Name: selecting multiple biomarker subsets with similarly effective binary classification performances
Uploaded: 2018-10-11T13:00:00
Description: Watch this Scientific Journal Video about Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances at JoVE.com

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB13040400) and the startup grant from Jilin University. Anonymous reviewers and biomedical testing users were appreciated for their constructive comments on improving the usability and functionality of kSolutionVis.

NOTE: The following protocol describes the details of the informatics analytic procedure and pseudo-codes of the major modules. The automatic analysis system was developed using Python version 3.6.0 and the Python modules pandas, abc, numpy, scipy, sklearn, sys, PyQt5, sys, mRMR, math and matplotlib. The materials used in this study are listed in the Table of Materials.
1. Prepare the Data Matrix and Class Labels
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	<li>Prepare the data matrix file as a TAB- or comma-deli...

This study presents an easy-to-follow multi-solution biomarker detection and characterization protocol for a user-specified binary classification dataset. The software puts an emphasis on user-friendliness and flexible import/export interfaces for various file formats, allowing a biomedical researcher to investigate their dataset easily using the GUI of the software. This study also highlights the necessity of generating more than one solution with similarly effective modeling performances, previously ignored by many exi...

Binary classification, one of the most commonly investigated and challenging data mining problems in the biomedical area, is used to build a classification model trained on two groups of samples with the most accurate discrimination power1,2,3,4,5,6,7. However, the big data generated in the biomedical field has the inherent &#34;large p small n&#34; paradigm, with the number of features usually much larger than the number of samples6,8,9. Therefore, biomedical researchers have to reduce the feature dimension before utilizing the classification algorithms to avoid the overfitting problem8,9. Diagnosis biomarkers are defined as a subset of detected features separating patients of a given disease from healthy control samples10,11. Patients are usually defined as the positive samples, and the healthy controls are defined as the negative samples12.
Recent studies have suggested that there exists more than one solution with identical or similarly effective classification performances for a biomedical dataset5. Almost all the feature selection algorithms are deterministic algorithms, producing only one solution for the same dataset. Genetic algorithms may simultaneously generate multiple solutions with similar performances, but they still try to select one solution with the best fitness function as the output for a given dataset13,14.
Feature selection algorithms can be roughly grouped as either filters or wrappers12. A filter algorithm chooses the top-k features ranked by their significant individual association with the binary class labels based on the assumption that features are independent of each other15,16,17. Although this assumption does not hold true for almost all real-world datasets, the heuristic filter rule performs well in many cases, for instance, the mRMR (Minimum Redundancy and Maximum Relevance) algorithm, the Wilcoxon test based feature filtering (WRank) algorithm, and the ROC (Receiver operating characteristic) plot based filtering (ROCRank) algorithm. mRMR, is an efficient filter algorithm because it approximates the combinatorial estimation problem with a series of much smaller problems, comparing to the maximum-dependency feature selection algorithm, each of which only involves two variables, and therefore uses pairwise joint probabilities which are more robust18,19. However, mRMR may underestimate the usefulness of some features as it does not measure the interactions between features which can increase relevancy, and thus misses some feature combinations that are individually useless but are useful only when combined. The WRank algorithm calculates a non-parametric score of how discriminative a feature is between two classes of samples, and is known for its robustness for outliers20,21. Furthermore, the ROCRank algorithm evaluates how significant the Area Under the ROC Curve (AUC) of a particular feature is for the investigated binary classification performance22,23.
On the other hand, a wrapper evaluates the pre-defined classifier&#39;s performance of a given feature subset, iteratively generated by a heuristic rule, and creates the feature subset with the best performance measurement24. A wrapper generally outperforms a filter in the classification performance but runs slower25. For example, the Regularized Random Forest (RRF)26,27 algorithm uses a greedy rule, by evaluating the features on a subset of the training data at each random forest node, whose feature importance scores are evaluated by the Gini index. The choice of a new feature will be penalized if its information gain does not improve that of the chosen features. Additionally, the Prediction Analysis for Microarrays (PAM)28,29 algorithm, also a wrapper algorithm, calculates a centroid for each of the class labels, and then selects features to shrink the gene centroids toward the overall class centroid. PAM is robust for outlying features.
Multiple solutions with the top classification performance may be necessary for any given dataset. Firstly, the optimization goal of a deterministic algorithm is defined by a mathematical formula, e.g., minimum error rate30, which is not necessarily ideal for biological samples. Secondly, a dataset may have multiple, significantly different, solutions with similar effective or even identical performances. Almost all existing feature selection algorithms will randomly select one of these solutions as the output31.
This study will introduce an informatics analytic protocol for generating multiple feature selection solutions with similar performances for any given binary classification dataset. Considering that most biomedical researchers are not familiar with informatic techniques or computer coding, a user-friendly graphical user interface (GUI) was developed to facilitate the rapid analysis of biomedical binary classification datasets. The analytic protocol consists of data loading and summarizing, parameter tuning, pipeline execution, and result interpretations. With a simple click, the researcher is able to generate the biomarker subsets and publication-quality visualization plots. The protocol has been tested using the transcriptomes of two binary classification datasets of Acute Lymphoblastic Leukemia (ALL), i.e., ALL1 and ALL212. The datasets of ALL1 and ALL2 were downloaded from the Broad Institute Genome Data Analysis Center, available at http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi.&#160;ALL1 contains 128 samples with 12,625 features. Of these samples, 95 are B-cell ALL and 33 are T-cell ALL. ALL2 includes 100 samples with 12,625 features as well. Of these samples, there are 65 patients that suffered relapse and 35 patients that did not. ALL1 was an easy binary classification dataset, with a minimum accuracy of four filters and four wrappers being 96.7%, and 6 of the 8 feature selection algorithms achieving 100%12. While ALL2 was a more difficult dataset, with the above 8 feature selection algorithms achieving no better than 83.7% accuracy12. This best accuracy was achieved with 56 features detected by the wrapper algorithm, Correlation-based Feature Selection (CFS).

