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</stro...

<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>

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.

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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 ...

Here, we present a protocol to explore the biomarker and survival predictor of breast cancer basis on the analysis of a variety of public accessible databases. This method can improve the credibility and representativeness of the conclusions, thereby present informative perspective on a gene of interest. The advantage of this method is that it allows for the rapid visualization and interpretation of a gene's potential role in breast cancer.Moreover, all the results obtained through this procedure can be immediately tested and repeated by simply querying the corresponding websites. We chose breast cancer in the study to test the feasibility of this method. Because breast cancer is a heterogeneous disease the diagnosis, treatment and prognosis of different molecular subtypes of breast cancer may vary.Therefore it is particularly important to find efficient online tool or database with information to reflect the disparate subtypes of breast cancer. To begin this procedure go to the ONCOMINE web interface. Type ID1 into the search box to obtain the relative expression levels of gene ID1 in various types of malignancies.In the primary filters menu select analysis type, then select Cancer vs. Normal Analysis. In the other views menu select gene summary view.Set the threshold of P-value at 0.01. Set the threshold of fold change to 2. Set the threshold of gene rank to All.Set the data type to All. Download the figures. First, go to the BC Gene-Expression Miner web interface.In the analysis menu, select correlation and then press the exhaustive button. Type ID1 into the search box and press the submit button and the start Analysis button. For subgroup analysis in the BC Gene-Expression Miner, go to the BC Gene-Expression Miner interface.In the analysis menu, select expression and press the exhaustive button. Type ID1 into the search box and press the submit button and the start analysis button. Click the nodal status and Scarff Bloom Richardson grade status thumbnails to view full images.In the Scarff Bloom Richardson images, press the button below to visualize the P-values of the figures. Then, download the figures. For subgroup analysis via Gene expression-based Outcome for Breast Cancer Online, go to the GOBO web interface.Type ID1 for the gene symbol of interest and upload the gene set. Set the search range for define gene/probe identifiers to gene symbol. Set all in tumor selection, and select node status and grade stratified in the multivariate parameters.The other items remain default. Now, submit the inquiry and download the figures. For survival analysis in the BC Gene-Expression Miner, go to the BC Gene-Expression Miner web interface.In the analysis menu, select prognostic and then press the exhaustive button. Type ID1 into the search box and press the submit button and then the start analysis button. In the exhaustive prognostic analysis, select Nm, ERm, MR in the population and event criteria, and press the submit button to obtain more information.After this click the Kaplan-Meier curve thumbnails to export the full graphs. For survival analysis in the Human Protein Atlas, go to the Human Protein Atlas web interface. Type ID1 to the search box and click the search button.Next, select the pathology sub-atlas. Click the label for breast cancer and a detailed page will appear showing an interactive survival scatter plot and survival analysis. Download these figures.For survival analysis in the Kaplan-Meier Plotter Survival, go to the Kaplan-Meier Plotter web interface. In the mRNA gene chip zone, click start KM plotter for breast cancer. Type ID1 into the search bar and select the green item in the candidate menu.Then, select RFS as the survival type and leave the other items at their default settings. Click draw Kaplan-Meier plot and download the figures. In this study, a representative result for the data mining and integrative analysis of a breast cancer biomarker is performed using ID1, one of the inhibitor's DNA-binding family members.The differences of ID1 mRNA expression between tumor and normal tissues are first analyzed using the ONCOMINE database, which contains a total of 445 unique analyses. There are only 5 studies revealing an mRNA expression level of ID1 that is significantly higher in normal tissues than in breast cancer tissues, which indicates that there is an expression dysregulation of ID1 in breast cancer. The BC Gene-Expression Miner is then used to identify the correlation between mRNA expression of ID1 and the clinicopathological parameters of breast cancer patients.The mRNA levels of ID1 are seen to be significantly increased in breast cancer patients without lymph node metastasis compared to those with lymph node metastasis. The analysis in GOBO demonstrates that increased mRNA levels of ID1 correlate to lower tumor grade. These results imply that increased expression of ID1 is linked to lower metastatic potential and lower pathological grade in breast cancer.The analysis from the BC Gene-Expression Miner database indicates that higher mRNA levels of ID1 is correlated to longer distant metastasis-free survival in breast cancer patients. The analysis from the Human Protein Atlas also suggests that elevated protein levels of ID1 is associated with better survival outcome in breast cancer patients. The survival analysis from the Kaplan-Meier Plotter is consistent with these findings, and shows that higher mRNA levels of ID1 expression predicts better recurrence-free survival in breast cancer patients.More and more online databases will be availables or accessible for researchers. The protocol might provide an efficient method for researchers to identify potential target genes and associated signaling pathway.

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

Research

JoVE Journal

Cancer Research

8.7K vistas.  First Affiliated Hospital of Shantou University Medical College. Aquí, presentamos un protocolo para explorar el biomarcador y el predictor de supervivencia de la base del cáncer de mama sobre el análisis de una variedad de bases de datos accesibles al público. Este método puede mejorar la credibilidad y representatividad de las conclusiones, presentando así una perspectiva informativa sobre un gen de interés. La ventaja de este método es que permite la rápida visualización e interpretación del papel potencial de un gen en el cáncer de mama.