CN111564214B - Method for establishing and verifying breast cancer prognosis evaluation model based on 7 special genes - Google Patents
Method for establishing and verifying breast cancer prognosis evaluation model based on 7 special genes Download PDFInfo
- Publication number
- CN111564214B CN111564214B CN201910114297.7A CN201910114297A CN111564214B CN 111564214 B CN111564214 B CN 111564214B CN 201910114297 A CN201910114297 A CN 201910114297A CN 111564214 B CN111564214 B CN 111564214B
- Authority
- CN
- China
- Prior art keywords
- genes
- breast cancer
- risk
- survival
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention relates to the field of gene technology and biomedicine, in particular to a method for establishing and verifying a breast cancer prognosis evaluation model based on 7 special genes. The invention provides seven genes related to survival in breast cancer development and a breast cancer prognosis evaluation model establishment and verification method based on 7 special genes. The seven genes associated with survival in the development of breast cancer include LYVE1, CD209, TMEM190, TUBA3D, AL049749.1, AC123595.1 and LILRB5. The model can accurately evaluate the risk of breast cancer incidence, accurately predict the prognosis of a patient and guide clinical treatment. By combining transcriptome data with clinical data analysis, the 7-gene combined prediction prognosis is found to be more accurate, and a foundation can be laid for clinical transformation through nomographic evaluation of patient prognosis.
Description
Technical Field
The invention relates to the field of gene technology and biomedicine, in particular to a method for establishing and verifying a breast cancer prognosis evaluation model based on 7 special genes.
Background
Breast cancer (BRCA) is one of the most common invasive cancers in women, accounting for 16% of all female cancers. Studies have shown that 12% of women worldwide suffer from this disease. Studies estimated that about 20120 new invasive breast cancers and 40920 breast cancer deaths in us women in 2018, and breast cancer accounts for 30% of the newly diagnosed cancers in women. In the past, we have seen efforts in the invention of breast cancer, but there are still many obstacles to treatment due to the heterogeneity of molecular and genetic levels (multiple subtypes) of breast cancer disease. Mammography is the most commonly used screening method for breast cancer, but problems such as overdiagnosis and oversherapy are difficult to avoid, which may lead to poor therapeutic results and poor prognosis. In addition, some traditional classification criteria, such as tumor size, axillary lymph node size, histological grading, steroid receptor expression levels, etc., have also been established and used in clinical work, but their predictive effect is not perfect because of the heterogeneity of breast cancer. Therefore, it is particularly important to find more biomarkers at the molecular level.
High throughput transcriptome sequencing technology (RNA-Seq) is used by an increasing number of researchers to compare differential genes, discover emerging markers, and to analyze various tumor, phenotype and prognostic information in depth. Currently, several genetic biomarkers have been used for prognostic evaluation of breast cancer patients, such as c-Met, ki67, BRCA1/2, etc., but the evaluation of a single prognostic marker results as follows: often not accurate enough and lack of practicality in clinical practice. In contrast, the prediction markers of BRCA patients are analyzed by establishing a patient risk score assessment model based on the whole genome expression profile analysis dataset by using a Cox multivariate regression model, and the reliability and the effectiveness are improved through ROC curve identification.
Disclosure of Invention
In view of the problems existing in the prior art, the invention aims to provide seven genes related to survival in the development of breast cancer and breast cancer, and a method for establishing and verifying a breast cancer prognosis evaluation model based on 7 special genes, wherein the model can accurately evaluate the risk of onset of breast cancer, accurately predict the prognosis of a patient and guide clinical treatment. In addition, by combining the risk score with other clinical indicators, a new nomogram is created.
In order to achieve the above object, the present invention adopts the following technical scheme.
Seven genes involved in survival in the development of breast cancer, including LYVE1, CD209, TMEM190, TUBA3D, AL049749.1, AC123595.1, and LILRB5.
A method for screening seven genes associated with survival in the development of breast cancer, comprising the steps of.
