This work was partly supported by the Natural Science Foundation of Guangdong Province, China (No. 2018A030313562), the Teaching Reform Project of Guangdong Clinical Teaching Base (NO.&#160; 2016JDB092), National Natural Science Foundation of China (81600358), and Youth Innovative Talent Project of Colleges and Universities in Guangdong Province, China (NO. 2017KQNCX073)

1. Expression Pattern Analysis
<ol>
	<li>Go to the ONCOMINE web interface26.</li>
	<li>Obtain the relative expression levels of gene ID1 in various types of malignancies by typing ID1 to the Search Box.</li>
	<li>Select Analysis Type from the Primary Filters menu. Then, select Cancer vs. Normal Analysis, Breast Cancer vs. Normal Analysis.</li>
	<li>Select Gene Summary View from the OTHER VIEWS menu. Set the threshold of P-value at 0.01. Download the figures. 
	NOTE: The threshold of fold change is 2, as described in the previous study27.</li>
</ol>
2. Expression Correlation Analysis
<ol>
	<li>Go to the bc-GenExMiner v4.0 web interface28.</li>
	<li>Select CORRELATION from the ANALYSIS menu, press the EXHAUSTIVE button. Type ID1 to the search box. Press the Submit button and the Start analysis button. 
	NOTE: Default setting show expression correlation analysis of all patients, which can be more accurate in different subtypes of breast cancer by pressing the Molecule subtype filter.</li>
</ol>
3. Subgroup Analysis
<ol>
	<li>Subgroup analysis in bc-GenExMiner v4.0
	<ol>
		<li>Go to the bc-GenExMiner v4.0 web interface28.</li>
		<li>Select EXPRESSION from the ANALYSIS menu, press the EXHAUSTIVE button. Type ID1 to the search box and press the Submit button and the Start analysis button.</li>
		<li>Click the Nodal status (LN) and Scarff Bloom &#38; Richardson grade status (SBR) thumbnails to view full images. In the SBR images, press the button below to visualize the P-values of the figures. Download the figures.</li>
	</ol>
	</li>
	<li>Subgroup analysis in Gene expression-based Outcome for Breast Cancer Online (GOBO)
	<ol>
		<li>Go to the GOBO web interface14.</li>
		<li>Type Gene symbol of interest ID1 to the screen upload the gene set.</li>
		<li>Set the search range of Define gene/probe identifiers to Gene Symbol. Set All in Tumor selection. Select Node status and Grade stratified in the Multivariate parameters. Other items remain default. Submit the inquiry and download the figures.</li>
	</ol>
	</li>
</ol>
4. Survival Analysis
<ol>
	<li>Survival analysis in bc-GenExMiner v4.0
	<ol>
		<li>Go to the bc-GenExMiner v4.0 web interface28.</li>
		<li>Select PROGNOSTIC from the ANALYSIS menu, press the EXHAUSTIVE button. Type ID1 to the search box and press the Submit button and the Start analysis button.</li>
		<li>In the Exhaustive prognostic analysis, select Nm, ERm, MR in the Population and event criteria and press the Submit button to obtain more information. Press the Kaplan-Meier curve thumbnails to export the full graphs. 
		NOTE: N (+, -, m): nodal status (+: positive, -: negative, m: mixed); ER (+, -, m): oestrogen receptor status (+: positive, -: negative, m: mixed); MR: metastatic relapse</li>
	</ol>
	</li>
	<li>Survival analysis in The Human Protein Atlas (HPA)
	<ol>
		<li>Go to the Human Protein Atlas web interface29.</li>
		<li>Type ID1 to the search box and click the Search button. Select Pathology sub-atlas. 
		NOTE: The mRNA expression levels across the 17 cancer types are shown in the RNA Expression overview section. Every cancer tissue label of the box plot is clickable to access a detailed page providing survival analysis data and RNA expression levels.</li>
		<li>Click the label of Breast Cancer, then the detailed page to show interactive survival scatter plot and survival analysis. Download the figures.</li>
	</ol>
	</li>
	<li>Survival analysis in The Kaplan-Meier Plotter Survival
	<ol>
		<li>Go to the Kaplan-Meier Plotter web interface30. Click Start KM plotter for breast cancer in the mRNA gene chip zone.</li>
		<li>Type ID1 to the search bar and select the green item in the candidate menu.</li>
		<li>Select RFS as survival type and Other items remain default. Click Draw Kaplan-Meier plot and download the figures. 
		NOTE: Settings of the survival types, cutoff types, and follow-up threshold, as well as probe set options, can be changed as required. Subgroup prognostic analysis including ER, PR, HER-2, lymph nodes, grade, Tp53 status, and molecular subtypes can be obtained via changing the setting in the Restrict analysis to subtypes box1. Likewise, the filter limitation of treatment could be set in Restrict analysis to selected cohorts&#8217; box.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>van &#39;t Veer, L. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+expression+profiling+predicts+clinical+outcome+of+breast+cancer.">Gene expression profiling predicts clinical outcome of breast cancer.</a> Nature. 415 (6871), 530-536 (2002).