Cancer Research
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Chapters
Summary 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.
Transcript
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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