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			<td height="22" style="height:22px;width:81px;">Hardware</td>
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			<td height="64" style="height:64px;width:81px;">laptop</td>
			<td style="width:88px;">Lenovo</td>
			<td style="width:137px;">X1 carbon</td>
			<td style="width:363px;">Any computer works. Recommended minimum configuration: 1GB extra hard disk space, 1 GB memory, 2.0MHz CPU</td>
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			<td height="22" style="height:22px;width:81px;">Name</td>
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			<td height="43" style="height:43px;width:81px;">Python 3.0</td>
			<td style="width:88px;">WingWare</td>
			<td style="width:137px;">Wing Personal</td>
			<td style="width:363px;">Any python programming and running environments support Python version 3.0 or above</td>
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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.

The goal of this workflow (Figure 6) is to detect multiple biomarker subsets with similar efficiencies for a binary classification dataset. The whole process is illustrated by two example datasets ALL1 and ALL2 extracted from a recently-published biomarker detection study12,48. A user may install kSolutionVis by following the instructions in the supplementary materials.
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Watch this Scientific Journal Video about Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances at JoVE.com

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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 ...

This measure can help answer key questions in the biomedical detection field about generating multiple solutions. The main advantage of this technique is that it provides a user-friendly graphical user interface for assisting biomedical researchers in the detection of multiple feature subsides. Begin by loading the data Matrix and class labels into the software.Click load data matrix to select the user specified data metrics file and load class labels to select the corresponding class label file. To determine the class labels in the number of top-ranked features, select the names of the positive and negative classes in the appropriate drop down boxes and select 10 as the number of top-ranked features in the top X drop down box for a comprehensive screen of the feature subset. To tune the system parameters for different performances, select the performance measurement accuracy as the accuracy balanced accuracy drop down box for the selected extreme learning machine classifier.Then, select a cut-off value of 0.7 for the specified performance measurement in the performance cutoff input box. To run the pipeline, click analyze and select 0.7 as the default value of the performance measurement cut off. And, 10 as the default number of the best feature subsets.Then collect and interpret the features detected by the software. To generate a 3D scatter plot of the top 10 features of the subsets with the best classification performances detected by the software, click analyze and sort the three features in a feature subset in ascending order of their ranks, using the ranks of the three features as the F1, F2, and F3 axes. Change the performance cut off value to 0.7 and click analyze to generate a 3D scatter plot of the feature subsets with a greater than or equal to performance cut off performance measurement value.Then click 3D tuning to open a new window for manual tuning of the viewing angles of the 3D scatter plot and reduce to reduce the redundancy of the detected feature subsets. To annotate a gene in both the DNA and protein sequence levels, open the David database web page and click on the gene ID conversion link to input the feature IDs of the first biomarker subset of the prepared data set. Click the gene list link and click submit list to retrieve the annotations of interest, and show Gene list to obtain the list of Gene symbols.Next, open the GeneCards database web page and enter the name of the gene of interest into the database query input box to find the annotations of this gene. Open the Online Mendelian Inheritance in Man database and search for the gene to find the annotations of this gene from the database. To annotate the encoded proteins, open the UniProt knowledge base database page and search for the annotations of the gene from this database.Open the group based prediction system, or GPS web server, and retrieve the protein sequence encoded by the biomarker gene from the UniProt knowledge base database and use the online GPS tool to predict the proteins post-transitional modification residues. To annotate the protein-protein interactions and there enriched functional modules, open the string web server page and use the string database to search the lift for the