CN113528664B - Biomarker and application thereof in prognosis prediction of intrahepatic cholangiocellular carcinoma - Google Patents

Biomarker and application thereof in prognosis prediction of intrahepatic cholangiocellular carcinoma Download PDF

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CN113528664B
CN113528664B CN202110798461.8A CN202110798461A CN113528664B CN 113528664 B CN113528664 B CN 113528664B CN 202110798461 A CN202110798461 A CN 202110798461A CN 113528664 B CN113528664 B CN 113528664B
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曾勇
高强
孙德强
袁克非
陈璐
陈星�
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West China Hospital of Sichuan University
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Abstract

A biomarker selected from the group consisting of a gene region of at least one gene of LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP2/STEAP2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6, TMEM106A, which gene region is an enhancer, promoter, exon, intron, 5 'untranslated region or 3' untranslated region of a gene, and use thereof in prognosis prediction of intrahepatic cholangiocellular carcinoma. The invention adopts whole genome DNA methylation sequencing, describes the whole epigenetic change of the intrahepatic cholangiocarcinoma in the whole genome range, and systematically evaluates the value of methylation levels of 17 gene regions in the prognosis of the intrahepatic cholangiocarcinoma.

Description

Biomarker and application thereof in prognosis prediction of intrahepatic cholangiocellular carcinoma
Technical Field
The invention relates to the field of biological medicine, in particular to a biomarker, and a model, a product and a system for predicting prognosis of intrahepatic cholangiocellular carcinoma by using the biomarker.
Background
Intrahepatic cholangiocarcinoma (Intrahepatic cholangiocarcinoma, ICC) is the second most common malignancy produced by the liver itself, accounting for 4.8-12.0% of primary liver cancer, and has a poor prognosis. The incidence and mortality of intrahepatic cholangiocarcinoma has continued to rise over the past few decades. Complete surgical resection is the only option for long-term survival of patients with resectable intrahepatic cholangiocarcinoma, and the total survival rate (OS) of some patients 5 years after surgery is approximately 25.0-39.8%.
The highly aggressive biological behavior of intrahepatic cholangiocarcinoma, and the lack of specific symptoms and signs, makes most patients manifest relatively advanced disease at the time of initial diagnosis, and thus only a few have the opportunity to undergo surgical resection. However, a high rate of recurrence after cure will result in poor prognosis, with 57.9-73.4% of patients relapsing, and 41.3-42.5% of relapsing patients dying from relapse, even with radical excision. Thus, it is important to identify patients with a high risk of mortality or recurrence after radical resections and to purposely explore appropriate adjuvant therapy strategies.
Abnormal DNA methylation in 5' methylcytosine (5-mC) is associated with the development and progression of intrahepatic cholangiocellular carcinoma. However, current studies on methylation and intrahepatic cholangiocarcinoma have focused mainly on the methylation level of single or multiple common tumor suppressor genes, such as CDH1, SOCS3, p15, hMLH1, APC, ARIDEAOPCML, etc., and little is known about the effect of methylation levels of the remaining genes, as well as specific gene regions on the genes, on predicting the long-term prognosis of intrahepatic cholangiocarcinoma.
Disclosure of Invention
It is an object of the present invention to provide a biomarker involving 1 to 17 gene regions corresponding to 1 to 18 genes, wherein the gene region may be one of an enhancer, a promoter, an exon, an intron, a 5 'untranslated region and a 3' untranslated region. By detecting the methylation level of the above-mentioned biomarker, a genomic methylation score (genomic methylation score, GMS) can be calculated based on the methylation level of each gene region, and a prognosis of intrahepatic bile duct cancer will be made by the genomic methylation score.
The above purpose is achieved by the following technical scheme:
a biomarker selected from a gene region of at least one gene of LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP2/STEAP2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6, TMEM106A, which gene region is an enhancer, promoter, exon, intron, 5 'untranslated region or 3' untranslated region of a gene.
Traditional researches on intrahepatic cholangiocellular carcinoma DNA methylation mainly focus on the results of a plurality of genes or 450k chips, and the effect and influence of methylation levels of specific gene regions on genes on intrahepatic cholangiocellular carcinoma cannot be completely reflected due to small sample size.
In the present application, whole genome DNA methylation sequencing (Whole Genome Bisulfite Sequencing, WGBS) was used for patients selected from university of henhouse Hua Xi hospital, university of double denier, and university of Tianjin medical science tumor hospital, epigenetic changes of intrahepatic cholangiocarcinoma as a whole were described in the whole genome, and prognostic value of methylation levels of the respective gene regions was systematically evaluated, and genome methylation scores were constructed to predict prognosis of patients based on the difference in prognostic value of methylation levels.
Specifically, in the present technical scheme, the methylation level of the gene region was detected by WGBS, and 1606362 gene regions corresponding to 61076 protein-encoding genes and non-encoding genes were determined. Next, the methylation level of 0 and the completely overlapping gene regions were removed, and the univariate COX analysis, C index calculation and CV calculation, determined 350 satisfactory gene regions. Finally, using the LASSO Cox algorithm, 17 gene regions corresponding to 18 genes were selected to construct a genome methylation score. The 17 gene regions correspond to the enhancers, promoters, exons, introns, 5 'untranslated regions or 3' untranslated regions in the genes LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP2/STEAP2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6, TMEM106A, respectively.
In some embodiments, the genome methylation score is calculated based on all 17 gene regions described above and the corresponding weight coefficients. In one or more embodiments, the genomic methylation score can also be calculated based on regions of the gene where the partial weighting coefficient is higher, e.g., based on the methylation score of exon 1 of gene LIX1L, and further, e.g., the methylation score of intron 1 of gene ORC 6.
The 18 genes corresponding to the selected gene regions comprise 12 protein coding genes, 3 lncRNA genes, 2 pseudogenes and 1 gene to be tested and confirmed (to be experimentally confirmed, TEC).
