<meta charset="utf8"></head><body>TMOD3 表达及其在卵巢癌进展中的作用

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			<td>cBioportal</td>
			<td>Memorial Sloan Kettering Cancer Center</td>
			<td></td>
			<td>Correlation analysis of&#160; TMOD3 with targeted miRNAs (https://www.cbioportal.org)</td>
		</tr>
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			<td>CTD database</td>
			<td>North Carolina State University</td>
			<td></td>
			<td>To analyze the relationships between chemistry, genes, phenotype, disease, and environment (https://ctdbase.org/)</td>
		</tr>
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			<td>Cytoscape</td>
			<td>National Institute of General Medical Sciences of the National Institutes of Health</td>
			<td></td>
			<td>Network Data Integration, Analysis, and Visualization (www.cytoscape.org/)</td>
		</tr>
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			<td>DAVID</td>
			<td>Frederick National Laboratory for Cancer Research</td>
			<td></td>
			<td>A comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes(https://david.ncifcrf.gov/)</td>
		</tr>
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			<td>GEO</td>
			<td>NCBI</td>
			<td></td>
			<td>Gene expression analysis (https://www.ncbi.nlm.nih.gov/geo/ )</td>
		</tr>
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			<td>HPA</td>
			<td>Knut &#38; Alice Wallenberg foundation</td>
			<td></td>
			<td>The Human Protein Atlas (HPA) database helped analyze the distribution of TMOD3 in various immune cells (https://www.proteinatlas.org/)</td>
		</tr>
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			<td>KM-plotter</td>
			<td>&#160;Department of Bioinformatics of the Semmelweis University</td>
			<td></td>
			<td>Prognostic Analysis (https://kmplot.com/analysis/)</td>
		</tr>
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			<td>LinkedOmics</td>
			<td>Baylor College of Medicine</td>
			<td></td>
			<td>A platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types (http://www.linkedomics.org/)</td>
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			<td>PubChem database</td>
			<td>U.S. National Library of Medicine</td>
			<td></td>
			<td>To determine the definitive molecular structure of the drug</td>
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			<td>ROC Plotter</td>
			<td>&#160;Department of Bioinformatics of the Semmelweis University</td>
			<td></td>
			<td>Validation of the interest gene as a predictive marker (http://www.rocplot.org/)</td>
		</tr>
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			<td>STRING</td>
			<td>Swiss Institute of Bioinformatics</td>
			<td></td>
			<td>Coexpression networks analysis(https://string-db.org)</td>
		</tr>
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			<td>TargetScan</td>
			<td>Whitehead Institute for Biomedical Research</td>
			<td></td>
			<td>Prediction of miRNA targets (www.targetscan.org/)</td>
		</tr>
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			<td>TIMER</td>
			<td>Harvard University</td>
			<td></td>
			<td>Systematical analysis of immune infiltrates across diverse cancer types (https://cistrome.shinyapps.io/timer/)</td>
		</tr>
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			<td>TISIDB</td>
			<td>The University of Hong Kong</td>
			<td></td>
			<td>A web portal for tumor and immune system interaction(http://cis.hku.hk/TISIDB/)</td>
		</tr>
		<tr>
			<td>TNMplot</td>
			<td>Department of Bioinformatics of the Semmelweis University</td>
			<td></td>
			<td>Gene expression analysis (https://www.tnmplot.com/ )</td>
		</tr>
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			<td>UALCAN</td>
			<td>The University of ALabama at Birmingham&#160;</td>
			<td></td>
			<td>Gene expression analysis (http://ualcan.path.uab.edu)</td>
		</tr>
	</tbody>
</table>

tropomodulin 3 overexpression as a marker for platinum resistance and immune infiltration in ovarian cancer

<meta charset="utf8"></head><body>作者聚焦：揭示 TMOD3 在卵巢癌铂耐药和免疫浸润中的作用

原调节蛋白 3 过表达作为卵巢癌铂耐药和免疫浸润的标志物

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

tropomodulin-3-overexpression-as-marker-for-platinum-resistance

Research

JoVE Journal

Cancer Research

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

2.5K 次观看.  Yangzhou Polytechnic College. 我们的研究重点是卵巢癌的耐药性。在我们的论文中，我们试图揭示 TMOD3 在卵巢癌和化疗中的作用。许多细胞日历蛋白已被证明在卵巢癌和耐药性中起重要作用，但仍然没有关于 TMOD3、卵巢癌和 sim 疗法作用的报道。我们的研究首次报道了 TMOD3 在卵巢癌上高表达，并与铂耐药和免疫浸润密切相关。这一结果有助于改善卵巢癌的治疗结果。