genes of interest to find their orchestrated properties. To export the detected biomarker subsets for further analysis, click export the table and select the appropriate text format for saving the files.Then, export the visualization plots as individual image files, clicking save under each plot and selecting the appropriate image format for saving each file. In this representative experiment, two data sets were formatted as CSV files and loaded into the software as demonstrated. In the first data set, 128 samples with 12, 625 features and individual class labels were loaded with the final data Matrix containing 95 negative samples and 33 positive samples.Similar operations were also conducted for the second difficult data set. Searching for a user-specific keyword in the feature names reveals a histogram of the features for each data set. After executing the pipeline algorithm for each data set, 120 qualified biomarker subsets were detected for the easy to discriminate data set, with 57 triplet biomarker subsets demonstrating a 100%accuracy.Only 76 biomarker subsets where detected for the difficult data set, however. And, with a lower biomarkers subset accuracy suggesting that biomarkers are phenotype specific, another major challenge in biomarker detection. While using this procedure, it's important to remember that a future selection problem has multiple solutions.Read the SIM best of performance. After its development, this technique paved the way for biomedical researchers to explore biomedical detection with multiple solutions.

selecting-multiple-biomarker-subsets-with-similarly-effective-binary

Research

JoVE Journal

Cancer Research

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Often cellular behavior and cellular responses are analyzed at the population level where the responses of many cells are pooled together as an average result masking the rich single cell behavior within a complex population. Single cell protein detection and quantification technologies have made a remarkable impact in recent years. Here we describe a practical and flexible single cell analysis platform based on addressable droplet microarrays. This study describes how the absolute copy numbers of target proteins may be measured with single cell resolution. The tumor suppressor p53 is the most commonly mutated gene in human cancer, with more than 50% of total cancer cases exhibiting a non-healthy p53 expression pattern. The protocol describes steps to create 10 nL droplets within which single human cancer cells are isolated and the copy number of p53 protein is measured with single molecule resolution to precisely determine the variability in expression. The method may be applied to any cell type including primary material to determine the absolute copy number of any target proteins of interest.

Here we present addressable droplet microarrays (ADMs), a droplet array based method able to determine absolute protein abundance in single cells. We demonstrate the capability of ADMs to characterize the heterogeneity in expression of the tumor suppressor protein p53 in a human cancer cell line.

Often cellular behavior and cellular responses are analyzed at the population level where the responses of many cells are pooled together as an average ...

Laparoscopic Pancreatoduodenectomy With Modified Blumgart Pancreaticojejunostomy

Minimally invasive pancreatic resections are technically demanding but rapidly increasing in popularity. In contrast to laparoscopic distal pancreatectomy, laparoscopic pancreatoduodenectomy (LPD) has not yet obtained wide acceptance, probably due to technical challenges, especially regarding the pancreatic anastomosis.
The study describes and demonstrates all steps of LPD, including the modified Blumgart pancreaticojejunostomy. Indications for LPD are all pancreatic and peri-ampullary tumors without vascular involvement. Relative contra-indications are body mass index &#62;35 kg/m2, chronic pancreatitis, mid-cholangiocarcinomas and large duodenal cancers.
The patient is in French position, 6 trocars are placed, and dissection is performed using an (articulating) sealing device. A modified Blumgart end-to-side pancreaticojejunostomy is performed with 4 large needles (3/0) barbed trans-pancreatic sutures and 4 to 6 duct-to-mucosa sutures using 5/0 absorbable multifilament combined with a 12 cm, 6 or 8 Fr internal stent using 3D laparoscopy. Two surgical drains are placed alongside the pancreaticojejunostomy.
The described technique for LPD including a modified Blumgart pancreatico-jejunostomy is well standardized, and its merits are currently studied in the randomized controlled multicenter trial. This complex operation should be performed at high-volume centers where surgeons have extensive experience in both open pancreatic surgery and advanced laparoscopic gastro-intestinal surgery.

Laparoscopic pancreatoduodenctomy (LPD) may offer advantages over open pancreatoduodenectomy, including early postoperative mobilization, less delayed gastric emptying and a shorter hospital stay. However, LPD is technically challenging and not well-standardized, especially regarding the pancreatic anastomosis. We describe a standardized technique for the pancreatic anastomosis during LPD: modified Blumgart pancreaticojejunostomy.

Minimally invasive pancreatic resections are technically demanding but rapidly increasing in popularity. In contrast to laparoscopic distal ...

Multimodal Bioluminescent and Positronic-emission Tomography/Computational Tomography Imaging of Multiple Myeloma Bone Marrow Xenografts in NOG Mice

Multiple myeloma (MM) tumors engraft in the bone marrow (BM) and their survival and progression are dependent upon complex molecular and cellular interactions that exist within this microenvironment. Yet the BM microenvironment cannot be easily replicated in vitro, which potentially limits the physiologic relevance of many in vitro and ex vivo experimental models. These issues can be overcome by utilizing a xenograft model in which luciferase (LUC)-transfected 8226 MM cells will specifically engraft in the mouse skeleton. When these mice are given the appropriate substrate, D-luciferin, the effects of therapy on tumor growth and survival can be analyzed by measuring changes in the bioluminescent images (BLI) produced by the tumors in vivo. This BLI data combined with positronic-emission tomography/computational tomography (PET/CT) analysis using the metabolic marker 2-deoxy-2-(18F)fluoro-D-glucose (18F-FDG) is used to monitor changes in tumor metabolism over time. These imaging platforms allow for multiple noninvasive measurements within the tumor/BM microenvironment.

Here we use bioluminescent, X-ray, and positron-emission tomography/computed tomography imaging to study how inhibiting mTOR activity impacts bone marrow-engrafted myeloma tumors in a xenograft model. This allows for physiologically relevant, non-invasive, and multimodal analyses of the anti-myeloma effect of therapies targeting bone marrow-engrafted myeloma tumors in vivo.

Multiple myeloma (MM) tumors engraft in the bone marrow (BM) and their survival and progression are dependent upon complex molecular and cellular ...

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 ...

Repression of Multiple Myeloma Cell Growth In Vivo by Single-wall Carbon Nanotube (SWCNT)-delivered MALAT1 Antisense Oligos

The single-wall carbon nanotube (SWCNT) is a new type of nanoparticle, which has been used to deliver multiple kinds of drugs into cells, such as proteins, oligonucleotides, and synthetic small-molecule drugs. The SWCNT has customizable dimensions, a large superficial area, and can flexibly bind with drugs through different modifications on its surface; therefore, it is an ideal system to transport drugs into cells. Long noncoding RNAs (lncRNAs) are a cluster of noncoding RNA longer than 200 nt, which cannot be translated to protein but play an important role in biological and pathophysiological processes. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a highly conserved lncRNA. It was demonstrated that higher MALAT1 levels are related to the poor prognosis of various cancers, including multiple myeloma (MM). We have revealed that MALAT1 regulates DNA repair and cell death in MM; thus, MALAT1 can be considered as a therapeutic target for MM. However, the efficient delivery of the antisense oligo to inhibit/knockdown MALAT1 in vivo is still a problem. In this study, we modify the SWCNT with PEG-2000 and conjugate an anti-MALAT1 oligo to it, test the delivery of this compound in vitro, inject it intravenously into a disseminated MM mouse model, and observe a significant inhibition of MM progression, which indicates that SWCNT is an ideal delivery shuttle for anti-MALAT1 gapmer DNA.