Among them, CDKN2B is a common cancer suppressor gene (tumour suppressor gene, TSG) as a gene encoding p15 protein, and methylation of the promoter of CDKN2B and its prognostic value have been verified in various other cancers. LETMD1 is a human cervical cancer oncogene that can promote proliferation and survival of cells by stabilizing p53, and is involved in regulation of tumor microenvironment by negatively regulating macrophage function. The gene RASSF3 is also a TSG that can induce apoptosis and G1-S arrest by stabilizing p53, and down-regulation of the gene RASSF3 can promote the malignant phenotype of lung cancer. The gene ORC6 plays an important role in the targeting, localization and assembly process of functional human ORC, and thus is involved in DNA replication and cell cycle. LIX1L is a novel mesenchymal gene and is related to EMT indexes of various cancers, in addition, LIX1L is highly expressed in various tumor samples, and in vitro experiments, the LIX1L knockdown can prevent proliferation, invasion and migration capacity of cancer cells. Upregulation of gene STEAP2 can inhibit malignant phenotype breast cancer cells, and the prognostic value of STEAP2 or STEAP2-AS1 has been demonstrated in a variety of cancers. In addition to inhibiting the EMT and PI3K/Akt/NF- κB pathways during cancer, the gene TMEM106A also maintains macrophage homeostasis through MAPK and NF- κB signaling pathways to regulate tumor microenvironment, similar results also exist in genes GAD2 and TUBA 4A.
In addition to the above genes, long non-coding RNAs (lncRNA) and pseudogenes, which are traditionally considered to be non-functional background noise or garbage genes, are also of increasing interest. In the technical scheme, 3 lncRNAs and 2 pseudogenes are adopted in the genome methylation score construction, and no report related to the occurrence or progress of tumors is found in all 5 genes except STEAP2-AS 1. In addition, the remaining coding genes and TEC genes have not been shown to be associated with tumor development or progression.
Thus, it can be seen that, of the gene regions obtained by the screening of the present application, a part of genes corresponding to the gene regions have been confirmed to be related to tumor biology and have a prognostic biomarker capable of being used as a cancer, and although it does not disclose the gene regions of the present application that are closely related to the prognosis of intrahepatic cholangiocarcinoma, the reliability of the genomic methylation score in the present application is demonstrated to some extent; and the other part of gene region and the corresponding gene have important biological functions in intrahepatic cholangiocellular carcinoma, even other types of cancers.
Further, the gene regions of AC097173.1, STEAP2/STEAP2-AS1, RASSF3, AC073592.4, GOLGA6B are enhancers, the gene regions of MFSD1, FAM153B, GAD, ORC6 are introns, the gene region of LIX1L, TUBA a is an exon, the gene region of LINC01822 is a promoter, the gene region of TMEM106A is a 5' untranslated region, the gene region of AC023481.1, CDKN2B, FAM205BP is a 5' untranslated region or exon, and the gene region of LETMD1 is a 3' untranslated region or exon. In the prior art, although part of the coding gene has been confirmed as an oncogene, such as CDKN2B, studies on methylation and prognostic value of its gene region have been focused mainly on the promoter of the gene. In this embodiment, the CDKN2B gene region is preferably a 5' untranslated region or an exon. Meanwhile, the inventors found in experiments that, among 350 gene regions satisfying requirements, the enhancer has a significantly higher ratio than other gene regions of several types, and the 5 'untranslated region and the 3' untranslated region have a higher ratio than the promoter. Finally, of the 17 gene regions selected by the LASSO Cox algorithm, most are enhancers, exons, introns, 5 'untranslated regions and 3' untranslated regions, with a number of promoters of only 1.
Further, the gene region of LIX1L is chr1:145957635-145958017, LINC01822 gene region is chr2: 21710623-212175023, TUBA4A gene region is chr2:219251586-219251713, AC023981.1 gene region is chr3:8365593-8365799, MFSD1 gene region is chr3: 158804371-1585340, AC097173.1 gene region is chr4: 119872612-1193113, FAM153B gene region is chr5: 176114061-17115592, STEAP2/STEAP2-AS1 gene region is chr7:90178863-90179364, CDKN2B gene region is chr9:22005202-22006271, FAM205BP gene region is chr9:3995-34958, GAD2 gene region is chr10: 26223977-2626538, LETMD1 gene region is chr12: 51056370-56502, RALG12-4952-AS 1 gene region is chr9: 90178863-46394, and A is used for making use of the gene region of WO-46394.46394.
In the technical scheme, the gene region of LIX1L is the 1 st exon, and the corresponding region is 145957635 th to 145958017 th bases on chromosome 1; the gene region of LINC01822 is promoter, the corresponding region is 21710623 th to 21712623 th base on chromosome 2; the gene region of TUBA4A is the 3 rd exon, and the corresponding region is 219251586 th to 219251713 th bases on chromosome 2; the gene region of AC023481.1 is the 2 nd exon or 5' untranslated region thereof, and the corresponding region is 8365593 th to 8365799 th bases on chromosome 3; the gene region of MFSD1 is intron 2, and the corresponding region is 158804371 to 158805340 bases on chromosome 3; the gene region of AC097173.1 is enhancer, and the corresponding region is 119872612 th to 119873113 th bases on chromosome 4; the gene region of FAM153B is 22 nd intron, and the corresponding region is 176114061 th to 176115592 th bases on chromosome 5; the gene region of STEAP2/STEAP2-AS1 is enhancer, and the corresponding region is 90178863 th to 90179364 th bases on chromosome 7; the CDKN2B gene region is the 1 st exon or 5' untranslated region, and the corresponding region is 22005202 th to 22006271 th bases on chromosome 9; the gene region of FAM205BP is the 1 st exon or 5' untranslated region, and the corresponding region is 34838349 th to 34838586 th bases on chromosome 9; the gene region of GAD2 is the 5 th intron, and the corresponding region is 26223977 th to 26224538 th bases on chromosome 10; the gene region of LETMD1 is the 1 st exon or 3' untranslated region, and the corresponding region is 51056370 th to 51056502 th bases on chromosome 12; the gene region of RASSF3 is enhancer, and the corresponding region is 64671189 th to 64671690 th bases on chromosome 12; the gene region of AC073592.4 is enhancer, and the corresponding region is 124626183 th to 124626684 th bases on chromosome 12; the gene region of GOLGA6B is enhancer, and the corresponding region is 72237361 th to 72237862 th bases on chromosome 15; the gene region of ORC6 is intron 1, and the corresponding region is 46689829 th to 46690990 th bases on chromosome 16; the gene region of TMEM106A is the 5' untranslated region, and the corresponding region is 43213020 th to 43213041 th bases on chromosome 17. The location of the gene region is clear and fixed. Based on the methylation level detection results of the above gene regions, a genomic methylation score can be calculated for predicting prognosis of intrahepatic cholangiocellular carcinoma of the patient.