1) Statistical analysis was performed using R package-http:// www.r-project. Org, all in R version 3.3.2 using the following packages: "glmnet '," lpc ', "CoxBoost '," Limma ', "pROC '," rms ".
2) 684 breast cancers were downloaded from TCGA website, of which 113 in normal group and 571 in cancer group; the test panel samples were subjected to univariate analysis by screening for differential genes by p <0.01 and log2 fold change >2, followed by multivariate analysis and stepwise regression to select 7 genes with significant prognosis for BRCA patients.
A method for establishing a breast cancer prognosis evaluation model based on 7 special genes specifically comprises the following steps.
After screening seven genes related to survival in breast cancer development, wind is established by stepwise regressionThe risk model, and in addition the risk score staging model, is developed by the R package "survival" function coxph () as follows:where βi represents the coefficient of each gene, xi represents the z-score converted relative expression value of each gene, and β>0 is defined as inversely related to survival time, beta<0 is defined as a protective gene; patients were divided into high risk and low risk groups according to median risk score, the risk score for each BRCA patient group was calculated using the model, R-pack "survival" was used to generate OS curves, and a double-sided log rank test was used to determine survival differences between the high and low risk patient groups.
A breast cancer prognosis evaluation model verification method based on 7 special genes specifically comprises the following steps.
1) And (3) verifying a prediction model: to test the stability of the predictive model, its predictive performance was further validated by testing the dataset and the entire dataset, using Kaplan-Meier survival curves and log rank test analysis to evaluate the differences in patient survival time in each of the two groups, receiver Operating Characteristics (ROC) were calculated using the "pROC" package to evaluate the model's specificity and sensitivity.
2) Clinical factor analysis: to determine whether the risk model is an independent prognostic factor, independent of other clinical observations of breast cancer patients, and its importance, a Cox proportional hazards regression model is used to compare the importance of clinical observations and risk scores, and to determine independent prognostic factors; the differences between the various clinical information and risk scores were then compared and the R package "rms" binding age, risk score, sex, estrogen receptor, histological typing and progesterone receptor established a new nomogram.
3) Functional enrichment analysis: to explore the role of these seven specific genes in the occurrence, progression and prognosis of breast cancer, GO functional enrichment analysis was performed, differences between high and low risk genes in bypass and biological processes were analyzed using JAVA GSEA software, and in depth analysis was performed.
Compared with the prior art, the invention has the following beneficial effects.
The invention provides seven genes related to survival in breast cancer and breast cancer development, and a method for establishing and verifying a breast cancer prognosis evaluation model based on 7 special genes, wherein the model can accurately evaluate the risk of onset of breast cancer, accurately predict prognosis of patients and guide clinical treatment. In addition, by combining the risk score with other clinical indicators, a new nomogram is created. The invention discovers that the 7 genes are more accurate in combined prediction and prognosis through the transcriptome data and the clinical data analysis, and can lay a foundation for clinical transformation through the nomogram for the prognosis evaluation of patients.
Drawings
FIG. 1 is a graph of performance of 7 gene signatures in a training dataset, where A is a Kaplan-Meier survival curve for overall survival between a high risk group and a low risk group in the training dataset; b is a Receiver Operating Characteristic (ROC) curve in the training dataset that is time dependent; c is 7 gene signature of 7 genes, survival status and distribution of expression profile of patients in training dataset.
Figure 2 is a performance of risk scoring in a test dataset and an overall dataset. Wherein A is a Kaplan-Meier survival curve of overall survival of BRCA patients by testing 7 gene signatures in the dataset; b is ROC curve analysis of 7 gene characteristics in the test data set; c is 7 gene characteristics in the whole data set, and Kaplan-Meier survival curve analysis is carried out on the overall survival rate of LAC patients; d is the ROC curve analysis of 7 gene signatures throughout the dataset.
FIG. 3 is a detail of survival in the test dataset and the entire dataset, wherein A is the distribution of 7 gene signatures, survival status and expression profiles of 7 genes for patients in the test dataset; b is the 7 gene signature, survival status and distribution of expression profile of 7 genes for patients throughout the dataset.
