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>

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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 algorithm of the database19. The most important step of this protocol is to select the appropriate databases, which should be widely recognized by a larger number of scientists with adequate representativeness. The investigator should use multiple databases to test the hypothesis and corroborate the results derived from different databases, rather than use a single database.
The protocol described here is an investigator friendly operation procedure. The advantage of this method is that it allows for the rapid visualization and interpretation of a gene&#8217;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. The limitation of this method is that the conclusions which come from the comprehensive analysis of the databases may not exactly reflect the actual function or relationship in the clinical setting. This could stem from the systematical bias of the database, and in some case, possibly due to inadequate sample size32,33. Using more than one database to query the same research question could mutually confirm the results and increase the credibility of the conclusion34. It is strongly recommended to use samples from the investigator&#8217;s institution to verify the results, or if feasible, to perform related basic experiments to test the results.
More and more online cancer genomics or proteomics databases will be available and accessible for researchers35,36. The protocol might provide an efficient and economical method for the researcher to identify a potential target gene and the associated signaling pathway through in-depth analysis of online databases and by using genomics, transcriptomics, and epigenomics approach.

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 ONCOMINE database, which contained a total of 445 unique analyses. There were 5 studies which revealed that the mRNA expression level of ID1 was significantly higher in normal tissues than in breast cancer tissues. These data indicated the expression dysregulation of ID1 in breast cancer. Figure 3 showed the best positive and negative correlative genes of ID1 from the analysis performed in bc-GenExMiner v4.0. To identify the correlation between mRNA expression of ID1 and the clinicopathological parameters of BC patients, bc-GenExMiner v4.0 database was used the analysis. As shown in Figure 4, significantly increased mRNA level of ID1 was found in breast cancer patients without lymph node metastasis, as compared to those with lymph node metastasis (P=0.0005). Furthermore, the analysis in GOBO demonstrated that increased mRNA levels of ID1 were correlated to lower tumor grade (Figure 5, P&#60;0.00001). These results implied that increased expression of ID1 was linked to lower metastatic potential and lower pathological grade in BC. The analysis from the bc-GenExMiner v4.0 database indicated that higher mRNA level of ID1 was correlated to longer distant metastasis-free survival (DMFS) in breast cancer patients (Figure 6, HR=0.82, 95% CI: 0.73-0.92, P=0.001). Consistently, analysis from The Human Protein Atlas suggested that elevated protein level of ID1 was associated with better survival outcome in breast cancer patients (Figure 7, P=0.0389). Survival analysis from the Kaplan-Meier Plotter also showed that higher mRNA level of ID1 expression predicted better recurrence-free survival (RFS) in breast cancer patients (Figure 8, HR=0.81, P=0.00023).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59238/59238fig1.jpg" /> 
Figure 1. Overview of exploring the expression patterns and prognostic values of distinct breast cancer biomarkers and online databases selection. Systematic analysis of distinct breast cancer biomarkers was performed step by step in a variety of databases. First, the expression pattern of the gene of interest in cancerous samples vs. normal samples. Then, the expression correlation of genes of interest in breast cancer was performed. Next, the expression profiles of ID1 was stratified according to different. Finally, the association between ID1 expression and survival out was analyzed. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59238/59238fig2.jpg" /> 
Figure 2. The mRNA expression pattern of the ID1 in different types of human cancer. The mRNA expression of ID1 analyzed with the ONCOMINE database. The graphic demonstrated the numbers of datasets with statistically significant mRNA overexpression (red) or downregulated expression (blue) of the target gene. The number in each cell represented the number of analyses that meet the threshold within those analysis and cancer types. The gene rank was analyzed by percentile of the target gene in the top of all genes measured in each research. Cell color was determined by the best gene rank percentile for the analyses within the cell. The P-value was set up at 0.01 and fold-change was defined as 2, as shown in the red frame. This figure has been modified from the previous study25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59238/59238fig3.jpg" /> 
Figure 3. Gene correlation analysis of ID1 in bc-GenExMiner v4.0. The mRNA expression correlation of ID1 and relevant genes in 5, 696 breast cancer patients within 36 studies analyzed in bcGenExMiner v4.0. This figure has been modified from the previous study25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59238/59238fig4.jpg" /> 
Figure 4. The relationship between ID1 expression and lymph node metastasis status. The mRNA expression level of ID1 in 4, 307 breast cancer patients with different lymph node (LN) status analyzed in bcGenExMiner v4.0. This figure has been modified from the previous study25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59238/59238fig5.jpg" /> 
Figure 5. The relationship between the gene expression level of ID1 and tumor grade. The mRNA expression level of ID1 in breast cancer patients with different pathological grade was analyzed in GOBO. The global significant difference between groups was assessed to generate P-values and P&#60;0.05 was considered to indicate a statistically significant difference. 1,2,3 in x-axis stand for sub-groups of patients in different pathological grade 1, grade 2, grade 3. This figure has been modified from the previous study 25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59238/59238fig6.jpg" /> 
Figure 6. The prognostic values of ID1 for distant metastasis-free survival in breast cancer patients. The association between ID1 mRNA levels and distant metastasis-free survival estimates was analyzed in bcGenExMiner v4.0. This figure has been modified from the previous study25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/59238/59238fig7.jpg" /> 
Figure 7. The survival probability of ID1 in breast cancer patients. &#160;Impact of ID1 protein level for the survival of patients with breast cancer was analyzed in the human protein atlas (HPA). This figure has been modified from the previous study25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/59238/59238fig8.jpg" /> 
Figure 8. The prognostic values of ID1 in breast cancer according to recurrent-free survival (RFS). Different ID1 mRNA level in all 3, 951 breast cancer patients analyzed in Kaplan-Meier plotter. This figure has been modified from the previous study25. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59238/59238fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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

Nanyang Technological University

Research

JoVE Journal

Cancer Research

8.9K Views.  First Affiliated Hospital of Shantou University Medical College. Hier presenteren we een protocol om de biomarker en overlevingsvoorspeller van borstkanker te verkennen op basis van de analyse van een verscheidenheid aan openbare toegankelijke databases. Deze methode kan de geloofwaardigheid en representativiteit van de conclusies verbeteren, waardoor een informatief perspectief op een gen van belang is. Het voordeel van deze methode is dat het zorgt voor de snelle visualisatie en interpretatie van de potentiële rol van een gen bij borstkanker.Bove...

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 ...

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 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 ...

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

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