Further, the biomarkers include the gene regions of LIX1L, AC097173.1, STEAP2/STEAP2-AS1, CDKN2B, ORC, and TMEM 106A. The constructed genome methylation score is the sum of the methylation degree of the gene region multiplied by the weight coefficient. In the technical scheme, the weight coefficients of LIX1L, AC097173.1, STEAP2/STEAP2-AS1, CDKN2B, ORC and TMEM106A are relatively higher, and prognosis can be predicted and evaluated by calculating GMS only by adopting the gene regions of the genes. Preferably, in some embodiments, the biomarker comprises, in addition to the gene regions of LIX1L, AC097173.1, STEAP2/STEAP2-AS1, CDKN2B, ORC6 and TMEM106A, the gene regions of LINC01822, TUBA4A, FAM153B, FAM BP, GAD2 and GOLGA 6B. In one or more embodiments, the biomarker includes the 17 gene regions described above.
The invention provides application of any of the biomarkers in prognosis prediction of intrahepatic cholangiocellular carcinoma, wherein in the application, methylation level of the biomarker is detected, genome methylation score is calculated based on the methylation level, and prognosis of intrahepatic cholangiocellular carcinoma is carried out according to the genome methylation score.
In this protocol, the methylation level of a biomarker of a patient is first detected. The methylation level can be detected by any of the existing methylation detection methods. In some embodiments, methylation detection may be performed by other methylation sequencing methods, such as whole genome DNA methylation sequencing or pyrosequencing, by gene chip methods, or by specific methylation qPCR methods. Preferably, the methylation level of a region of a gene of a patient is detected using WGBS.
Further, the methylation level of a region of a gene is a percentage of methylated cytosines in a biological sample. In the present embodiment, the methylation level of the gene region has a value of 0 to 1, which specifically refers to the sum of the number of methylated cytosines divided by the number of unmethylated cytosines on the gene region of a cell in a biological sample, such as a tissue. Compared with the mode of setting the cut-off value and directly taking the methylation level as 0 or 1 according to the cut-off value, the methylation level detection in the technical scheme is continuous, the genome methylation scores of different patients are more obvious in difference, and further the difference in prognosis prediction of intrahepatic cholangiocellular carcinoma is more effectively represented. In some embodiments, the methylation level can also be expressed in other ways, such as when using qPCR for methylation detection, where the methylation level is expressed as the ratio of the number of methylation-targeted genes divided by the number of reference genes.
After obtaining the methylation level of the gene region, the genomic methylation score GMS of the patient is calculated using the calculation formula of the genomic methylation score. Specifically, the calculation formula is:wherein N is the number of gene regions, GR, used to calculate the methylation score of the genome i Methylation level of the ith Gene region, w i I=1, 2, …, N, which is the coefficient of the i-th gene region.
Preferably, in the above GMS formula, the gene region of gene LIX1L has a coefficient of-2.21, the gene region of gene LINC01822 has a coefficient of-0.35, the gene region of gene TUBA4A has a coefficient of-0.47, the gene region of gene AC023481.1 has a coefficient of-0.25, the gene region of gene MFSD1 has a coefficient of-0.23, the gene region of gene AC097173.1 has a coefficient of 0.64, the gene region of gene FAM153B has a coefficient of-0.57, the gene STEAP2 or STEAP2-AS1 has a coefficient of-0.62, the gene region of gene CDKN2B has a coefficient of-0.88, the gene region of gene FAM205BP has a coefficient of-0.30, the gene region of gene GAD2 has a coefficient of-0.48, the gene region of gene lemd 1 has a coefficient of-0.21, the gene region of gene RASSF3 has a coefficient of-0.28, the gene region of gene AC073592.4 has a coefficient of-0.11, the gene lgg 6-B has a coefficient of-0.04, and the gene region of gene c 106.73 has a coefficient of-0.73. The cut-off value is determined by the surviving_cut-point function in the surviviner package, and the prognosis of patients in low value groups with GMS below the cut-off value is better than the prognosis of patients in high value groups with GMS above the cut-off value. Preferably, when the calculation formula of the GMS uses the above coefficients, the optimum cut-off value of the GMS is-3.10.
In the invention, three patients with independent liver and gall centers are divided into a training group and a verification group, a GMS model is constructed through the training group, a gene region in the GMS model and a weight coefficient corresponding to the gene region are determined, and then the GMS model is verified in the verification group, so that the effect of the GMS in prognosis prediction of intrahepatic cholangiocellular carcinoma is verified. Furthermore, comparing the constructed GMS model with nomograms, and the united states joint cancer committee (AJCC) TNM staging system (eighth edition), the excellent prognostic performance of the GMS model was demonstrated by C index, AUC and Kaplan-Meier survival curves.
The invention provides a prognosis product for intrahepatic cholangiocellular carcinoma, which comprises a detection reagent for detecting the methylation level of the aforementioned biomarker. In some embodiments, the product may be a kit comprising primers or chips for detecting the methylation level of the aforementioned biomarkers. In some embodiments, the product may also be a gene chip by which the methylation level of the biomarker is detected.
The invention also provides a system for prognosis of intrahepatic cholangiocellular carcinoma, which comprises a detection unit, a calculation unit and an analysis unit, wherein:
The detection unit is used for detecting the methylation level of the biomarker in the biological sample;
the calculation unit is used for calculating genome methylation scores based on the methylation levels;
the analysis unit is used for comparing the genome methylation score with the magnitude of the cut-off value, and dividing the patients into a low-value group and a high-value group according to the comparison result, wherein the prognosis of the patients in the low-value group is better than that of the patients in the high-value group.