</li><li>Loi, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Definition+of+clinically+distinct+molecular+subtypes+in+estrogen+receptor-positive+breast+carcinomas+through+genomic+grade.">Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.</a> Journal of Clinical Oncology. 25 (10), 1239-1246 (2007).</li><li>Cancer Genome Atlas, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+molecular+portraits+of+human+breast+tumours.">Comprehensive molecular portraits of human breast tumours.</a> Nature. 490 (7418), 61-70 (2012).</li><li>Emerson, J. W., Dolled-Filhart, M., Harris, L., Rimm, D. L., Tuck, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+assessment+of+tissue+biomarkers+and+construction+of+a+model+to+predict+outcome+in+breast+cancer+using+multiple+imputation.">Quantitative assessment of tissue biomarkers and construction of a model to predict outcome in breast cancer using multiple imputation.</a> Cancer Informatics. 7, 29-40 (2009).</li><li>Yu, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+genomic+and+transcriptomic+analysis+for+pinpointing+recurrent+alterations+of+plant+homeodomain+genes+and+their+clinical+significance+in+breast+cancer.">Integrative genomic and transcriptomic analysis for pinpointing recurrent alterations of plant homeodomain genes and their clinical significance in breast cancer.</a> Oncotarget. 8 (8), 13099-13115 (2017).</li><li>He, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TCGA+datasetbased+construction+and+integrated+analysis+of+aberrantly+expressed+long+noncoding+RNA+mediated+competing+endogenous+RNA+network+in+gastric+cancer.">TCGA datasetbased construction and integrated analysis of aberrantly expressed long noncoding RNA mediated competing endogenous RNA network in gastric cancer.</a> Oncology Reports. , (2018).</li><li>Liu, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+Integrated+TCGA+Pan-Cancer+Clinical+Data+Resource+to+Drive+High-Quality+Survival+Outcome+Analytics.">An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.</a> Cell. 173 (2), 400-416 (2018).</li><li>Esgueva, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+prostate+cancer+biobanking:+toward+a+processing+protocol+amenable+for+the+International+Cancer+Genome+Consortium.">Next-generation prostate cancer biobanking: toward a processing protocol amenable for the International Cancer Genome Consortium.</a> Diagnostic Molecular Pathology. 21 (2), 61-68 (2012).</li><li>Joly, Y., Dove, E. S., Knoppers, B. M., Bobrow, M., Chalmers, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+sharing+in+the+post-genomic+world:+the+experience+of+the+International+Cancer+Genome+Consortium+(ICGC)+Data+Access+Compliance+Office+(DACO).">Data sharing in the post-genomic world: the experience of the International Cancer Genome Consortium (ICGC) Data Access Compliance Office (DACO).</a> PLoS Computational Biology. 8 (7), e1002549 (2012).</li><li>Zhang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=International+Cancer+Genome+Consortium+Data+Portal--a+one-stop+shop+for+cancer+genomics+data.">International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data.</a> Database (Oxford). 2011, (2011).</li><li>Rhodes, D. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ONCOMINE:+a+cancer+microarray+database+and+integrated+data-mining+platform.">ONCOMINE: a cancer microarray database and integrated data-mining platform.</a> Neoplasia. 6 (1), 1-6 (2004).</li><li>Rhodes, D. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oncomine+3.0:+genes,+pathways,+and+networks+in+a+collection+of+18,000+cancer+gene+expression+profiles.">Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles.</a> Neoplasia. 9 (2), 166-180 (2007).</li><li>Jezequel, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=bc-GenExMiner:+an+easy-to-use+online+platform+for+gene+prognostic+analyses+in+breast+cancer.">bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer.</a> Breast Cancer Research and Treatment. 131 (3), 765-775 (2012).</li><li>Ringner, M., Fredlund, E., Hakkinen, J., Borg, A., Staaf, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GOBO:+gene+expression-based+outcome+for+breast+cancer+online.">GOBO: gene expression-based outcome for breast cancer online.</a> PLoS One. 6 (3), e17911 (2011).</li><li>Ponten, F., Jirstrom, K., Uhlen, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Human+Protein+Atlas--a+tool+for+pathology.">The Human Protein Atlas--a tool for pathology.</a> Journal of Pathology. 216 (4), 387-393 (2008).</li><li>Ponten, F., Schwenk, J. M., Asplund, A., Edqvist, P. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Human+Protein+Atlas+as+a+proteomic+resource+for+biomarker+discovery.">The Human Protein Atlas as a proteomic resource for biomarker discovery.</a> Journal of Internal Medicine. 270 (5), 428-446 (2011).</li><li>Gyorffy, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+online+survival+analysis+tool+to+rapidly+assess+the+effect+of+22,277+genes+on+breast+cancer+prognosis+using+microarray+data+of+1,809+patients.">An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients.</a> Breast Cancer Research and Treatment. 123 (3), 725-731 (2010).</li><li>Stevinson, C., Lawlor, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Searching+multiple+databases+for+systematic+reviews:+added+value+or+diminishing+returns?.">Searching multiple databases for systematic reviews: added value or diminishing returns?.</a> Complementary Therapies in Medicine. 12 (4), 228-232 (2004).</li><li>Yin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+multiple+genome+annotation+databases+improves+the+interpretation+of+microarray+gene+expression+data.">Integrating multiple genome annotation databases improves the interpretation of microarray gene expression data.</a> BMC Genomics. 11, 50 (2010).</li><li>Patel, D., Morton, D. J., Carey, J., Havrda, M. C., Chaudhary, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibitor+of+differentiation+4+(ID4):+From+development+to+cancer.">Inhibitor of differentiation 4 (ID4): From development to cancer.</a> Biochimica et Biophysica Acta. 1855 (1), 92-103 (2015).</li><li>Kamalian, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increased+expression+of+Id+family+proteins+in+small+cell+lung+cancer+and+its+prognostic+significance.">Increased expression of Id family proteins in small cell lung cancer and its prognostic significance.</a> Clinical Cancer Research. 14 (8), 2318-2325 (2008).</li><li>Cruz-Rodriguez, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+expression+of+ID+family+and+IGJ+genes+signature+as+predictor+of+low+induction+treatment+response+and+worst+survival+in+adult+Hispanic+patients+with+B-acute+lymphoblastic+leukemia.">High expression of ID family and IGJ genes signature as predictor of low induction treatment response and worst survival in adult Hispanic patients with B-acute lymphoblastic leukemia.</a> Journal of Experimental and Clinical Cancer Research. 35, 64 (2016).</li><li>Yang, H. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expression+and+prognostic+value+of+Id+protein+family+in+human+breast+carcinoma.">Expression and prognostic value of Id protein family in human breast carcinoma.</a> Oncology Reports. 23 (2), 321-328 (2010).</li><li>Zhou, X. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prognostic+values+of+the+inhibitor+of+DNAbinding+family+members+in+breast+cancer.">Prognostic values of the inhibitor of DNAbinding family members in breast cancer.</a> Oncology Reports. 40 (4), 1897-1906 (2018).</li><li>. Available from: <a target="_blank" href="https://www.oncomine.org">https://www.oncomine.org</a> (2018)</li><li>Lin, H. Y., Zeng, L., iang, Y. K., Wei, X. L., Chen, C. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GATA3+and+TRPS1+are+distinct+biomarkers+and+prognostic+factors+in+breast+cancer:+database+mining+for+GATA+family+members+in+malignancies.">GATA3 and TRPS1 are distinct biomarkers and prognostic factors in breast cancer: database mining for GATA family members in malignancies.</a> Oncotarget. 8 (21), 34750-34761 (2017).</li><li>. Available from: <a target="_blank" href="https://www.proteinatlas.org">https://www.proteinatlas.org</a> (2018)</li><li>Zhu, Y. F., Dong, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expression+of+TUSC3+and+its+prognostic+significance+in+colorectal+cancer.">Expression of TUSC3 and its prognostic significance in colorectal cancer.</a> Pathology-Research and Practice. 214 (9), 1497-1503 (2018).</li><li>Nelson, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+sampling+can+reduce+bias+in+health+care+database+studies:+an+illustration+using+influenza+vaccination+effectiveness.">Validation sampling can reduce bias in health care database studies: an illustration using influenza vaccination effectiveness.</a> Journal of Clinical Epidemiology. 66 (8 Suppl), S110-S121 (2013).</li><li>Haibe-Kains, B., Desmedt, C., Sotiriou, C., Bontempi, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+study+of+survival+models+for+breast+cancer+prognostication+based+on+microarray+data:+does+a+single+gene+beat+them+all?.">A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?.</a> Bioinformatics. 24 (19), 2200-2208 (2008).</li><li>Yang, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+genetic+toxicity+through+data+mining:+the+process+of+building+knowledge+by+integrating+multiple+genetic+toxicity+databases.">Understanding genetic toxicity through data mining: the process of building knowledge by integrating multiple genetic toxicity databases.</a> Toxicology Mechanisms and Methods. 18 (2-3), 277-295 (2008).</li><li>Cannata, N., Merelli, E., Altman, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time+to+organize+the+bioinformatics+resourceome.">Time to organize the bioinformatics resourceome.</a> PLoS Computational Biology. 1 (7), e76 (2005).</li><li>Wren, J. D., Bateman, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Databases,+data+tombs+and+dust+in+the+wind.">Databases, data tombs and dust in the wind.</a> Bioinformatics. 24 (19), 2127-2128 (2008).</li></ol>