Fig. 4 is the clinical significance of clinical information and risk scores throughout the dataset (clinical factors and clinical importance of risk scores. CI, confidence interval; HR, risk ratio).
Fig. 5 is an association between risk scores and clinical factors throughout the dataset. In addition: age, estrogen receptor, sex, histology, progestin receptor, staging.
FIG. 6 is a Kaplan-Meier survival curve analysis of overall patient survival by age and tumor stage using 7 gene signature throughout the dataset. Wherein A is the Kaplan-Meier tumor survival curve for the young patient group; b is Kaplan-Meier survival curve of the elderly patient group; c is Kaplan-Meier survival curve of early patient group; d is the Kaplan-Meier survival curve for the late patient group.
Fig. 7 is a nomogram including clinical factors and risk scores.
FIG. 8 is GSEA results for high and low risk differentially expressed genes in TCGA, wherein A is the cell cycle; b is cell division; c is the mitotic cell cycle.
Detailed Description
The invention will now be described in detail with reference to the drawings and examples, which are only preferred embodiments of the invention, it being noted that modifications and additions can be made to the person skilled in the art without departing from the method of the invention, which modifications and additions should also be considered as being within the scope of the invention.
An embodiment is a method for establishing and verifying a breast cancer prognosis evaluation model based on 7 special genes.
(1) Genes associated with survival in BRCA development were identified.
To determine new specific biomarkers associated with BRCA patient prognosis, test panel samples were subjected to univariate analysis followed by multivariate analysis, stepwise regression to select 7 genes with significant BRCA patient prognosis, table 1. And a patient risk assessment model based on these 7 genes was established, namely: risk score = (-0.15110) lyve1+ 0.4676 lilrb5+ (-0.2924) cd209+ 0.1744 ] AL049749.1 +0.1990 ] AC123595.1 + (-0.1735) TMEM 190+ (-0.1024) TUBA3D.
(2) And verifying the performance of the risk scoring model in the training set.
Using this 7-gene signature model, risk was assessed for each patient, and the test dataset and the entire dataset were divided into high-risk and low-risk groups using median risk scores. By comparing the results of the survival analysis, a significant difference in OS curves between the two risk groups was found. The survival time was significantly shorter in the high risk group than in the low risk group (p <0.0001, see figure 1A). Meanwhile, by researching the ROC curve of patient survival, the AUC of the prediction model is as high as 0.705, as shown in FIG. 1B, which shows that the model has good stability. In addition, other survival details were analyzed, including risk score distribution, survival status, and expression levels of these seven specific genes, as shown in fig. 1C.
(3) The stability of the predictive model is evaluated.
The model was validated using the test dataset (n=228) and the entire dataset (n=684). The test dataset and the complete dataset are also divided into a high risk group and a low risk group using median risk scores.
The test dataset is similar to the training set, with the OS curves between the two groups being significantly different, as shown in FIG. 2A. The survival time of the high risk group was shorter than that of the low risk group (p=0.026) and AUC of the ROC curve was 0.613 (as shown in fig. 2B), indicating that the model had good stability.
This 7 gene signature predictive model was further used for the entire observation dataset. Also, there was a significant difference in OS curves between the two groups, higher risk scores generally predicted a worse prognosis (p < 0.0001), see FIG. 2C; AUC of ROC curve was 0.671, as shown in fig. 2D, indicating that this model has good stability. Similarly, the risk score distribution, survival status and expression level of these seven specific genes were analyzed in the test dataset and the entire dataset, and the detailed results are shown in fig. 3.
(4) The risk score is correlated with patient outcome and clinical parameters throughout the dataset.