Further, the calculation formula of the calculation unit for calculating the genome methylation score is:wherein N is the number of gene regions, GR, used to calculate the methylation score of the genome i Methylation level of the ith Gene region, w i I=1, 2, …, N, which is the coefficient of the i-th gene region.
In one or more embodiments, after the weighting factors of the gene regions are introduced, the calculation formula of the GMS is: (-2.21) chr1:145957635-145958017+ (-0.35) chr2:21710623-21712623+ (-0.47) chr2:219251586-219251713+ (-0.25) chr3:8365593-8365799+ (-0.23) chr3:158804371-158805340+0.64 chr4:11987612-119873113 + (-0.57) chr5: 176114061-176115592: 90178863-90179364+ (-0.88) chr9:22005202-22006271+ (-0.30) chr9:34838349-34838586+ (-0.48) chr10:26223977-26224538+ (-0.21) chr12:51056370-51056502+ (-0.28) chr12:64671189-64671690+ (-0.11) chr12:124626183-124626684+ (-0.54) chr15:39315:39316.73+ (-39.73) chr4:.
Further, the methylation level of the gene region is a percentage of methylated cytosines in the biological sample.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention adopts whole genome DNA methylation sequencing, describes the whole epigenetic change of the intrahepatic cholangiocarcinoma in the whole genome range, and systematically evaluates the value of methylation levels of 17 gene regions in the prognosis of the intrahepatic cholangiocarcinoma;
2. the selected gene regions comprise enhancers, promoters, exons, introns, 5 '-untranslated regions and 3' -untranslated regions, which prove that methylation of various gene regions can be used as biomarkers for survival monitoring, and compared with the prior art which focuses on single CpG sites, the genome features are more comprehensively selected, so that genome methylation scores constructed by the gene regions can more accurately and reliably predict prognosis of intrahepatic cholangiocellular carcinoma;
3. in the present invention, the genome methylation score constructed based on the selected gene region, i.e., the GMS model, can effectively predict the long-term survival rate after surgery and the patient response to adjuvant therapy, the effect is verified in the verification group, and in addition, the excellent prognostic performance of the GMS model is demonstrated by the C index, AUC and Kaplan-Meier survival curves by comparing the constructed GMS model with the nomogram and united states cancer joint committee (AJCC) TNM staging system;
4. According to the application, patients are divided into two groups according to the ranking of GMS from high to low, the ranking position of the first group is 30% in front of the ranking position column, the ranking position of the second group is 70% behind the ranking position column, analysis shows that 30% of patients with GMS ranking in front can benefit from auxiliary treatment, and 70% of patients with GMS ranking in back can not benefit from auxiliary treatment, and by distinguishing patients, targeted auxiliary treatment can be carried out on patients capable of responding to auxiliary treatment more accurately;
5. compared with the method of setting a cut-off value in the prior art, the methylation level of the application adopts a mode of the percentage of the methylated cytosine in the biological sample, and the continuous change of the methylation level ensures that the difference of genome methylation scores of different patients is more obvious, thereby more effectively showing the difference in prognosis prediction of intrahepatic cholangiocellular carcinoma.
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The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a schematic flow chart of 17 gene regions further screened by univariate analysis, C index calculation, CV calculation and LASSO after pretreatment screening for construction of a GMS model;
FIG. 2 is a diagram of a further screening of 17 gene regions from 350 gene regions using LASSO regression analysis, including gene regions of genes LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP2/STEAP2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6 and TMEM 106A;
fig. 3 shows the area under the curve (AUC) of the ROC curves of the training group (WCHSU house), the validation group (ZSHFU & tmuci h house) and the external validation group (SEAE 450k house) for 1 year, 2 years and 3 years, and shows the survival curves of the GMS high value group/s GMS high value group and the GMS low value group/s GMS low value group among the three groups;
FIG. 4 shows ROC curves and areas under the curves for 1 year, 2 years, and 3 years for scoring training and validation sets using a GMS model, WCHSU alignment, JHUSM alignment, EHBSH alignment, and TNM staging system;
FIG. 5 shows survival curves for each group after four-fold grouping of patients according to scoring results of the GMS model, WCHSU nomogram, JHUSM nomogram, EHBSH nomogram, and TNM staging system;
FIG. 6 shows survival curves for patients with top 30% scores based on scoring results of the GMS model, WCHSU alignment, JHUSM alignment, EHBSH alignment, and TNM staging system with or without adjuvant therapy;
FIG. 7 shows a block diagram of a prognostic system in one or more embodiments of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
In the description of the present invention, a biological sample refers to one or more of cells, tissues, organs, body fluids, digestive fluids, expectoration, pulmonary bronchus cleaning fluid, urine, feces. In one or more embodiments, the biological sample is tissue of a patient.
In the present invention, prognosis refers to an expectation regarding medical development, such as a possibility of long-term survival, disease-free survival rate, etc., and includes a negative prognosis including disease progression such as recurrence, tumor growth, metastasis, and drug-resistant death rate, and a positive prognosis including disease alleviation and disease improvement, such as disease-free status, tumor regression, or stabilization.
In the examples below, statistics of the data were performed using RStudio 1.1.463,SPSS 25.0 and GraphPad Prism 8. The R-package "survivinal" was used for univariate Cox analysis and the R-package "rms" was used for consistency index (C index) calculation. For all experiments, a double tail p value of less than 0.05 was considered statistically significant.
In the present invention, to investigate the prognostic value of DNA methylation in but intracholangiocarcinoma, whole genome DNA methylation sequencing (WGBS) was used for a total of 334 patients from the university of si Hua Xi hospital, the university of double denier, the university of chinese and the university of Tianjin medical science tumor hospital. Wherein, the isolation of genomic DNA, and the pooling and sequencing of WGBS are performed by Novogene, inc. of Beijing norelsen; the FastQC is used to generate a quality report of the original WGBS sequencing read, and Fastp is used for quality control and filtering of low quality reads. DNA methylation analysis was performed using software MOABS. The rest of the experimental methods, which are not pointed out as specific conditions, are performed using conventional experimental conditions, methods or products in the prior art or according to the manufacturer's recommendations.