视频: 作者聚焦：揭示 TMOD3 在卵巢癌铂耐药和免疫浸润中的作用

Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for immunotherapies. Recent clinical advances in immunotherapy highlight the need for reproducible methods to accurately and thoroughly characterize the tumor and its associated immune infiltrate. Tumor enzymatic digestion and flow cytometric analysis allow broad characterization of numerous immune cell subsets and phenotypes; however, depth of analysis is often limited by fluorophore restrictions on panel design and the need to acquire large tumor samples to observe rare immune populations of interest. Thus, we have developed an effective and high throughput method for separating and enriching the tumor immune infiltrate from the non-immune tumor components. The described tumor digestion and centrifugal density-based separation technique allows separate characterization of tumor and tumor immune infiltrate fractions and preserves cellular viability, and thus, provides a broad characterization of the tumor immunologic state. This method was used to characterize the extensive spatial immune heterogeneity in solid tumors, which further demonstrates the need for consistent whole tumor immunologic profiling techniques. Overall, this method provides an effective and adaptable technique for the immunologic characterization of subcutaneous solid murine tumors; as such, this tool can be used to better characterize the tumoral immunologic features and in the preclinical evaluation of novel immunotherapeutic strategies.

Here we describe a method to separate and enrich components of the tumor immune and non-immune microenvironment in established subcutaneous tumors. This technique allows for the separate analysis of tumor immune infiltrate and non-immune tumor fractions which can permit comprehensive characterization of the tumor immune microenvironment.

肿瘤免疫和非免疫微环境在小鼠皮下肿瘤中的富集与鉴定

The tumor immune microenvironment (TIME) has recently been recognized as a critical mediator of treatment response in solid tumors, especially for ...

科研

癌症研究

3-D Cell Culture System for Studying Invasion and Evaluating Therapeutics in Bladder Cancer

Bladder cancer is a significant health problem. It is estimated that more than 16,000 people will die this year in the United States from bladder cancer. While 75% of bladder cancers are non-invasive and unlikely to metastasize, about 25% progress to an invasive growth pattern. Up to half of the patients with invasive cancers will develop lethal metastatic relapse. Thus, understanding the mechanism of invasive progression in bladder cancer is crucial to predict patient outcomes and prevent lethal metastases. In this article, we present a three-dimensional cancer invasion model which allows incorporation of tumor cells and stromal components to mimic in vivo conditions occurring in the bladder tumor microenvironment. This model provides the opportunity to observe the invasive process in real time using time-lapse imaging, interrogate the molecular pathways involved using confocal immunofluorescent imaging and screen compounds with the potential to block invasion. While this protocol focuses on bladder cancer, it is likely that similar methods could be used to examine invasion and motility in other tumor types as well.

The processes governing bladder cancer invasion represent opportunities for biomarker and therapeutic development. Here we present a bladder cancer invasion model which incorporates 3-D culture of tumor spheroids, time-lapse imaging and confocal microscopy. This technique is useful for defining the features of the invasive process and for screening therapeutic agents.

3维细胞培养系统研究膀胱癌侵袭和评价治疗方法

Bladder cancer is a significant health problem. It is estimated that more than 16,000 people will die this year in the United States from bladder cancer. ...

Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism. Most ovarian cancers are diagnosed in the advanced stage, which seriously hampers therapy. Mitochondrial changes are a hallmark of human ovarian cancers, and mitochondria are the centers of energy metabolism, cell signaling, and oxidative stress. In-depth insights into the changes of the mitochondrial proteome in ovarian cancers compared to control ovarian tissue will benefit in-depth understanding of the molecular mechanisms of ovarian cancer, and the discovery of effective and reliable biomarkers and therapeutic targets. An effective mitochondrial preparation method coupled with an isobaric tag for relative and absolute quantification (iTRAQ) quantitative proteomics are presented here to analyze human ovarian cancer and control mitochondrial proteomes, including differential-speed centrifugation, density gradient centrifugation, quality assessment of mitochondrial samples, protein digestion with trypsin, iTRAQ labeling, strong cation exchange fractionation (SCX), liquid chromatography (LC), tandem mass spectrometry (MS/MS), database analysis, and quantitative analysis of mitochondrial proteins. Many proteins have been successfully identified to maximize the coverage of the human ovarian cancer mitochondrial proteome and to achieve the differentially expressed mitochondrial protein profile in human ovarian cancers.

This article presents a protocol of differential-speed centrifugation in combination with density gradient centrifugation to separate mitochondria from human ovarian cancer tissues and control ovarian tissues for quantitative proteomics analysis, resulting in a high-quality mitochondrial sample and high-throughput and high-reproducibility quantitative proteomics analysis of a human ovarian cancer mitochondrial proteome.

卵巢癌组织线粒体的制备与控制卵巢组织进行定量蛋白质组学分析

Ovarian cancer is a common gynecologic cancer with high mortality but unclear molecular mechanism. Most ovarian cancers are diagnosed in the advanced ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

实验鼠模型中骨代谢的早期检测机器学习算法

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable preclinical models that can accurately predict patient responses. One of the disadvantages of existing preclinical models is the inability to contextually preserve the human tumor microenvironment (TME) and accurately represent intratumoral heterogeneity, thus limiting the clinical translation of data. By contrast, by representing the culture of live fragments of human tumors, the patient-derived explant (PDE) platform allows drug responses to be examined in a three-dimensional (3D) context that mirrors the pathological and architectural features of the original tumors as closely as possible. Previous reports with PDEs have documented the ability of the platform to distinguish chemosensitive from chemoresistant tumors, and it has been shown that this segregation is predictive of patient responses to the same chemotherapies. Simultaneously, PDEs allow the opportunity to interrogate molecular, genetic, and histological features of tumors that predict drug responses, thereby identifying biomarkers for patient stratification as well as novel interventional approaches to sensitize resistant tumors. This paper reports PDE methodology in detail, from collection of patient samples through to endpoint analysis. It provides a detailed description of explant derivation and culture methods, highlighting bespoke conditions for particular tumors, where appropriate. For endpoint analysis, there is a focus on multiplexed immunofluorescence and multispectral imaging for the spatial profiling of key biomarkers within both tumoral and stromal regions. By combining these methods, it is possible to generate quantitative and qualitative drug response data that can be related to various clinicopathological parameters and thus potentially be used for biomarker identification.

This paper describes methods for the generation, drug treatment, and analysis of patient-derived explants for assessing tumor drug responses in a live, patient-relevant, preclinical model system.

患者源性肿瘤外植体作为预测患者耐药性的"实时"临床前平台

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable ...

Generation and Culturing of High-Grade Serous Ovarian Cancer Patient-Derived Organoids

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the discovery of novel therapeutics and predictive biomarkers for ovarian cancer. These models recapitulate clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Additionally, they have been shown to match the primary tumor morphologically, cytologically, immunohistochemically, and genetically. Thus, organoids facilitate research on tumor cells and the tumor microenvironment and are superior to cell lines. The present protocol describes distinct methods to generate patient-derived ovarian cancer organoids from patient tumors, ascites, and pleural fluid samples with a higher than 97% success rate. The patient samples are separated into cellular suspensions by both mechanical and enzymatic digestion. The cells are then plated utilizing a basement membrane extract (BME) and are supported with optimized growth media containing supplements specific to the culturing of high-grade serous ovarian cancer (HGSOC). After forming initial organoids, the PDOs can sustain long-term culture, including passaging for expansion for subsequent experiments.

Patient-derived organoids (PDO) are a three-dimensional (3D) culture that can mimic the tumor environment in vitro. In high-grade serous ovarian cancer, PDOs represent a model to study novel biomarkers and therapeutics.

高级别浆液性卵巢癌患者来源类器官的生成和培养

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the ...

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

Immunofluorescence is one of the most widely used techniques to visualize target antigens with high sensitivity and specificity, allowing for the accurate identification and localization of proteins, glycans, and small molecules. While this technique is well-established in two-dimensional (2D) cell culture, less is known about its use in three-dimensional (3D) cell models. Ovarian cancer organoids are 3D tumor models that recapitulate tumor cell clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Thus, they are superior to cell lines for the evaluation of drug sensitivity and functional biomarkers. Therefore, the ability to utilize immunofluorescence on primary ovarian cancer organoids is extremely beneficial in understanding the biology of this cancer. The current study describes the technique of immunofluorescence to detect DNA damage repair proteins in high-grade serous patient-derived ovarian cancer organoids (PDOs). After exposing the PDOs to ionizing radiation, immunofluorescence is performed on intact organoids to evaluate nuclear proteins as foci. Images are collected using z-stack imaging on confocal microscopy and analyzed using automated foci counting software. The described methods allow for the analysis of temporal and special recruitment of DNA damage repair proteins and colocalization of these proteins with cell-cycle markers.

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

The present protocol describes methods for evaluating DNA damage repair proteins in patient-derived ovarian cancer organoids. Included here are comprehensive plating and staining methods, as well as detailed, objective quantification procedures.

通过免疫荧光测定 可视化 患者来源的卵巢癌类器官中的 DNA 损伤修复蛋白

Immunofluorescence is one of the most widely used techniques to visualize target antigens with high sensitivity and specificity, allowing for the accurate ...

Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments is crucial to advancing healthcare and improving patient outcomes. Organoids are in-vitro three-dimensional multicellular miniature organs. Patient-derived organoid (PDO) models of ovarian cancer may be optimal for drug screening because they more accurately recapitulate tissues of interest than two-dimensional cell culture models and are inexpensive compared to patient-derived xenografts. In addition, ovarian cancer PDOs mimic the variable tumor microenvironment and genetic background typically observed in ovarian cancer. Here, a method is described that can be used to test conventional and novel drugs on PDOs derived from ovarian cancer tissue and ascites. A luminescence-based adenosine triphosphate (ATP) assay is used to measure viability, growth rate, and drug sensitivity. Drug screens in PDOs can be completed in 7-10 days, depending on the rate of organoid formation and drug treatments.

We present a protocol that can be used to conduct therapeutic drug testing with patient-derived ovarian cancer organoids.

用于临床前药物测试的卵巢癌患者来源的类器官模型

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments ...

TMEM200A：一种在胃癌中调节 EMT 和 PI3K/AKT 的新型癌基因

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.

作者聚焦：揭示胃癌中跨膜蛋白家族相关标志物及其对靶向治疗的影响 

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.

TMEM200A作为泛癌生物标志物的多组学分析

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