Repression of Multiple Myeloma Cell Growth In Vivo by Single-wall Carbon Nanotube SWCNT-delivered MALAT1 Antisense Oligos

This manuscript describes the synthesis of a single-wall carbon nanotube (SWCNT)-conjugated MALAT1 antisense gapmer DNA oligonucleotide (SWCNT-anti-MALAT1), which demonstrates the reliable delivery of the SWCNT and the potent therapeutic effect of anti-MALAT1 in vitro and in vivo. Methods used for synthesis, modification, conjugation, and injection of SWCNT-anti-MALAT1 are described.

The single-wall carbon nanotube (SWCNT) is a new type of nanoparticle, which has been used to deliver multiple kinds of drugs into cells, such as ...

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.

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 ...

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

Chemotherapeutic drugs can induce irreparable DNA damage in cancer cells, leading to apoptosis or premature senescence. Unlike apoptotic cell death, senescence is a fundamentally different machinery restraining&#160;propagation of cancer cells. Decades of scientific studies have revealed the complex pathological effects of senescent cancer cells in tumors and microenvironments that modulate cancer cells and stromal cells. New evidence suggests that senescence is a potent prognostic factor during cancer treatment, and therefore rapid and accurate detection of senescent cells in cancer samples is essential. This paper presents a method to visualize and detect therapy-induced senescence (TIS) in cancer cells. Diffuse large B-cell lymphoma (DLBCL) cell lines were treated with mafosfamide (MAF) or daunorubicin (DN) and examined for the senescence marker, senescence-associated &#946;-galactosidase (SA-&#946;-gal), the DNA synthesis marker 5-ethynyl-2&#8242;-deoxyuridine (EdU), and the DNA damage marker gamma-H2AX (&#947;H2AX). Flow cytometer imaging can help generate high-resolution single-cell images in a short period of time to simultaneously visualize and quantify the three markers in cancer cells.

Here we present a flow cytometry-based method for visualization and quantification of multiple senescence-associated markers in single cells.

Chemotherapeutic drugs can induce irreparable DNA damage in cancer cells, leading to apoptosis or premature senescence. Unlike apoptotic cell death, ...

A Standardized Liquid Biopsy Preanalytical Protocol for Downstream Circulating-Free DNA Applications

The term liquid biopsy (LB) refers to molecules such as proteins, DNA, RNA, cells, or extracellular vesicles in blood and other bodily fluids that originate from the primary and/or metastatic tumor. LB has emerged as a mainstay in translational research and has started to become part of clinical oncology practice, providing a minimally invasive alternative to solid biopsy. The LB allows real-time monitoring of a tumor via a minimally invasive sample extraction, such as blood. The applications include early cancer detection, patient follow-up for the detection of disease progression, assessment of minimal residual disease, and potential identification of molecular progression and mechanism of resistance. In order to achieve a reliable analysis of these samples that can be reported in the clinic, the preanalytical procedures should be carefully considered and strictly followed. Sample collection, quality, and storage are crucial steps that determine their usefulness in downstream applications. Here, we present standardized protocols from our liquid biopsy working module for collecting, processing, and storing plasma and serum samples for downstream liquid biopsy analysis based on circulating-free DNA. The protocols presented here require standard equipment and are sufficiently flexible to be applied in most laboratories focused on biological procedures.

The liquid biopsy has revolutionized our approach to oncology translational studies, with sample collection, quality, and storage being crucial steps for its successful clinical application. Here we describe a standardized and validated protocol for downstream circulating-free DNA applications that can be applied in most translational research laboratories.

The term liquid biopsy (LB) refers to molecules such as proteins, DNA, RNA, cells, or extracellular vesicles in blood and other bodily fluids that ...

&lt;em&gt;TMEM200A&lt;/em&gt;: 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 ...

Cytometer Assays of MPNST Cells Challenged with Therapeutic Agents

To begin, plate the 80%confluent malignant peripheral nerve sheath tumor or MPNST cells at a density of 1200 cells per well in black-walled 96 well plates. After preparing dilutions of the drug to be tested, add each dilution or vehicle to at least three replicate cells. To assess direct cell number, add hoaxed 33342, to a final concentration of one to five micrograms per milliliter to the plates.And incubate the plates for 30 minutes at 37 degrees Celsius. Place the plates on a high throughput imaging cytometer and scan the plates using the direct cell count for total cell number option with 100, 000 to 600, 000 milliseconds exposure times. Analyze the reads using the software, export them into a spreadsheet and use appropriate software for statistical analyses.