The authors have nothing to disclose

Comprehensive analysis of public databases may indicate the underlying function of the gene of interest and reveal the potential link between this gene and clinicopathological parameters in specific cancer27,31. The exploration and analysis based on one single database might provide limited or isolated perspectives due to the potential selection bias, or in a certain extent, possibly due to the variety of data quality, including data collection and the analytical...

Breast cancer is a heterogeneous disease with diverse prognosis and treatment strategies in different molecular subtypes, in which the pathogenesis and development are probably associated with disparate molecular mechanisms1,2,3. However, identifying a therapeutic target usually takes years, or even decades, from initial discovery in basic research to clinical use4. Genome wide application of high-throughput sequencing technology for cancer genome has greatly advanced the process of searching for valuable biomarkers or therapeutic targets 5.
The overwhelming amount of cancer genomics data generated from the large-scale cancer genomics platforms, such as the ICGC (International Cancer Genome Consortium) and TCGA (The Cancer Genome Atlas), is posing a great challenge for researchers to perform data exploration, integration, and analytics, particularly for users lacking intensive training in informatics and computation6,7,8,9,10. In recent years, emerging databases, (e.g., ONCOMINE, bcGenExMiner v4.0, and Kaplan-Meier plotter, etc.) were designed and developed to lower the bar for approaching the intricate cancer genomic datasets, thereby, facilitating investigators to analyze and interpret the genes, samples and clinical data across various types of cancer11. The goal of this protocol is to describe a research strategy that integrated with multiple levels of gene information from a series of open access databases, which have been widely recognized by a great number of researchers, to identify the potential biomarkers and prognostic factors for breast cancer.
The ONCOMINE database is a web-based data-mining platform with cancer microarray information and is designed to facilitate discovery of novel biomarkers and therapeutic targets11. Currently, there are more than 48 million gene expression measurements from 65 gene expression datasets in this database11,12. The bcGenExMiner v4.0 (a free tool for non-profit institution), also called breast cancer Gene-Expression Miner, is a user-friendly web-based application comprising DNA microarrays results of 3,414 recovered breast cancer patients and 1,209 experienced a pejorative event13. It is designed to improve gene prognostic analysis performance with R statistical software and packages.
The GOBO is a multifunctional user-friendly online tool with microarrays information (e.g., Affymetrix U133A) from a 51-sample breast cancer cell line set and an 1881-sample breast tumor data set, that allows a wide array of analyses14. There are a variety of applications available in the GOBO database, which include rapid analysis of gene expression profiles in different molecular subtypes of breast tumors and cell lines, screening for co-expressed genes for creation of potential metagenes, and correlation analysis between outcome and gene expression levels of single genes, sets of genes, or gene signatures in breast cancer data set15.
The Human Protein Atlas is an open-access program designed for scientists to explore human proteome, which has already contributed to a large number of publications in the field of human biology and disease. The Human Protein Atlas is recognized as a European core resource for life science community16,17.
The Kaplan Meier plotter is an online tool integrating gene expression and clinical data simultaneously that allows assessment of the prognostic effect of&#160;54,675 genes&#160;based on&#160;10,461 cancer samples, which include&#160;1,065 gastric,&#160;2,437 lung,&#160;1,816 ovarian and 5,143 breast cancer patients&#160;with a mean follow-up of 33/49/40/69 months18. Information of gene expression,&#160;relapse-free survival (RFS) and overall survival (OS) are downloadable from this database19,20.
Here, we describe a practical operation procedure of using multiple publicly accessible databases to compare, analyze and visualize patterns of alterations in the expression of the gene of interest across multiple cancer studies, with the goal of summarizing the expression profiles, prognostic values and potential biological functions in breast cancer. For example, recent studies have indicated the oncogenic properties of ID proteins in tumors and were associated with malignant features, including cellular transformation, immortalization, enhanced proliferation and metastasis21,22,23. However, each member of the ID family plays distinct roles in different types of solid tumors, and their role in breast cancer remains unclear24. In previous studies, explored through this method, we found that ID1 was a meaningful prognostic indicator in breast cancer25. Therefore, the protocol will take ID1 as an example to introduce the data mining methods.
The analysis starts from querying the expression pattern of the gene of interest in cancerous samples vs. normal samples in ONCOMINE. Then, the expression correlation of genes of interest in breast cancer was performed using the bc-GenExMiner v4.0, GOBO, and ONCOMINE. Next, the expression profiles of ID1 was stratified according to different subgroups using the above three databases. Finally, the association between ID1 expression and survival out was analyzed using bc-GenExMiner v4.0, the human protein atlas, and Kaplan-Meier plotter. The operation procedure was shown as the flowchart in Figure 1.

