Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
May 17th, 2019
Here, we present a protocol to explore the biomarker and survival predictor of breast cancer based on the comprehensive analysis of pooled clinical datasets derived from a variety of publicly accessible databases, using the strategy of expression, correlation and survival analysis step by step.
Here, we present a protocol to explore the biomarker and survival predictor of breast cancer basis on the analysis of a variety of public accessible databases. This method can improve the credibility and representativeness of the conclusions, thereby present informative perspective on a gene of interest. The advantage of this method is that it allows for the rapid visualization and interpretation of a gene's potential role in breast cancer.
Moreover, all the results obtained through this procedure can be immediately tested and repeated by simply querying the corresponding websites. We chose breast cancer in the study to test the feasibility of this method. Because breast cancer is a heterogeneous disease the diagnosis, treatment and prognosis of different molecular subtypes of breast cancer may vary.
Therefore it is particularly important to find efficient online tool or database with information to reflect the disparate subtypes of breast cancer. To begin this procedure go to the ONCOMINE web interface. Type ID1 into the search box to obtain the relative expression levels of gene ID1 in various types of malignancies.
In the primary filters menu select analysis type, then select Cancer vs. Normal Analysis. In the other views menu select gene summary view.
Set the threshold of P-value at 0.01. Set the threshold of fold change to 2. Set the threshold of gene rank to All.
Set the data type to All. Download the figures. First, go to the BC Gene-Expression Miner web interface.
In the analysis menu, select correlation and then press the exhaustive button. Type ID1 into the search box and press the submit button and the start Analysis button. For subgroup analysis in the BC Gene-Expression Miner, go to the BC Gene-Expression Miner interface.
In the analysis menu, select expression and press the exhaustive button. Type ID1 into the search box and press the submit button and the start analysis button. Click the nodal status and Scarff Bloom Richardson grade status thumbnails to view full images.
In the Scarff Bloom Richardson images, press the button below to visualize the P-values of the figures. Then, download the figures. For subgroup analysis via Gene expression-based Outcome for Breast Cancer Online, go to the GOBO web interface.
Type ID1 for the gene symbol of interest and upload the gene set. Set the search range for define gene/probe identifiers to gene symbol. Set all in tumor selection, and select node status and grade stratified in the multivariate parameters.
The other items remain default. Now, submit the inquiry and download the figures. For survival analysis in the BC Gene-Expression Miner, go to the BC Gene-Expression Miner web interface.
In the analysis menu, select prognostic and then press the exhaustive button. Type ID1 into the search box and press the submit button and then the start analysis button. In the exhaustive prognostic analysis, select Nm, ERm, MR in the population and event criteria, and press the submit button to obtain more information.
After this click the Kaplan-Meier curve thumbnails to export the full graphs. For survival analysis in the Human Protein Atlas, go to the Human Protein Atlas web interface. Type ID1 to the search box and click the search button.
Next, select the pathology sub-atlas. Click the label for breast cancer and a detailed page will appear showing an interactive survival scatter plot and survival analysis. Download these figures.
For survival analysis in the Kaplan-Meier Plotter Survival, go to the Kaplan-Meier Plotter web interface. In the mRNA gene chip zone, click start KM plotter for breast cancer. Type ID1 into the search bar and select the green item in the candidate menu.
Then, select RFS as the survival type and leave the other items at their default settings. Click draw Kaplan-Meier plot and download the figures. In this study, a representative result for the data mining and integrative analysis of a breast cancer biomarker is performed using ID1, one of the inhibitor's DNA-binding family members.
The differences of ID1 mRNA expression between tumor and normal tissues are first analyzed using the ONCOMINE database, which contains a total of 445 unique analyses. There are only 5 studies revealing an mRNA expression level of ID1 that is significantly higher in normal tissues than in breast cancer tissues, which indicates that there is an expression dysregulation of ID1 in breast cancer. The BC Gene-Expression Miner is then used to identify the correlation between mRNA expression of ID1 and the clinicopathological parameters of breast cancer patients.
The mRNA levels of ID1 are seen to be significantly increased in breast cancer patients without lymph node metastasis compared to those with lymph node metastasis. The analysis in GOBO demonstrates that increased mRNA levels of ID1 correlate to lower tumor grade. These results imply that increased expression of ID1 is linked to lower metastatic potential and lower pathological grade in breast cancer.
The analysis from the BC Gene-Expression Miner database indicates that higher mRNA levels of ID1 is correlated to longer distant metastasis-free survival in breast cancer patients. The analysis from the Human Protein Atlas also suggests that elevated protein levels of ID1 is associated with better survival outcome in breast cancer patients. The survival analysis from the Kaplan-Meier Plotter is consistent with these findings, and shows that higher mRNA levels of ID1 expression predicts better recurrence-free survival in breast cancer patients.
More and more online databases will be availables or accessible for researchers. The protocol might provide an efficient method for researchers to identify potential target genes and associated signaling pathway.
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Overview
This article presents a protocol for exploring biomarkers and survival predictors in breast cancer through a comprehensive analysis of publicly accessible clinical datasets. The method enhances the credibility of findings and allows for rapid visualization of gene roles in breast cancer.
Key Study Components
Area of Science
- Breast cancer research
- Biomarker analysis
- Survival prediction
Background
- Breast cancer is a heterogeneous disease.
- Diagnosis, treatment, and prognosis vary among molecular subtypes.
- Public databases provide valuable clinical data.
- Understanding gene roles can inform treatment strategies.
Purpose of Study
- To develop a protocol for analyzing breast cancer biomarkers.
- To assess survival predictors based on gene expression.
- To improve the representativeness of conclusions drawn from data.
Methods Used
- Comprehensive analysis of pooled clinical datasets.
- Expression, correlation, and survival analysis techniques.
- Visualization of gene roles in breast cancer.
- Reproducibility through querying public databases.
Main Results
- Identification of potential biomarkers for breast cancer.
- Insights into survival predictors based on gene expression.
- Demonstration of the method's feasibility using breast cancer data.
- Enhanced understanding of molecular subtype variations.
Conclusions
- The protocol offers a systematic approach to biomarker analysis.
- Results can be validated through public database queries.
- This method can inform future breast cancer research and treatment.
Frequently Asked Questions
Executive Industry Relevance
Integrating multidimensional genomic data from public databases enables rapid hypothesis testing and biomarker validation in breast cancer research. This approach improves the credibility and representativeness of conclusions by leveraging pooled clinical datasets across multiple platforms. It supports early discovery workflows by providing accessible, reproducible insights into gene expression patterns and survival associations.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses through expression pattern analysis of biomarkers like ID1 in cancerous versus normal tissues.
- Scientific Value: Supports biological de-risking by correlating biomarker expression with clinicopathological characteristics such as lymph node metastasis and tumor grade.
- Scientific Value: Enhances target confidence by identifying survival associations across independent datasets, reinforcing predictive value of biomarker candidates.
Screening & Assay Development
- Operational Value: Prepares validated biological systems for downstream workflows by establishing expression baselines and subgroup-specific profiles using standardized query thresholds.
- Operational Value: Promotes assay standardization and reproducibility through consistent application of P-value, fold change, and gene rank filters across databases.
- Operational Value: Enables screening readiness by generating quantitative outputs (e.g., expression levels, survival curves) that can be immediately re-tested via public web interfaces.
Translational & Preclinical Research
- Translational Value: Supports disease-relevant system modeling by stratifying biomarker expression across molecular subtypes and clinical subgroups relevant to breast cancer heterogeneity.
- Translational Value: Facilitates mechanistic de-risking by linking ID1 expression to lower metastatic potential and improved survival outcomes, informing preclinical prioritization.
- Translational Value: Strengthens translational continuity by demonstrating consistent survival associations across independent platforms (bcGenExMiner, HPA, Kaplan-Meier Plotter).
Pipeline & Workflow Integration
The method integrates into the discovery continuum from target validation through preclinical evaluation by delivering reproducible, multi-source evidence on biomarker behavior in breast cancer.
- Discovery Biology: Supports hypothesis testing and pathway clarification by analyzing expression dysregulation and clinical correlations of genes like ID1 across cancer and normal samples.
- Screening: Delivers assay-ready quantitative outputs such as fold-change thresholds and survival hazard ratios that enable reliable compound or genetic perturbation evaluation.
- Analytics: Provides standardized measurements including expression levels, correlation coefficients, P-values, and Kaplan-Meier survival estimates for cross-condition comparison.
- Translational Research: Connects discovery findings to preclinical continuity by validating biomarker-survival links across independent clinical datasets.
- Enterprise Reuse: Establishes a reusable capability for biomarker assessment across cancer types, leveraging publicly accessible databases without proprietary infrastructure.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in biomarker candidates through cross-database validation and reduction of mechanistic ambiguity.
- Operational Value: Ensures reproducibility and standardization via repeatable web-based queries with defined analytical thresholds.
- Strategic Value: Improves go/no-go decisions by providing representative, multi-cohort evidence on biomarker associations with survival and pathology.
- Portfolio Impact: Enables risk-adjusted prioritization of targets by integrating expression, correlation, and survival data to assess biological and clinical relevance.
Implementation Considerations
- Requires expertise in genomic data interpretation and familiarity with public cancer database interfaces.
- Dependent on stable access to online platforms such as ONCOMINE, bcGenExMiner, GOBO, HPA, and Kaplan-Meier Plotter.
- Necessitates cross-team standardization of query parameters (e.g., P-value thresholds, fold-change cutoffs) to ensure consistent results.
- Involves adaptation considerations when applying the protocol to different cancer types or biomarker genes due to database-specific data availability.
- Limited by the scope and annotation quality of publicly accessible datasets, which may affect completeness of subgroup or survival analyses.