[ example 1 ]
This embodiment includes patients in multicenter and retrospective cohorts. Patients who received curative hepatectomy from 5 months in 2010 to 7 months in 2019 were studied in Hua Xi hospitals at university of Sichuan, in Zhongshan hospitals at double denier university and in tumor hospitals at university of Tianjin medical science. In view of clinical pathology and molecular heterogeneity, only intrahepatic cholangiocarcinoma patients were included. All patients were histologically diagnosed with intrahepatic cholangiocarcinoma for the first time, and patients with recurrent intrahepatic cholangiocarcinoma were not included. The study protocol has been approved by the ethics committee of three hepatobiliary centers and written informed consent was obtained for each patient prior to surgery.
A total of 334 patients selected from the three hepatobiliary centers were divided into training and validation groups. Of these, 164 patients in Huaxi Hospital (WCHSU house) at the university of Sichuan were training groups, 117 patients in Zhongshan Hospital (ZSFFU house) at the university of double denier and 53 patients in tumor Hospital (TMUCIH house) at the university of Tianjin medical science, and 170 patients in total constituted a verification group. The pathological parameters of the patients in the training and validation sets are shown in table 1. The median follow-up time of the training group was 28.5 months, and 106 patients died during the follow-up period; the median follow-up time for the validation group was 19.0 months, with 62 patients dying during the follow-up period.
TABLE 1
[ example 2 ]
And (3) carrying out methylation sequencing on the whole genome DNA of the patient in the training group to obtain the methylation level of the genome, and removing the gene regions such as the region with the identical CpG sites, the region with the NA number not less than 16, the region with the methylation level of 0 and the like, and then preliminarily screening 1028088 gene regions from 1606362 gene regions.
Subsequently, further screening was performed from the initially screened gene regions. After further removal of 12 gene regions with methylation level 0, 350 gene regions were screened using univariate Cox analysis, consistency index calculation, and coefficient of variation calculation, as shown in fig. 1. The 350 gene regions meet the following conditions: (1) in univariate Cox analysis, the p-value is less than 0.001; (2) a C index greater than 0.65; and (3) CV value is greater than 0.2.
Finally, compressing the high-dimensional data by using LASSO Cox algorithm, selecting candidate gene regions based on the pathological parameters and methylation level of the patient, as shown in fig. 2 (a) and (b), and finally obtaining 17 gene regions and weight coefficients corresponding to the gene regions, wherein the 17 gene regions are shown in table 2:
TABLE 2
Based on the gene regions and the coefficients of the respective gene regions in table 2, the following GMS models were obtained:
GMS＝(-2.21)*chr1:145957635-145958017+(-0.35)*chr2:21710623-21712623+(-0.47)*chr2:219251586-219251713+(-0.25)*chr3:8365593-8365799+(-0.23)*chr3:158804371-158805340+0.64*chr4:119872612-119873113+(-0.57)*chr5:176114061-176115592+(-0.62)*chr7:90178863-90179364+(-0.88)*chr9:22005202-22006271+(-0.30)*chr9:34838349-34838586+(-0.48)*chr10:26223977-26224538+(-0.21)*chr12:51056370-51056502+(-0.28)*chr12:64671189-64671690+(-0.11)*chr12:124626183-124626684+(-0.54)*chr15:72237361-72237862+(-15.04)*chr16:46689829-46690990+(-0.73)*chr17:43213020-43213041
[ example 3 ]
To verify the stability of the GMS model, a GMS investigation was first performed in a training set (WCHSU method) with a total survival (OS) C index of 0.779 (95% CI: 0.738-0.820).
As shown in fig. 3 (a), the area under the curve (AUC) for the total survival for 1, 2, and 3 years was 0.859, 0.842, and 0.880, respectively, in the training set. When the GMS model is applied, the optimal cut-off value of the GMS model is determined to be-3.10 according to a survivin_cutpoint function in a surviviner package. Patients are classified into a GMS low value group (GMS-low) and a GMS high value group (GMS-high) by comparing the magnitude of the patient's GMS score and cutoff value. Wherein 98 patients in the GMS low value group and 66 patients in the GMS high value group. As shown in fig. 3 (B), the median overall survival for 98 patients in the GMS low value group was 55.5±3.5 months, with survival rates of 93.9%, 74.1%, 46.3% for 1 year, 3 years, and 5 years, respectively; whereas the median overall survival for 66 patients in the GMS high value group was 10.5±1.5 months, survival rates of 1 year, 3 years and 5 years were 45.0%, 7.5% and 0%, respectively. It follows that the total survival of patients in the GMS low value group is significantly better than the total survival of patients in the GMS high value group.
Subsequently, the same gene region as that detected in the training group was detected in the validation group (ZSHFU & tmuci h cover) and carried into the same GMS model for calculation, resulting in a C index of 0.739 (95% ci:0.675 to 0.803) for total survival.
As shown in fig. 3 (C), the area under the curve (AUC) for 1, 2, and 3 years of total survival was 0.787, 0.770, and 0.786, respectively. Similarly, 170 patients in the verification group were successfully classified into a GMS low value group and a GMS high value group based on the GMS score and the same cutoff value as the training group. Of these, there were 111 patients in the GMS low-value group, 59 patients in the GMS high-value group, and reports of another 10 patients were temporarily unavailable (the verification group part parameter deletion causes were the same in table 1). As shown in fig. 3 (D), the median overall survival for the GMS low value group was 53.0±11.5 months, with survival rates of 92.2%, 70.8% and 41.8% for 1 year, 3 years and 5 years, respectively; whereas the median overall survival for the GMS high value group was 16.9±2.4 months, the survival rates for 1 year, 3 years and 5 years were 65.6%, 20.7% and 20.7%, respectively. The total survival of patients in the GMS low value group is significantly better than the total survival of patients in the GMS high value group.
The stability of the GMS model was then verified based on another external verification group (SEAE 450 house), which was the largest ICC queue with high throughput methylation data reported so far by apinya jussakul et al (total lifetime greater than 1 month), consisting mainly of ICC patients in southeast asia and europe. Methylation data for this validation set was generated by the Human Methylation BeadChip (450 k sequence) from Infinium, inc., as shown in Table 1, which covers only the gene regions of 6 representative CpG sites, i.e., the gene regions of the genes LIX1L, FAM153B, CDKN2B, FAM BP, AC073592.4 and ORC 6. Thus, using the same coefficients as the 6 gene regions in the GMS model of the training set, a simplified sgs model was obtained to calculate the genome methylation score. The total lifetime of the sGMS has a C index of 0.662 (95% CI: 0.575-0.750) and 1 year, 2 years, 3 years areas under the curve (AUC) of 0.670, 0.726, and 0.773, respectively, as shown in FIG. 3 (E).
Further, the median of the sGMS was taken as a cut-off point, and the patients were classified into a sGMS low value group (sGMS-low) and a sGMS high value group (sGMS-high), wherein the number of patients in the sGMS low value group was 45 and the number of patients in the sGMS high value group was 46. As shown in fig. 3 (F), the average OS for the gms low-value group was 70.5±7.9 months, the survival rates for 1 year, 3 years, and 5 years were 86.5%, 72.4%, and 55.3%, respectively, while the average OS for the gms high-value group was 18.0±1.5 months, and the survival rates for 1 year, 3 years, and 5 years were 65.8%, 34.5%, and 27.6%, respectively. The total survival of patients in the sGMS low value group is significantly better than that of patients in the sGMS high value group.
In summary, the GMS model can accurately predict patient survival, and its effects are demonstrated in two independent validation groups from different regions and countries, with high stability; meanwhile, the GMS model is adopted to divide the patients into a low-value group and a high-value group, so that the total survival time and the survival rate of the patients can be predicted and distinguished; moreover, the simplified sGMS model does not use all 17 gene regions, but can realize more accurate prognosis on the premise of only using 6 gene regions, and the patients are divided into a low-value group and a high-value group, so that the GMS model is further proved to have higher stability and accuracy.
[ example 4 ]
The AJCC TNM staging system and nomograms (nomograms) are commonly used to predict patient survival. Currently, the JHUSM nomograms disclosed by Hyper et al in A nomogram to predict long-term survival after resection for intrahepatic cholangiocarcinoma and the EHBSH nomograms disclosed by Wang et al in Prognostic nomogram for intrahepatic cholangiocarcinoma after partial hepatectomy are two highly referenced nomograms. To verify the performance of the GMS model, clinical parameters of the training and verification groups were analyzed using the jhasm alignment, EHBSH alignment, AJCC TNM staging system, WCHSU alignment of the university of tsuch, huaxi hospital.
The univariate and multivariate analysis data for the training and validation sets are shown in Table 3, and it can be seen from Table 3 that in multivariate analysis involving clinical parameters, the GMS model is an independent predictor of total survival (p <0.001,HR:3.201, 95%CI:2.451-4.180).
Table 3:
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further, it is demonstrated by pairwise comparison that the C index of GMS (0.769, 95% CI: 0.733-0.804) is higher than that of WCHSU alignment (0.715, 95% CI: 0.675-0.755, p=0.013), higher than that of JHUSM alignment (0.686, 95% CI: 0.642-0.729, p < 0.001), higher than that of EHBSH alignment (0.674, 95% CI: 0.632-0.716, p < 0.001), and higher than that of AJCC TNM staging system (0.605, 95% CI: 0.559-0.652, p < 0.001).
Fig. 4 (a) - (c) show GMS models for 1 year, 2 years, and 3 years, ROC curves for each nomogram and the staging system, respectively, and corresponding AUCs. From the graph, AUCs for 1, 2 and 3 years for the GMS model are 0.833, 0.822 and 0.853, respectively; AUC for 1, 2 and 3 years of WCHSU alignment were 0.747, 0.747 and 0.744, respectively; AUC for 1, 2 and 3 years of jhus alignment were 0.722, 0.724 and 0.702, respectively; AUC of EHBSH alignment for 1, 2 and 3 years was 0.707, 0.709 and 0.719, respectively; AUCs of the AJCC TNM staging system for 1, 2 and 3 years were 0.606, 0.622 and 0.608, respectively. It can be seen that the GMS model performs better than the WCHSU alignment, JHUSM alignment, EHBSH alignment and AJCC TNM staging systems.
In addition, after scoring the GMS model, WCHSU nomograms, jhus nomograms, EHBSH nomograms, and AJCC TNM staging system, patients were divided into 4 parts of quaternie 1, quaternie 2, quaternie 3, and quaternie 4, each part of the total number of patients being 25% of the total number, based on the score from low to high. As shown in fig. 5 (a), 4 survival curves of quaterile 1 to quaterile 4 of the GMS model can be completely separated, and the survival rate is sequentially reduced (overall p < 0.001). Also, as shown, the p value of quatil 2 to quatil 1 is less than 0.001, the p value of quatil 3 to quatil 2 is equal to 0.015, and the p value of quatil 4 to quatil 3 is less than 0.001, which also indicates that there is a significant difference between the two packets. In contrast, as shown in fig. 5 (b) to (e), although the overall p-values of the WCHSU alignment, jhsm alignment, EHBSH alignment, and AJCC TNM staging system all reached statistical significance, none of the survival curves exhibited complete separation as in the GSM model, and the survival rate was sequentially reduced, for example, in the WCHSU alignment, the survival curves of quaterile 3 and quaterile 2 crossed, and for example, in the jhsm alignment, the survival curves of quaterile 2 and quaterile 1 crossed. Furthermore, as can be seen from the p-value of the ratio of the adjacent two groups, there is no significant difference between the adjacent two groups.
[ example 5 ]
The GMS model may also be used to identify ICC patients that can benefit from Adjuvant Therapy (AT) after surgery. By trying different thresholds (10% -90%), the inventors found that 30% of patients with GMS scores going from high to low, were able to benefit from adjuvant therapy, while 70% of patients after ranking did not benefit from adjuvant therapy. As shown in fig. 6A and 6B, survival curves for the first 30% of patients with higher GMS scores receiving adjuvant treatment and not receiving adjuvant treatment were completely separated, hr=0.533, 95% ci: 0.329-0.866, log-rank p=0.032; survival curves for post 70% of patients with lower GMS scores, with and without adjuvant treatment, crossed, hr=1.264, 95% ci: 0.720-2.220, log-rank p=0.373.