To compare the importance of risk scores to the prognosis of patients with conventional clinical factors (age, sex, estrogen receptor, progesterone receptor, histological typing, tumor grading), the risk scores were combined with other clinical indicators. Multivariate Cox regression analysis showed that the 7 gene marker risk scores still independently predicted other clinical factors (HR, 2.464;95%CI,1.546 to 3.929; p < 0.0001), indicating that the risk scores were likely compared to the independent predictors of OS as compared to other factors, as shown in figure 4. And (3) performing correlation analysis: the risk scores and other clinical indicators of BRCA were collected for correlation analysis and the results were displayed as shown in fig. 5.
To investigate the predictive value of 7 gene signatures in the same clinical information, further hierarchical analysis was performed on the data. Based on the results of the multivariate regression analysis, two more sensitive clinical indices, age (P < 0.001) and tumor grade (P < 0.05), were selected for further analysis. All patients were first divided into two risk subgroups, age and age, median age (65 years). The predictive model may be a high risk group and a low risk group, or may be two operating systems. The curves are significantly different (p=0.0036), the higher the risk score, the shorter the survival (as shown in fig. 6A); the predictive model also allowed the elderly to be assigned a high risk score and a low risk score group, but both were not significantly different in OS curves (p=0.051) (as shown in fig. 6B).
Also, patients were classified into early stage (stage 1-2) and late stage (stage 3-4) according to tumor stage, the predictive model served as a high risk combination low risk group with significant difference in OS curves between the two groups (p=0.0013), and the survival rate was significantly lower in the high risk group than in the low risk group. In the latter group, a similar structure (p=0.02) still exists, as shown in fig. 6C and 6D. In summary, both multivariate and stratified analyses showed that 7 gene signatures could be used as independent indicators of other clinical variables in BRCA survival prediction.
(5) A new nomogram is created.
To make this 7-gene signature model more suitable for clinical use, the results of multivariate regression analysis were combined with other 6 clinical predictor (age, sex, estrogen receptor, progesterone receptor, histology) types and tumor grade. It can be seen that a high total score indicates a lower survival rate for five years, while a low score indicates the opposite. The established alignment of predicted OS is suitable for predicting prognosis of BRCA patients in clinical practice, as shown in fig. 7.
(6) Exploration of these 7 genes could predict patient prognosis and explore important signaling pathways for high and low risk groups, and GO functional enrichment analysis was performed as shown in fig. 8. The discovery that the "cell cycle", "cell differentiation" and "cell mitotic cycle" gene pathways are higher in the high risk group than in the low risk group suggests that DNA loss of cells from these seven genes is involved in biological processes that may also be a significant contributor to the prognostic impact of BRCA patients.
Claims (4)
- Use of a combination of seven genes of lyve1, CD209, TMEM190, TUBA3D, AL049749.1, AC123595.1 and LILRB5 for the preparation of a prognostic evaluation product for breast cancer.
- 2. A method for screening seven genes according to claim 1, comprising the steps of:1) Statistical analysis was performed using R packets, all in version R3.3.2 using the following packets: "'glmcet', 'lpc', 'CoxBoost', 'Limma', 'pROC', 'rms',;2) 684 breast cancers were downloaded from TCGA website, of which 113 in normal group and 571 in cancer group; the test panel samples were subjected to univariate analysis by screening for differential genes by p <0.01 and log2 fold change >2, followed by multivariate analysis and stepwise regression to select 7 genes with significant prognosis for BRCA patients.
- 3. A method for establishing a breast cancer prognosis evaluation model based on seven genes as claimed in claim 1, which is characterized by comprising the following steps:after screening seven genes related to survival in breast cancer development, establishing a risk model through stepwise regression, and developing a risk scoring stage model by an R-package survival function coxph (), wherein the formula is as follows:where βi represents the coefficient of each gene, xi represents the z-score converted relative expression value of each gene, and β>0 is defined as inversely related to survival time, beta<0 is defined as a protective gene; patients were divided into high risk and low risk groups according to median risk score, the risk score for each BRCA patient group was calculated using the model, R-pack "survival" was used to generate OS curves, and a double-sided log rank test was used to determine survival differences between the high and low risk patient groups.