<table>
	<tbody>
		<tr>
			<td>A personal computer or computing device with an Internet browser with Javascript 
			enabled</td>
			<td>Microsoft</td>
			<td>051690762553</td>
			<td>We support and test the following browsers: Google Chrome, Firefox 3.0 and above, Safari, and Internet Explorer 9.0 and above</td>
		</tr>
		<tr>
			<td>Adobe Flash player</td>
			<td>Adobe Systems Inc.</td>
			<td>It can be freely downloaded from http://get.adobe.com/flashplayer/.</td>
			<td>This browser plug-in is required for visualizing networks on the network 
			analysis tab.</td>
		</tr>
		<tr>
			<td>Chrome Broswer</td>
			<td>Google Inc.</td>
			<td>It can be freely downloaded from https://www.google.cn/chrome/</td>
			<td>This is necessary for viewing PDF files including the Pathology Reports and many of 
			the downloadable files.</td>
		</tr>
		<tr>
			<td>Java Runtime Environment</td>
			<td>Oracle Corporation</td>
			<td>It can be downloaded from http://www.java.com/getjava/.</td>
			<td></td>
		</tr>
		<tr>
			<td>Office 365 ProPlus for Faculty</td>
			<td>Microsoft</td>
			<td>2003BFFD8117EA68</td>
			<td>This is necessary for viewing the Pathology Reports and for viewing many of 
			the downloadable files.</td>
		</tr>
		<tr>
			<td>Vectr Online</td>
			<td>Vectr Labs Inc.</td>
			<td>It can be freely used from https://vectr.com/new</td>
			<td>This is necessary for visualizing and editing many of 
			the downloadable files and pictures.</td>
		</tr>
	</tbody>
</table>

performing data mining and integrative analysis of biomarker in breast cancer using multiple publicly accessible databases

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating investigators to analyze and interpret genes, samples and clinical data across different types of cancer. Herein, we describe a practical operation procedure, taking ID1 (Inhibitor of DNA binding proteins 1) as an example, to characterize the expression patterns of biomarker and survival predictors of breast cancer based on pooled clinical datasets derived from online accessible databases, including ONCOMINE, bcGenExMiner v4.0 (Breast cancer gene-expression miner v4.0), GOBO (Gene expression-based Outcome for Breast cancer Online), HPA (The human protein atlas), and Kaplan-Meier plotter. The analysis began with querying the expression pattern of the gene of interest (e.g., ID1) in cancerous samples vs. normal samples. Then, the correlation analysis between ID1 and clinicopathological characteristics in breast cancer was performed. Next, the expression profiles of ID1 was stratified according to different subgroups. Finally, the association between ID1 expression and survival outcome was analyzed. The operation procedure simplifies the concept to integrate multidimensional data types at the gene level from different databases and test hypotheses regarding recurrence and genomic context of gene alteration events in breast cancer. This method can improve the credibility and representativeness of the conclusions, thereby, present informative perspective on a gene of interest.

A representative result of data mining and integrative analysis of breast cancer biomarker was performed using ID1, one of the inhibitors of DNA-binding family members, which have been reported in the previous study 25.
As demonstrated in Figure 2, the differences of ID1 mRNA expression between tumor and normal tissues in multiple types of cancer were analyzed using the ONCOM...

Watch this Scientific Journal Video about Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases at JoVE.com

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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.

