SSRN * |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111748624A (en) * | 2020-06-08 | 2020-10-09 | 郑州大学第一附属医院 | Biomarker for predicting whether liver cancer is recurrent |
| CN111979322A (en) * | 2020-08-26 | 2020-11-24 | 郑州大学第一附属医院 | Gene markers for hepatocellular carcinoma |
| CN112094909A (en) * | 2020-08-26 | 2020-12-18 | 郑州大学第一附属医院 | Gene marker for pancreatic cancer prognosis diagnosis |
| CN112270992A (en) * | 2020-12-25 | 2021-01-26 | 江苏高美基因科技有限公司 | Construction method of intrahepatic cholangiocellular carcinoma patient prognosis evaluation model |
| CN112725453A (en) * | 2021-02-03 | 2021-04-30 | 复旦大学附属肿瘤医院 | Application of m5c modified regulatory genome in preparation of tumor prognosis evaluation reagent or kit |
| CN113151451A (en) * | 2021-05-28 | 2021-07-23 | 苏州市立医院 | Biomarker for diagnosing maturation of ovum development and application thereof |
| CN114540502A (en) * | 2022-04-12 | 2022-05-27 | 东南大学 | Detection method and kit for gastric cancer chemotherapy drug sensitivity and application of NSUN2 detection |
| CN115807095A (en) * | 2022-12-07 | 2023-03-17 | 中国人民解放军总医院第八医学中心 | Primer composition for detecting methylation sites of lung adenocarcinoma and application thereof |
| CN115807095B (en) * | 2022-12-07 | 2023-10-13 | 中国人民解放军总医院第八医学中心 | Primer composition for detecting methylation sites of lung adenocarcinoma and application of primer composition |
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