As shown in fig. 6C to 6F, when the same critical value of 30% is adopted, 30% of patients before scoring of WCHSU alignment (hr=0.639, 95% ci:0.379 to 1.075, log-rank p=0.131), jhsm alignment (hr=0.732, 95% ci:0.426 to 1.257, log-rank p=0.294), EHBSH alignment (hr=0.784, 95% ci:0.464 to 1324, log-rank p=0.388) and TNM staging system (hr=0.948, 95% ci:0.533 to 1.685, log-rank p=0.855) appear worse in survival curves of both the patients receiving and the patients not receiving the adjuvant therapy, so that patients capable or incapable of responding to the adjuvant therapy cannot be distinguished. Therefore, by differentiating patients by the GMS model, targeted adjuvant therapy can be performed more accurately on patients who can respond to the adjuvant therapy.
[ example 6 ]
A prognosis system for intrahepatic cholangiocellular carcinoma as shown in fig. 7, comprising a detection unit, a calculation unit and an analysis unit, wherein:
the detection unit for detecting the methylation level of any one or more of the biomarkers of the previous embodiments in a biological sample;
the calculation unit is used for calculating genome methylation scores based on the methylation levels;
the analysis unit is used for comparing the genome methylation score with the magnitude of the cut-off value, and dividing the patients into a low-value group and a high-value group according to the comparison result, wherein the prognosis of the patients in the low-value group is better than that of the patients in the high-value group.
In one or more embodiments, methylation detection can be performed using other methylation sequencing methods, such as whole genome DNA methylation sequencing or pyrosequencing, can be performed using gene chip methods, or can be performed using specific methylation qPCR methods. Preferably, the methylation level of a region of a gene of a patient is detected using WGBS.
In some embodiments, the calculation unit calculates the genome methylation score according to the formula: wherein N is the number of gene regions used to calculate the genome methylation score, GRi is the methylation level of the ith gene region, w i I=1, 2, …, N, which is the coefficient of the i-th gene region.
In some embodiments, the methylation level of the gene region is a percentage of methylated cytosines in the biological sample. In one embodiment, the methylation level can also be expressed in other ways, such as when methylation detection is performed using qPCR, the methylation level is expressed as the ratio of the number of methylation-targeted genes divided by the number of reference genes.
In some embodiments, the computing unit computes the WCHSU nomograms according to clinical parameters in addition to the GMS score, and performs prognosis prediction in combination with the GMS score and the WCHSU nomograms to further improve accuracy of prognosis.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (6)

1. Use of a biomarker in the preparation of a prognostic prediction product for intrahepatic cholangiocellular carcinoma, characterized in that the biomarker is a combination of the gene regions of LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP/STEAP 2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6, TMEM106A, wherein the gene regions of AC097173.1, STEAP2/STEAP2-AS1, RASSF3, AC073592.4, GOLGA6B are enhancers, the gene regions of MFSD1, FAM153B, GAD, ORC6 are introns, the gene region of LIX1L, TUBA a is an exon, the gene region of LINC01822 is a promoter, the gene region of TMEM106A is a 5' untranslated region, the gene region of AC023481.1, CDKN2 92205 BP is a 5' untranslated region or the exon 3' untranslated region;
Detecting the methylation level of the biomarker, calculating a genomic methylation score based on the methylation level, and prognosis of intrahepatic cholangiocellular carcinoma according to the genomic methylation score.
2. The use according to claim 1, wherein the LIX1L gene region is chr1: 145957635-145958017, LINC01822 gene region is chr2: 21710623-212175023, TUBA4A gene region is chr2: 219251586-219251713, AC023981.1 gene region is chr3: 8365593-8365799, MFSD1 gene region is chr3: 158804371-158805340, AC0971973.1 gene region is chr4: 119872612-119873113, FAM153B gene region is chr5: 176114061-115592, STEAP2/STEAP2-AS1 gene region is chr7: 90178863-90179364, CD KNO 2B gene region is chr9: 22005202-22071, FAM205BP gene region is chr9: 34838349-348586, GAD2 gene region is chr10:6223-1193113, FAM 15373.1 gene region is chr5: 176114061-17592, STEAP2/STEAP2-AS1 gene region is chr7: 90178863-90179364, and CD 62-4639:4639-4639 gene region is chr, and the GAD2 gene region is used AS a gene region is chr-4639, and the F-taking place of WO-4639, and the F-taking place of the gene region AS a gene region of WO-taking place of WO-F-L.
3. The use according to claim 1, wherein the genome methylation score is calculated by the formula: gms= Wherein N is the number of gene regions used to calculate the genome methylation score, +.>Methylation level for the ith Gene region, < ->I=1, 2, …, N, the coefficient of the i-th gene region;
GMS=(-2.21)*chr1:145957635-145958017+(-0.35)*chr2:21710623-21712623+(-0.47)*chr2: 219251586-219251713+(-0.25)*chr3:8365593-8365799+(-0.23)*chr3:158804371-158805340+0.64*chr4:119872612-119873113+(-0.57)*chr5:176114061-176115592+(-0.62)*chr7:90178863-90179364+(-0.88)*chr9:22005202-22006271+(-0.30)*chr9:34838349-34838586+(-0.48)*chr10:26223977-26224538+(-0.21)*chr12:51056370-51056502+(-0.28)*chr12:64671189-64671690+(-0.11)*chr12:124626183-124626684+(-0.54)*chr15:72237361-72237862+(-15.04)*chr16:46689829-46690990+(-0.73)*chr17:43213020-43213041。
4. a prognosis product for intrahepatic cholangiocellular carcinoma, characterized in that it comprises a detection reagent for detecting the methylation level of a biomarker;
the biomarker is a combination of genes of LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP2/STEAP2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6, TMEM106A, wherein the gene region of AC097173.1, STEAP2/STEAP2-AS1, RASSF3, AC073592.4, GOLGA6B is an enhancer, the gene region of MFSD1, FAM153B, GAD2, ORC6 is an intron, the gene region of LIX1L, TUBA a is an exon, the gene region of LINC01822 is a promoter, the gene region of TMEM106A is a 5' untranslated region, the gene region of AC023481.1, CDKN2B, FAM BP is a 5' untranslated region or exon, and the gene region of letm 1 is a 3' untranslated region or exon.
5. A hepatobiliary cell cancer prognosis system, comprising a detection unit, a calculation unit, and an analysis unit, wherein:
the detection unit is used for detecting the methylation level of the biomarker in the biological sample;
the calculation unit is used for calculating genome methylation scores based on the methylation levels;
the analysis unit is used for comparing the genome methylation score with the magnitude of the cut-off value, and dividing the patients into a low-value group and a high-value group according to the comparison result, wherein the prognosis of the patients in the low-value group is better than that of the patients in the high-value group;
the biomarker is a combination of gene regions of LIX1L, LINC01822, TUBA4A, AC023481.1, MFSD1, AC097173.1, FAM153B, STEAP2/STEAP2-AS1, CDKN2B, FAM BP, GAD2, LETMD1, RASSF3, AC073592.4, GOLGA6B, ORC6, TMEM106A, wherein the gene regions of AC097173.1, STEAP2/STEAP2-AS1, RASSF3, AC073592.4, GOLGA6B are enhancers, the gene regions of MFSD1, FAM153B, GAD, ORC6 are introns, the gene region of LIX1L, TUBA4A is an exon, the gene region of LINC01822 is a promoter, the gene region of TMEM106A is a 5' untranslated region, the gene regions of AC023481.1, CDKN2B, FAM BP are 5' untranslated regions or exons, and the gene region of letm 1 is a 3' untranslated region or exon;
The calculation formula for calculating the genome methylation score by the calculation unit is as follows: gms=Wherein N is the number of gene regions used to calculate the genome methylation score, +.>Methylation level for the ith Gene region, < ->I=1, 2, …, N, the coefficient of the i-th gene region;
GMS=(-2.21)*chr1:145957635-145958017+(-0.35)*chr2:21710623-21712623+(-0.47)*chr2: 219251586-219251713+(-0.25)*chr3:8365593-8365799+(-0.23)*chr3:158804371-158805340+0.64*chr4:119872612-119873113+(-0.57)*chr5:176114061-176115592+(-0.62)*chr7:90178863-90179364+(-0.88)*chr9:22005202-22006271+(-0.30)*chr9:34838349-34838586+(-0.48)*chr10:26223977-26224538+(-0.21)*chr12:51056370-51056502+(-0.28)*chr12:64671189-64671690+(-0.11)*chr12:124626183-124626684+(-0.54)*chr15:72237361-72237862+(-15.04)*chr16:46689829-46690990+(-0.73)*chr17:43213020-43213041；
the LIX1L has a gene region of chr1: 145957635-145958017, LINC01822 has a gene region of chr2: 21710623-212175023, TUBA4A has a gene region of chr2: 219251586-219251713, AC023981.1 has a gene region of chr3: 8365593-8365799, MFSD1 has a gene region of chr3: 158804371-158805340, AC0971973.1 has a gene region of chr4: 119872612-119873113, FAM153B has a gene region of chr5: 176114061-1717115592, STEAP2/STEAP2-AS1 has a gene region of chr7: 90178863-90179364, TUBA2B has a gene region of chr9: 22005202-22071, FAM has a gene region of chr9:65-34838586, GAD2 has a gene region of chr10: 26223977-26538, and a gene region of LEDS 1:3712-46394, and A has a gene region of WO-46394, and a gene region of WO-46394-46394.62, and a gene region of WO-46394 is a gene region of WO-4639-46394.
6. The system of claim 5, wherein the methylation level of the gene region is a percentage of methylated cytosines in the biological sample.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103305604A (en) * 2013-05-09 2013-09-18 宁波大学 Kit for detecting CDKN2B gene promoter region methylation degree related to coronary disease and application thereof
WO2019077191A1 (en) * 2017-10-20 2019-04-25 Universidad De Málaga Precision medicine for the diagnosis, prognosis and prediction of colorectal cancer
CN111122865A (en) * 2019-12-12 2020-05-08 中山大学 Marker for liver cancer prognosis prediction based on CD11b and CD169 protein molecules
CN111458508A (en) * 2020-04-14 2020-07-28 中国人民解放军海军军医大学第三附属医院 Molecular marker, kit and method for evaluating intrahepatic bile duct cancer prognosis
CN112280868A (en) * 2020-12-25 2021-01-29 江苏高美基因科技有限公司 Intrahepatic cholangiocellular carcinoma patient prognosis detection biomarker and detection kit
KR20210087690A (en) * 2020-01-03 2021-07-13 서울대학교산학협력단 DLEC1 methylation marker for predicting prognosis of intrahepatic cholangiocarcinoma

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10184154B2 (en) * 2014-09-26 2019-01-22 Mayo Foundation For Medical Education And Research Detecting cholangiocarcinoma

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103305604A (en) * 2013-05-09 2013-09-18 宁波大学 Kit for detecting CDKN2B gene promoter region methylation degree related to coronary disease and application thereof
WO2019077191A1 (en) * 2017-10-20 2019-04-25 Universidad De Málaga Precision medicine for the diagnosis, prognosis and prediction of colorectal cancer
CN111122865A (en) * 2019-12-12 2020-05-08 中山大学 Marker for liver cancer prognosis prediction based on CD11b and CD169 protein molecules
KR20210087690A (en) * 2020-01-03 2021-07-13 서울대학교산학협력단 DLEC1 methylation marker for predicting prognosis of intrahepatic cholangiocarcinoma
CN111458508A (en) * 2020-04-14 2020-07-28 中国人民解放军海军军医大学第三附属医院 Molecular marker, kit and method for evaluating intrahepatic bile duct cancer prognosis
CN112280868A (en) * 2020-12-25 2021-01-29 江苏高美基因科技有限公司 Intrahepatic cholangiocellular carcinoma patient prognosis detection biomarker and detection kit

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
肝内胆管细胞癌预后分子生物标志物的研究;谢巧华;万方;全文 *

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