In recent years, emerging databases were designed to lower the barriers for approaching the intricate cancer genomic datasets, thereby, facilitating ...

performing-data-mining-integrative-analysis-biomarker-breast-cancer

Research

JoVE Journal

Cancer Research

8.8K Views.  First Affiliated Hospital of Shantou University Medical College. 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.

Video: Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery of the next generation of novel drug targets. It is becoming apparent that more complex models of cancer are required to fully appreciate the contributing factors that drive tumorigenesis in vivo and increase the efficacy of novel therapies that make the transition from pre-clinical models to clinical trials.
Here we present a methodology for generating uniform and reproducible tumor spheroids that can be subjected to siRNA functional screening. These spheroids display many characteristics that are found in solid tumors that are not present in traditional two-dimension culture. We show that several commonly used breast cancer cell lines are amenable to this protocol. Furthermore, we provide proof-of-principle data utilizing the breast cancer cell line BT474, confirming their dependency on amplification of the epidermal growth factor receptor HER2 and mutation of phosphatidylinositol-4,5-biphosphate 3-kinase (PIK3CA) when grown as tumor spheroids. Finally, we are able to further investigate and confirm the spatial impact of these dependencies using immunohistochemistry.

Identifying novel drug targets that transition from pre-clinical testing to human trials is a scientific priority. To that end, here we describe a functional genomics approach for examining the impact of gene depletion on cancer cell line spheroids, which more appropriately model human cancers in vivo.

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery ...

Ultrasonographic Evaluation of Breast Cancer-related Lymphedema

Lymphedema is one of the most common complications after breast cancer surgery. There are many diagnostic tools for lymphedema, but no standard method yet exists. Progressive Resistance Exercise (PRE) is expected to improve lymphedema without additional swelling. This study showed the therapeutic effects of PRE on lymphedema by using ultrasonography to measure the change in thickness of the muscle and subcutaneous tissue. The thickness of subcutaneous tissue decreased more in the PRE group than in the non-PRE group. Ultrasonography is widely used in many clinics because of its easy accessibility, safety, and inexpensiveness. Ultrasound is one of the best tools for diagnosing and determining treatment efficacy on breast cancer-related lymphedema (BCRL).

This study utilized ultrasonography for diagnosis and follow-up tests to confirm treatment efficacy of Progressive Resistance Exercise (PRE) in breast cancer-related lymphedema (BCRL). Ultrasonography can be applied effectively to ensure diagnosis and treatment of lymphedema.

Lymphedema is one of the most common complications after breast cancer surgery. There are many diagnostic tools for lymphedema, but no standard method yet ...

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.

Existing algorithms generate one solution for a biomarker detection dataset. This protocol demonstrates the existence of multiple similarly effective solutions and presents a user-friendly software to help biomedical researchers investigate their datasets for the proposed challenge. Computer scientists may also provide this feature in their biomarker detection algorithms.

Biomarker detection is one of the more important biomedical questions for high-throughput &#39;omics&#39; researchers, and almost all existing biomarker ...

Phospho Flow Cytometry with Fluorescent Cell Barcoding for Single Cell Signaling Analysis and Biomarker Discovery

Aberrant cell signaling plays a central role in cancer development and progression. Most novel targeted therapies are indeed directed at proteins and protein functions, and cell signaling aberrations may therefore serve as biomarkers to indicate personalized treatment options. As opposed to DNA and RNA analyses, changes in protein activity can more efficiently evaluate the mechanisms underlying drug sensitivity and resistance. Phospho flow cytometry is a powerful technique that measures protein phosphorylation events at the cellular level, an important feature that distinguishes this method from other antibody-based approaches. The method allows for simultaneous analysis of multiple signaling proteins. In combination with fluorescent cell barcoding, larger medium- to high-throughput data-sets can be acquired by standard cytometer hardware in short time. Phospho flow cytometry has applications both in studies of basic biology and in clinical research, including signaling analysis, biomarker discovery and assessment of pharmacodynamics. Here, a detailed experimental protocol is provided for phospho flow analysis of purified peripheral blood mononuclear cells, using chronic lymphocytic leukemia cells as an example.

Here, a protocol for medium- to high-throughput analysis of protein phosphorylation events at the cellular level is presented. Phospho flow cytometry is a powerful approach to characterize signaling aberrations, identify and validate biomarkers, and assess pharmacodynamics.

Aberrant cell signaling plays a central role in cancer development and progression. Most novel targeted therapies are indeed directed at proteins and ...

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less aggressive breast tumors, the 5-year survival rate of TNBC patients is 77% due to their characteristic drug-resistant phenotype and metastatic burden. Toward this end, murine models have been established aimed at identifying novel therapeutic strategies limiting TNBC tumor growth and metastatic spread. This work describes a practical guide for the TNBC orthotopic model where MDA-MB-231 breast cancer cells suspended in a basement membrane matrix are implanted in the fourth mammary fat pad, which closely mimics the cancer cell behavior in humans. Measurement of tumors by caliper, lung metastasis assessment via in vivo and ex vivo imaging, and molecular detection are discussed. This model provides an excellent platform to study therapeutic efficacy and is especially suitable for the study of the interaction between the primary tumor and distal metastatic sites.

This work presets an advanced protocol to accurately assess tumor loading by detection of green fluorescent protein and bioluminescence signals as well as the integration of quantitative molecular detection technique.

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited therapeutic options. When compared to patients with less ...

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Preclinical models that faithfully recapitulate tumor heterogeneity and therapeutic response are critical for translational breast cancer research. Immortalized cell lines are easy to grow and genetically modify to study molecular mechanisms, yet the selective pressure from cell culture often leads to genetic and epigenetic alterations over time. Patient-derived xenograft (PDX) models faithfully recapitulate the heterogeneity and drug response of human breast tumors. PDX models exhibit a relatively short latency after orthotopic transplantation that facilitates the investigation of breast tumor biology and drug response. The transplantable genetically engineered mouse models allow the study of breast tumor immunity. The current protocol describes the method to orthotopically transplant breast tumor fragments into the mammary fat pad followed by drug treatments. These preclinical models provide valuable approaches to investigate breast tumor biology, drug response, biomarker discovery and mechanisms of drug resistance.

Patient-derived xenograft (PDX) models and transplantable genetically engineered mouse models faithfully recapitulate human disease and are preferred models for basic and translational breast cancer research. Here, a method is described to orthotopically transplant breast tumor fragments into the mammary fat pad to study tumor biology and evaluate drug response.

Preclinical models that faithfully recapitulate tumor heterogeneity and therapeutic response are critical for translational breast cancer research. ...

Evaluating Cell Death Using Cell-Free Supernatant of Probiotics in Three-Dimensional Spheroid Cultures of Colorectal Cancer Cells

This manuscript describes a protocol to evaluate cancer cell deaths in three dimensional (3D) spheroids of multicellular types of cancer cells using supernatants from Lactobacillus fermentum cell culture, considered as probiotics cultures. The use of 3D cultures to test Lactobacillus cell-free supernatant (LCFS) are a better option than testing in 2D monolayers, especially as L. fermentum can produce anti-cancer effects within the gut. L. fermentum supernatant was identified to possess increased anti-proliferative effects against several colorectal cancer (CRC) cells in 3D culture conditions. Interestingly, these effects were strongly related to the culture model, demonstrating the notable ability of L. fermentum to induce cancer cell death. Stable spheroids were generated from diverse CRCs (colorectal cancer cells) using the protocol presented below. This protocol of generating 3D spheroid is time saving and cost effective. This system was developed to easily investigate the anti-cancer effects of LCFS in multiple types of CRC spheroids. As expected, CRC spheroids treated with LCFS strongly induced cell death during the experiment and expressed specific apoptosis molecular markers as analyzed by qRT-PCR, western blotting, and FACS analysis. Therefore, this method is valuable for exploring cell viability and evaluating the efficacy of anti-cancer drugs.

Here methods are presented to understand anti-cancer effects of Lactobacillus cell-free supernatant (LCFS). Colorectal cancer cell lines show cell deaths when treated with LCFS in 3D cultures. The process of generating spheroids can be optimized depending on the scaffold and the analysis methods presented are useful for evaluating the involved signaling pathways.

This manuscript describes a protocol to evaluate cancer cell deaths in three dimensional (3D) spheroids of multicellular types of cancer cells using ...