- 4. A method for verifying a breast cancer prognosis evaluation model based on seven genes as defined in claim 1, which is characterized by comprising the following steps:1) And (3) verifying a prediction model: to test the stability of the predictive model, its predictive performance was further validated by testing the dataset and the entire dataset, using Kaplan-Meier survival curves and log rank test analysis to evaluate the differences in patient survival time in each of the two groups, receiver operating characteristics were calculated using the "pROC" package to evaluate the model's specificity and sensitivity;2) Clinical factor analysis: to determine whether the risk model is an independent prognostic factor, independent of other clinical observations of breast cancer patients, and its importance, a Cox proportional hazards regression model is used to compare the importance of clinical observations and risk scores, and to determine independent prognostic factors; then comparing the differences between the various clinical information and the risk scores, and creating a new nomogram for R package "rms" binding age, risk score, sex, estrogen receptor, histological typing, and progesterone receptor;3) Functional enrichment analysis: to explore the role of these seven specific genes in the occurrence, progression and prognosis of breast cancer, GO functional enrichment analysis was performed, differences between high and low risk genes in bypass and biological processes were analyzed using JAVA GSEA software, and in depth analysis was performed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910114297.7A CN111564214B (en) | 2019-02-14 | 2019-02-14 | Method for establishing and verifying breast cancer prognosis evaluation model based on 7 special genes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910114297.7A CN111564214B (en) | 2019-02-14 | 2019-02-14 | Method for establishing and verifying breast cancer prognosis evaluation model based on 7 special genes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111564214A CN111564214A (en) | 2020-08-21 |
| CN111564214B true CN111564214B (en) | 2023-08-11 |
Family
ID=72074121
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910114297.7A Active CN111564214B (en) | 2019-02-14 | 2019-02-14 | Method for establishing and verifying breast cancer prognosis evaluation model based on 7 special genes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111564214B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112185546A (en) * | 2020-09-23 | 2021-01-05 | 山东大学第二医院 | Model for prognosis prediction of breast cancer patient and establishing method |
| CN112501290B (en) * | 2020-09-30 | 2022-11-22 | 首都医科大学 | Marker molecule related to breast cancer prognosis and detection kit |
| CN112331343A (en) * | 2020-11-04 | 2021-02-05 | 复旦大学附属中山医院 | Method for establishing hepatocellular carcinoma postoperative risk assessment model |
| CN112735592B (en) * | 2021-01-18 | 2024-03-01 | 中国医学科学院肿瘤医院 | Construction method and application method of lung cancer prognosis model and electronic equipment |
| CN112735529A (en) * | 2021-01-18 | 2021-04-30 | 中国医学科学院肿瘤医院 | Breast cancer prognosis model construction method, application method and electronic equipment |
| CN113380415A (en) * | 2021-05-20 | 2021-09-10 | 昆明医科大学第二附属医院 | Alcohol use disorder clinical prognosis bad prediction model and construction method thereof |
| CN114203255B (en) * | 2022-02-17 | 2022-06-14 | 北京泽桥医疗科技股份有限公司 | Traditional Chinese medicine anticancer key target prediction method based on machine learning |
| CN115346656B (en) * | 2022-06-10 | 2023-10-27 | 江门市中心医院 | Three-group IDC prognosis model building method and prognosis model system based on CAFs, WSIs and clinical information |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105586390A (en) * | 2014-10-23 | 2016-05-18 | 王辉云 | Reagent for prognosis of breast cancer |
| CN107885973A (en) * | 2017-11-20 | 2018-04-06 | 沈阳柏敖生信生物科技有限公司 | A kind of clinical research methods of DNA-repair gene in colorectal cancer |
| CN108424969A (en) * | 2018-06-06 | 2018-08-21 | 深圳市颐康生物科技有限公司 | A kind of biomarker, the method for diagnosing or estimating mortality risk |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012031008A2 (en) * | 2010-08-31 | 2012-03-08 | The General Hospital Corporation | Cancer-related biological materials in microvesicles |
| EP3202913B1 (en) * | 2016-02-08 | 2019-01-30 | King Faisal Specialist Hospital And Research Centre | A set of genes for use in a method of predicting the likelihood of a breast cancer patient's survival |
-
2019
- 2019-02-14 CN CN201910114297.7A patent/CN111564214B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105586390A (en) * | 2014-10-23 | 2016-05-18 | 王辉云 | Reagent for prognosis of breast cancer |
| CN107885973A (en) * | 2017-11-20 | 2018-04-06 | 沈阳柏敖生信生物科技有限公司 | A kind of clinical research methods of DNA-repair gene in colorectal cancer |
| CN108424969A (en) * | 2018-06-06 | 2018-08-21 | 深圳市颐康生物科技有限公司 | A kind of biomarker, the method for diagnosing or estimating mortality risk |
Non-Patent Citations (1)
| Title |
|---|
| 阿波斯托利亚-玛蒂亚·钦巴瑞多 等.《癌症转化医学研究中的靶向治疗》.上海科学技术出版社,2017,第430-431页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111564214A (en) | 2020-08-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111564214B (en) | Method for establishing and verifying breast cancer prognosis evaluation model based on 7 special genes | |
| CN109859801B (en) | Model for predicting lung squamous carcinoma prognosis by using seven genes as biomarkers and establishing method | |
| Győrffy et al. | Multigene prognostic tests in breast cancer: past, present, future | |
| Brooks et al. | ClearCode34: a prognostic risk predictor for localized clear cell renal cell carcinoma | |
| CN105986034A (en) | Application of group of gastric cancer genes | |
| CN110004226B (en) | Method for predicting prognosis based on rectal cancer transcriptome gene and methylation combined analysis and model application | |
| CN113355419B (en) | Breast cancer prognosis risk prediction marker composition and application | |
| KR102170726B1 (en) | Method for selecting biomarker and method for providing information for diagnosis of cancer using thereof | |
| CN113192560A (en) | Construction method of hepatocellular carcinoma typing system based on iron death process | |
| Shang et al. | Identification of lung adenocarcinoma specific dysregulated genes with diagnostic and prognostic value across 27 TCGA cancer types | |
| CN113430266A (en) | Application of G6PC and genome thereof in preparation of renal clear cell carcinoma diagnosis or prognosis evaluation system | |
| Zhu et al. | Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study | |
| He et al. | A novel RNA sequencing-based risk score model to predict papillary thyroid carcinoma recurrence | |
| Qi et al. | An immune-related lncRNA signature for the prognosis of pancreatic adenocarcinoma | |
| Dong et al. | Development of a prognostic signature based on immune-related genes and the correlation with immune microenvironment in breast cancer | |
| Wang et al. | Single cell sequencing analysis and transcriptome analysis constructed the liquid-liquid phase separation (LLPS)-related prognostic model for endometrial cancer | |
| Guo et al. | A novel promoter CpG-based signature for long-term survival prediction of breast cancer patients | |
| Munkácsy et al. | Gene expression-based prognostic and predictive tools in breast cancer | |
| CN110408706A (en) | It is a kind of assess recurrent nasopharyngeal carcinoma biomarker and its application | |
| CN115798703A (en) | Apparatus and computer-readable storage medium for predicting prognosis of renal clear cell carcinoma based on novel fatty acid metabolism-related gene | |
| CN116469552A (en) | Method and system for breast cancer polygene genetic risk assessment | |
| CN109880905B (en) | Genes for immunohistochemical typing of triple negative breast cancer and application thereof | |
| Wang et al. | Construction and validation of a novel prognostic model using the cellular senescence-associated long non-coding RNA in gastric cancer: a biological analysis | |
| US20120329060A1 (en) | Diagnostic for lung cancer using mirna | |
| CN114678062B (en) | Hepatocellular carcinoma prognosis prediction system based on multiple sets of chemical characteristics and prediction method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |