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&lt;br/&gt; 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&lt;br/&gt; 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&lt;br/&gt; 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&lt;br/&gt; 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&lt;br/&gt; 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...

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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

Universidad de Cartagena UDEC

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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

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 ...

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to interference with proper development and may even result in initiation or progression of cancers in certain cases. Because this pathway serves complex and versatile functions, it can be studied extensively through many different approaches. Of these, bioinformatics provides an undeniably cost-efficient, approachable, and user-friendly method of study. Bioinformatics is a useful way to extract smaller pieces of information from large-scale datasets. Through the implementation of various bioinformatics approaches, researchers can quickly, reliably, and efficiently interpret these large datasets, yielding insightful applications and scientific discoveries. Here, a protocol is presented for integration of bioinformatics approaches to investigate the role of Notch signaling in ovarian cancer. Furthermore, bioinformatics findings are validated through experimentation.

Bioinformatics is a useful way to process large-scale datasets. Through the implementation of bioinformatics approaches, researchers can quickly, reliably, and efficiently obtain insightful applications and scientific discoveries. This article demonstrates the utilization of bioinformatics in ovarian cancer research. It also successfully validates bioinformatics findings through experimentation.

Notch signaling is a highly conserved regulatory pathway involved in many cellular processes. Dysregulation of this signaling pathway often leads to ...

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was established using robust, reproducible in vivo models and standardized methods to identify novel players in melanoma metastasis. This approach allows for data inference at specific experimental stages to precisely characterize a gene's role in metastasis. Models are established by introducing genetically modified melanoma cells via intracardiac, intradermal, or subcutaneous injections into mice, followed by monitoring with serial in vivo imaging. Once preestablished endpoints are reached, primary tumors and/or metastases-bearing organs are harvested and processed for various analyses. Tumor cells can be sorted and subjected to any of several 'omics' platforms, including single-cell RNA sequencing. Organs undergo imaging and immunohistopathological analyses to quantify the overall burden of metastases and map their specific anatomic location. This optimized pipeline, including standardized protocols for engraftment, monitoring, tissue harvesting, processing, and analysis, can be adopted for patient-derived, short-term cultures and established human and murine cell lines of various solid cancer types.

This article describes a workflow of techniques employed for testing novel candidate mediators of melanoma metastasis and their mechanism(s) of action.

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was ...

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

According to the American Cancer Society, more than 200,000 women will be diagnosed with invasive breast cancer each year and approximately 40,000 will die from the disease. The human leukocyte antigen (HLA) class I samples peptides derived from proteasomal degradation of cellular proteins and presents these fragments on the cell surface for interrogation by circulating cytotoxic T lymphocytes (CTL). Generation of T-cell receptor mimic (TCRm) monoclonal antibodies (mAbs) which recognize breast cancer specific peptide/HLA-A*02:01 complexes such as those derived from macrophage migration inhibitory factor (MIF19-27) and NY-ESO-1157-165 enable detection and destruction of breast cancer cells in the absence of an effective anti-tumor CTL response. Intact class I HLA/peptide complexes are shed by breast cancer cells and represent potentially relevant cancer biomarkers. In this work, a breakthrough biomarker screening system for cancer diagnostics incorporating T-cell receptor mimic monoclonal antibodies combined with a novel, label-free biosensor utilizing guided-mode resonance (GMR) sensor technology is presented. Detection of shed MIF/HLA-A*02:01 complexes in MDA-MB-231 cell supernatants, spiked human serum, and patient plasma is demonstrated. The impact of this work could revolutionize personalized medicine through development of companion disease diagnostics for targeted immunotherapies.

Intact class I HLA/peptide complexes are shed by cancer cells, representing a potential relevant cancer biomarker. Utilizing label-free sensor technology and T-cell receptor mimicking monoclonal antibodies, detection of shed MIF/HLA-A*02:01 complexes in MDA-MB-231 cell supernatants, spiked human serum, and patient plasma is demonstrated, enabling development of a novel cancer diagnostic platform.

According to the American Cancer Society, more than 200,000 women will be diagnosed with invasive breast cancer each year and approximately 40,000 will ...