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Breast cancer is a devastating malignancy, accounting for 40,000 female deaths and 30% of new female cancer diagnoses in the United States in 2019 alone. The leading cause of breast cancer related deaths is the metastatic burden. Therefore, preclinical models for breast cancer need to analyze metastatic burden to be clinically relevant. The 4T1 breast cancer model provides a spontaneously-metastasizing, quantifiable mouse model for stage IV human breast cancer. However, most 4T1 protocols quantify the metastatic burden by manually counting stained colonies on tissue culture plates. While this is sufficient for tissues with lower metastatic burden, human error in manual counting causes inconsistent and variable results when plates are confluent and difficult to count. This method offers a computer-based solution to human counting error. Here, we evaluate the protocol using the lung, a highly metastatic tissue in the 4T1 model. Images of methylene blue-stained plates are acquired and uploaded for analysis in Fiji-ImageJ. Fiji-ImageJ then determines the percentage of the selected area of the image that is blue, representing the percentage of the plate with metastatic burden. This computer-based approach offers more consistent and expeditious results than manual counting or histopathological evaluation for highly metastatic tissues. The consistency of Fiji-ImageJ results depends on the quality of the image. Slight variations in results between images can occur, thus it is recommended that multiple images are taken and results averaged. Despite its minimal limitations, this method is an improvement to quantifying metastatic burden in the lung by offering consistent and rapid results.

We describe a more consistent and expeditious method to quantify lung metastasis in the 4T1 breast cancer model by using Fiji-ImageJ.

Breast cancer is a devastating malignancy, accounting for 40,000 female deaths and 30% of new female cancer diagnoses in the United States in 2019 alone. ...

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Breast cancer (BC) remains a leading cause of death for women. Despite more than $700 million invested in BC research annually, 97% of candidate BC drugs fail clinical trials. Therefore, new models are needed to improve our understanding of the disease. The NIH Microphysiological Systems (MPS) program was developed to improve the clinical translation of basic science discoveries and promising new therapeutic strategies. Here we present a method for generating MPS for breast cancers (BC-MPS). This model adapts a previously described approach of culturing primary human white adipose tissue (WAT) by sandwiching WAT between adipose-derived stem cell sheets (ASC)s. Novel aspects of our BC-MPS include seeding BC cells into non-diseased human breast tissue (HBT) containing native extracellular matrix, mature adipocytes, resident fibroblasts, and immune cells; and sandwiching the BC-HBT admixture between HBT-derived ASC sheets. The resulting BC-MPS is stable in culture ex vivo for at least 14 days. This model system contains multiple elements of the microenvironment that influence BC including adipocytes, stromal cells, immune cells, and the extracellular matrix. Thus BC-MPS can be used to study the interactions between BC and its microenvironment. 
We demonstrate the advantages of our BC-MPS by studying two BC behaviors known to influence cancer progression and metastasis: 1) BC motility and 2) BC-HBT metabolic crosstalk. While BC motility has previously been demonstrated using intravital imaging, BC-MPS allows for high-resolution time-lapse imaging using fluorescence microscopy over several days. Furthermore, while metabolic crosstalk was previously demonstrated using BC cells and murine pre-adipocytes differentiated into immature adipocytes, our BC-MPS model is the first system to demonstrate this crosstalk between primary human mammary adipocytes and BC cells in vitro.

This protocol describes the construction of an in vitro microphysiological system for studying breast cancer using primary human breast tissue with off the shelf materials.

Breast cancer (BC) remains a leading cause of death for women. Despite more than $700 million invested in BC research annually, 97% of candidate BC drugs ...

Monitoring Breast Cancer Growth and Metastatic Colony Formation in Mice using Bioluminescence

Breast cancer is a frequent heterogeneous malignancy and the second leading cause of mortality in women, mainly due to distant organ metastasis. Several animal models have been generated, including the widely used orthotopic mouse models, where cancer cells are injected into the mammary fat pad. However, these models cannot help monitor tumor growth kinetics and metastatic colonization. Cutting-edge tools to monitor cancer cells in real time in mice will significantly advance the understanding of tumor biology. 
Here, breast cancer cell lines stably expressing luciferase and green fluorescent protein (GFP) were established. Specifically, this technique contains two sequential steps initiated by measuring the luciferase activity in vitro and followed by the implantation of the cancer cells into mammary fat pads of nonobese diabetic-severe combined immunodeficiency (NOD-SCID) mice. After the injection, both the tumor growth and metastatic colonization are monitored in real time by the noninvasive bioluminescence imaging system. Then, the quantification of GFP-expressing metastases in the lungs will be examined by fluorescence microscopy to validate the observed bioluminescence results. This sophisticated system combining luciferase and fluorescence-based detection tools evaluates cancer metastasis in vivo, which has great potential for use in breast cancer therapeutics and disease management.

Here, we describe a noninvasive monitoring method involving luciferase and green fluorescent protein expression in various breast cancer cell lines. This protocol provides a technique to monitor tumor formation and metastatic colonization in real time in mice.

Breast cancer is a frequent heterogeneous malignancy and the second leading cause of mortality in women, mainly due to distant organ metastasis. Several ...