Immunology and Infection

Unsupervised Analysis of Multi-Omics Data in Coronary Syndromes Using MOFA

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a valuable resource to generate more insights into those processes, but the analysis of such datasets can be challenging due to the high dimensionality resulting, for example, from different disease conditions, timepoints, and omics capturing the process at different resolutions.
Here, we showcase an approach to analyze and explore such a complex multiomics dataset in an unsupervised way by applying multi-omics factor analysis (MOFA) to a dataset generated from blood samples that capture the immune response in acute and chronic coronary syndromes. The dataset consists of several assays at differing resolutions, including sample-level cytokine data, plasma-proteomics and neutrophil prime-seq, and single-cell RNA-seq (scRNA-seq)&#160;data. Further complexity is added by having several different time points measured per patient and several patient subgroups.
The analysis workflow outlines how to integrate and analyze the data in several steps: (1) Data pre-processing and harmonization, (2) Estimation of the MOFA model, (3) Downstream analysis. Step 1 outlines how to process the features of the different data types, filter out low-quality features, and normalize them to harmonize their distributions for further analysis. Step 2 shows how to apply the MOFA model and explore the major sources of variance within the dataset across all omics and features. Step 3 presents several strategies for the downstream analysis of the captured patterns, linking them to the disease conditions and potential molecular processes governing those conditions.
Overall, we present a workflow for unsupervised data exploration of complex multi-omics datasets to enable the identification of major axes of variation composed of differing molecular features that can also be applied to other contexts and multi-omics datasets (including other assays as presented in the exemplary use case).

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

We present a flexible, extendible Jupyter-lab-based workflow for the unsupervised analysis of complex multi-omics datasets that combines different pre-processing steps, estimation of the multi-omics factor analysis model, and several downstream analyses.

Disease mechanisms are usually complex and governed by the interaction of several distinct molecular processes. Complex, multidimensional datasets are a ...

Genetics

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a &#8216;donor&#8217; block into a &#8216;recipient&#8217; block and then used for a variety of biomarker applications. The construction of conventional TMAs is labor intensive, imprecise, and time-consuming. Here, a protocol using next-generation Tissue Microarrays (ngTMA) is outlined. ngTMA is based on TMA planning and design, digital pathology, and automated tissue microarraying. The protocol is illustrated using an example of 134 metastatic colorectal cancer patients. Histological, statistical and logistical aspects are considered, such as the tissue type, specific histological regions, and cell types for inclusion in the TMA, the number of tissue spots, sample size, statistical analysis, and number of TMA copies. Histological slides for each patient are scanned and uploaded onto a web-based digital platform. There, they are viewed and annotated (marked) using a 0.6-2.0 mm diameter tool, multiple times using various colors to distinguish tissue areas. Donor blocks and 12 &#8216;recipient&#8217; blocks are loaded into the instrument. Digital slides are retrieved and matched to donor block images. Repeated arraying of annotated regions is automatically performed resulting in an ngTMA. In this example, six ngTMAs are planned containing six different tissue types/histological zones. Two copies of the ngTMAs are desired. Three to four slides for each patient are scanned; 3 scan runs are necessary and performed overnight. All slides are annotated; different colors are used to represent the different tissues/zones, namely tumor center, invasion front, tumor/stroma, lymph node metastases, liver metastases, and normal tissue. 17 annotations/case are made; time for annotation is 2-3 min/case. 12 ngTMAs are produced containing 4,556 spots. Arraying time is 15-20 hr. Due to its precision, flexibility and speed, ngTMA is a powerful tool to further improve the quality of TMAs used in clinical and translational research.

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

The protocol aims at optimizing the construction and quality of tissue microarrays for biomarker research. It includes aspects of planning and design, digital pathology, virtual slide annotation, and automated tissue arraying.

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a &#8216;donor&#8217; block into ...

Medicine

TMEM200A: A Novel Oncogene Regulating EMT and PI3K/AKT in Gastric Cancer

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

The transmembrane protein, TMEM200A, is known to be associated with human cancers and immune infiltration. Here, we assessed the function of TMEM200A in common cancers by multiomics analysis and used in vitro cell cultures of gastric cells to verify the results. The expression of TMEM200A in several human cancer types was assessed using the RNA-seq data from the UCSC Xena database. Bioinformatic analysis revealed a potential role of TMEM200A as a diagnostic and prognostic biomarker. 
Cultures of normal gastric and cancer cell lines were grown and TMEM200A was knocked down. The expression levels of TMEM200A were measured by using quantitative real-time polymerase chain reaction and western blotting. In vitro loss-of-function studies were then used to determine the roles of TMEM200A in the malignant behavior and tumor formation of gastric cancer (GC) cells. Western blots were used to assess the effect of the knockdown on epithelial-mesenchymal transition (EMT) and PI3K/AKT signaling pathway in GC. Bioinformatic analysis showed that TMEM200A was expressed at high levels in GC. 
The proliferation of GC cells was inhibited by TMEM200A knockdown, which also decreased vimentin, N-cadherin, and Snai proteins, and inhibited AKT phosphorylation. The PI3K/AKT signaling pathway also appeared to be involved in TMEM200A-mediated regulation of GC development. The results presented here suggest that TMEM200A regulates the tumor microenvironment by affecting the EMT. TMEM200A may also affect EMT through PI3K/AKT signaling, thus influencing the tumor microenvironment. Therefore, in pan-cancers, especially GC, TMEM200A may be a potential biomarker and oncogene.

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies 

Here, a protocol is presented in which multiple bioinformatic tools are combined to study the biological functions of TMEM200A in cancer. In addition, we also experimentally validate the bioinformatics predictions.

The transmembrane protein, TMEM200A, is known to be associated with human cancers and immune infiltration. Here, we assessed the function of TMEM200A in ...

TMOD3 Expression and Its Role in Ovarian Cancer Progression

Tropomodulin 3 Overexpression as a Marker for Platinum Resistance and Immune Infiltration in Ovarian Cancer

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, but its role in the drug resistance of ovarian cancer remains unexplored. By analyzing data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, this study compared TMOD3 expression in ovarian cancer and normal tissues, and examined the expression of TMOD3 after platinum treatment in platinum-sensitive and platinum-resistant ovarian cancers. The Kaplan-Meier method was used to assess the effect of TMOD3 on overall survival (OS) and progression-free survival (PFS) in ovarian cancer patients. microRNAs (miRNAs) targeting TMOD3 were predicted using TargetScan and analyzed using the TCGA database. Tumor Immune Estimation Resource (TIMER) and an integrated repository portal for tumor-immune system interactions (TISIDB) were used to determine the relationship between TMOD3 expression and immune infiltration. TMOD3 coexpression networks in ovarian cancer were explored using LinkedOmics, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics. The results showed that TMOD3 was highly expressed in ovarian cancer and was associated with the grading, staging, and metastasis of ovarian cancer. TMOD3 expression was significantly reduced in platinum-treated ovarian cancer cells and patients. However, TMOD3 expression was higher in platinum-resistant ovarian cancer cells and tissues compared to platinum-sensitive ones. Higher TMOD3 expression was significantly associated with lower OS and PFS in ovarian cancer patients treated with platinum-based chemotherapy. miRNA-mediated post-transcriptional regulation is likely responsible for high TMOD3 expression in ovarian cancer and platinum-resistant ovarian tissues. The expression of TMOD3 mRNA was associated with immune infiltration in ovarian cancer. These findings indicate that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration.

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Tropomodulin 3 (TMOD3) has been increasingly studied in tumors in recent years. This study is the first to report that TMOD3 is highly expressed in ovarian cancer and is closely associated with platinum resistance and immune infiltration. These results could help improve the therapeutic outcomes for ovarian cancer.

The cytoskeleton plays an important role in platinum resistance in ovarian cancer. Tropomodulin 3 (TMOD3) is critical in the development of many tumors, ...

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Nucleic acid degradation in archival tissue, tumor heterogeneity, and a lack of fresh frozen tissue specimens can negatively impact cancer diagnostic services in pathology laboratories worldwide. Gene amplification and expression diagnostic testing using archival material or material that requires transportation to servicing laboratories, needs a more robust and accurate test adapted to current clinical workflows. Our research team optimized the use of Invitrogen&#8482; QuantiGene&#8482; Plex Assay (Thermo Fisher Scientific) to quantify RNA in archival material using branched-DNA (bDNA) technology on Luminex xMAP&#174; magnetic beads. The gene expression assay described in this manuscript is a novel, quick, and multiplex method that can accurately classify breast cancer into the different molecular subtypes, omitting the subjectivity of interpretation inherent in imaging techniques. In addition, due to the low input of material required, heterogeneous tumors can be laser microdissected using Hematoxylin and Eosin (H&#38;E) stained sections. This method has a wide range of possible applications including tumor classification with diagnostic potential and measurement of biomarkers in liquid biopsies, which would allow better patient management and disease monitoring. In addition, the quantitative measurement of biomarkers in archival material is useful in oncology research with access to libraries of clinically-annotated material, in which retrospective studies can validate potential biomarkers and their clinical outcome correlation.

Nucleic acid degradation in archival tissue, tumor heterogeneity, and a lack of fresh frozen tissue specimens can negatively impact cancer diagnostic services in pathology laboratories worldwide. This manuscript describes the optimization of a panel of biomarkers using a multiplex magnetic bead assay to classify breast cancers.

Nucleic acid degradation in archival tissue, tumor heterogeneity, and a lack of fresh frozen tissue specimens can negatively impact cancer diagnostic ...

Bioengineering

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective mining of the information in the large-scale datasets that are obtained from gene expression microarrays, deep sequencing experiments or metabolic profiling is essential to uncover and then effectively target the critical regulators of diseased cell phenotypes. Estrogen Receptor &#945; (ER&#945;) is one of the master transcription factors regulating the gene programs that are important for estrogen responsive breast cancers. In order to understand to role of ER&#945; signaling in breast cancer metabolism we utilized transcriptomic, cistromic and metabolomic data from MCF-7 cells treated with estradiol. In this report we described generation of samples for RNA-Seq, ChIP-Seq and metabolomics experiments and the integrative computational analysis of the obtained data. This approach is useful in delineating novel molecular mechanisms and gene regulatory circuits that are regulated by a particular transcription factor which impacts metabolism of normal or diseased cells.

Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol.

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective ...