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Erratum to "Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression". “乳腺癌居民t细胞受体CDR3结构域和癌症抗原ARMC3的化学互补性与更高的生存率和颗粒酶表达水平相关”的勘误。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231189051

[This corrects the article DOI: 10.1177/11769351231177269.].

[这更正了文章DOI: 10.1177/11769351231177269.]。
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引用次数: 0
Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications. 基于监督机器学习算法的简易癌症分类决策支持系统和web应用。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351221147244
K Chandrashekar, Anagha S Setlur, Adithya Sabhapathi C, Satyam Suresh Raiker, Satyam Singh, Vidya Niranjan

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

使用对各种癌症进行分类的决策支持系统(DSS)为临床医生/研究人员提供支持,帮助他们做出更好的决策,从而有助于早期癌症诊断,从而减少错误疾病诊断的机会。因此,本工作旨在设计一个分类模型,该模型可以使用从全外显子组癌症分析中鉴定的突变,准确预测由20个癌症外显子组组成的5种不同癌症类型。首先,使用k近邻(KNN)、支持向量机(SVM)、决策树、naïve贝叶斯和随机森林(RF)等监督机器学习分类算法设计基本模型,其中决策树和随机森林在模型初步精度上表现较好。然而,由于训练分数较少,输出预测是不正确的。因此,然后选择16个基本特征,使用2种方法进行模型改进。使用SMOTE对所有不平衡数据集进行平衡。在第一种方法中,对来自20个癌症外显子组数据集的所有特征进行训练,并使用决策树和随机森林设计模型。决策树模型的平衡数据集显示准确率为77%,而RF模型的准确率提高到82%,其中所有5种癌症类型都被正确预测。与决策树模型相比,射频模型的曲线下面积更接近于1。在第二种方法中,所有15个数据集都进行了训练,而5个数据集进行了测试。然而,只有两种癌症类型预测正确。为了交叉验证RF模型,采用Matthew’s相关系数检验(MCC)。方法1的MCC检验和MCC交叉验证分别为0.7796和0.9356。同样,对于第二种方法,观察到MCC为0.9365,证实了设计模型的准确性。该模型已成功部署,使用Streamlit作为web应用程序,方便使用。这项研究为简化癌症分类提供了见解。
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引用次数: 1
TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma. TP53及其调控基因与皮肤黑色素瘤预后的关系。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231177267
Safir Ullah Khan, Zahid Ullah, Hadia Shaukat, Sheeza Unab, Saba Jannat, Waqar Ali, Amir Ali, Muhammad Irfan, Muhammad Fiaz Khan, Rodolfo Daniel Cervantes-Villagrana

The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with TP53 followed by CDKN2A being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed that TP53, MDM2, CDKN2A, and TP53BP1 were overexpressed, while MDM4 and CDKN2B were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, CDKN2A was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the MDM4 was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. MDM2 expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. TP53 showed a positive correlation with B cells, MDM2 with CD8+T cells, macrophages and neutrophils, and MDM4 with neutrophils. CDKN2A/B had a non-significant correlation with all six types of immune cells. However, TP53BP1 was positively correlated with all five types of immune cells except B cells. Only TP53, MDM2, and CDKN2A had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.

本研究首次通过提供大型数据集的各种遗传数据库对皮肤黑色素瘤中p53信号基因的基因突变和表达水平进行了全面研究。p53及其信号基因的突变情况比预期的要高,TP53其次是CDKN2A是皮肤黑色素瘤中突变最多的基因。此外,表达分析显示TP53、MDM2、CDKN2A和TP53BP1在皮肤黑色素瘤中过表达,而MDM4和CDKN2B在皮肤黑色素瘤中过表达。总体而言,TCGA数据显示,在所有其他p53信号蛋白中,CDKN2A在阳光照射和非阳光照射的健康组织中均显著高于黑色素瘤。同样,与其他组相比,MDM4和TP53BP1在非阳光照射的健康组织中的表达明显更高。然而,CDKN2B在阳光照射的健康组织中的表达高于其他组织。此外,各基因在雄性和雌性之间的表达也存在显著差异。此外,CDKN2A在SK-MEL-30皮肤癌细胞系中高表达,而免疫细胞类型表达分析显示MDM4在naïve b细胞中高表达。此外,与健康组织相比,所有六个基因在异常超重或肥胖的肿瘤组织中都显着过表达。MDM2的表达与肿瘤分期密切相关。不同患者年龄组和阳性淋巴结状态的基因表达存在差异。TP53与B细胞呈正相关,MDM2与CD8+T细胞、巨噬细胞、中性粒细胞呈正相关,MDM4与中性粒细胞呈正相关。CDKN2A/B与所有六种免疫细胞均无显著相关性。而TP53BP1与除B细胞外的5种免疫细胞均呈正相关。只有TP53、MDM2和CDKN2A在皮肤黑色素瘤特异性肿瘤免疫中起作用。所有TP53及其调控基因均可预测预后。目前研究的结果需要通过未来的筛选、体内和体外研究来验证。
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引用次数: 0
Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program. 在我们所有人的研究项目中,乳腺癌和胆固醇之间关系的可视化和量化。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351221144132
Jianglin Feng, Esteban Astiazaran Symonds, Jason H Karnes

Epidemiologic evidence for the association of cholesterol and breast cancer is inconsistent. Several factors may contribute to this inconsistency, including limited sample sizes, confounding effects of antihyperlipidemic treatment, age, and body mass index, and the assumption that the association follows a simple linear function. Here, we aimed to address these factors by combining visualization and quantification a large-scale contemporary electronic health record database (the All of Us Research Program). We find clear visual and quantitative evidence that breast cancer is strongly, positively, and near-linearly associated with total cholesterol and low-density lipoprotein cholesterol, but not associated with triglycerides. The association of breast cancer with high-density lipoprotein cholesterol was non-linear and age dependent. Standardized odds ratios were 2.12 (95% confidence interval 1.9-2.48), P = 5.6 × 10-31 for total cholesterol; 1.99 (1.75-2.26), P = 2.6 × 10-26 for low-density lipoprotein cholesterol; 1.69 (1.3-2.2), P = 9.0 × 10-5 for high-density lipoprotein cholesterol at age < 56; and 0.65 (0.55-0.78), P = 1.2 × 10-6 for high-density lipoprotein cholesterol at age ⩾ 56. The inclusion of the lipid levels measured after antihyperlipidemic treatment in the analysis results in erroneous associations. We demonstrate that the use of the logistic regression without inspecting risk variable linearity and accounting for confounding effects may lead to inconsistent results.

关于胆固醇和乳腺癌之间关系的流行病学证据并不一致。有几个因素可能导致这种不一致,包括有限的样本量、抗高脂血症治疗的混杂效应、年龄和体重指数,以及这种关联遵循简单线性函数的假设。在这里,我们的目标是通过结合可视化和量化大型现代电子健康记录数据库(我们所有人研究计划)来解决这些因素。我们发现清晰的视觉和定量证据表明,乳腺癌与总胆固醇和低密度脂蛋白胆固醇呈强烈、积极和近线性相关,但与甘油三酯无关。乳腺癌与高密度脂蛋白胆固醇的关系是非线性和年龄相关的。总胆固醇的标准化优势比为2.12(95%可信区间为1.9-2.48),P = 5.6 × 10-31;低密度脂蛋白胆固醇1.99 (1.75-2.26),P = 2.6 × 10-26;1.69(1.3-2.2),年龄时高密度脂蛋白胆固醇的P = 9.0 × 10-5,年龄大于或等于56岁时高密度脂蛋白胆固醇的P = 1.2 × 10-6。在分析中纳入抗高脂血症治疗后测量的脂质水平会导致错误的关联。我们证明,使用逻辑回归而不检查风险变量线性和考虑混杂效应可能导致不一致的结果。
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引用次数: 1
Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud. 多组学途径工作流(MOPAW):癌症基因组云上的自动化多组学工作流。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231180992
Trinh Nguyen, Xiaopeng Bian, David Roberson, Rakesh Khanna, Qingrong Chen, Chunhua Yan, Rowan Beck, Zelia Worman, Daoud Meerzaman

Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently.

Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow.

Results: The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing.

Conclusion: Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

导读:在大数据时代,多组学衍生的基因集通路分析异常强大。在准备和分析高维多组学数据时,使用现有工具所需的安装过程和编程技能可能具有挑战性。对于那些不熟悉编码的人来说尤其如此。此外,需要使用高性能计算解决方案来实现高效运行这些工具。方法:我们引入了一个自动的多组学通路工作流,一个点按式图形用户界面,用于多变量单样本基因集分析(MOGSA),该分析由Seven Bridges Genomics托管在Cancer Genomics Cloud上。该工作流利用不同工具的组合来为每个给定的数据类型、降维和MOGSA路径分析执行数据准备。组学数据包括拷贝数改变、转录组学数据、蛋白质组学和磷蛋白质组学数据。我们还提供了一个额外的工作流程来帮助从癌症基因组图谱和临床蛋白质组学肿瘤分析协会下载数据,并对这些数据进行预处理,以用于多组学途径工作流程。结果:此工作流的主要输出是用户提供的感兴趣的子组的不同路径,如果确定,则显示在热图中。除此之外,还提供图形和表格供用户查看。结论:Multi-omics Pathway Workflow不需要编码经验。用户可以带来他们自己的数据,或者下载和预处理来自癌症基因组图谱和临床蛋白质组学肿瘤分析联盟的公共数据集,使用我们基于感兴趣样本的额外工作流程。对于感兴趣的群体,可以发现不同的过度激活或不激活的途径。这些有用的信息对于有效的靶向治疗非常重要。
{"title":"Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.","authors":"Trinh Nguyen,&nbsp;Xiaopeng Bian,&nbsp;David Roberson,&nbsp;Rakesh Khanna,&nbsp;Qingrong Chen,&nbsp;Chunhua Yan,&nbsp;Rowan Beck,&nbsp;Zelia Worman,&nbsp;Daoud Meerzaman","doi":"10.1177/11769351231180992","DOIUrl":"https://doi.org/10.1177/11769351231180992","url":null,"abstract":"<p><strong>Introduction: </strong>In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently.</p><p><strong>Methods: </strong>We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow.</p><p><strong>Results: </strong>The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing.</p><p><strong>Conclusion: </strong>Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/28/1c/10.1177_11769351231180992.PMC10278438.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9707715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cell Adaptive Fitness and Cancer Evolutionary Dynamics. 细胞适应适应度和癌症进化动力学。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231154679
Youcef Derbal
Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.
癌细胞基因组的不稳定性转化为熵的增加和信息处理能力的降低,导致代谢重编程向更高的能量状态发展,据推测这与癌症生长的必要性是一致的。这一命题被称为细胞适应性适应度,它假设细胞信号传导和代谢之间的耦合限制了癌症沿着维持生存所需的代谢充足性的轨迹进化动力学。特别是,该猜想假设,当基因改变在调节信号网络中引起足够高水平的紊乱(即高熵)时,克隆扩展受到限制,从而取消癌细胞成功复制的能力,导致克隆停滞阶段。该命题在肿瘤进化动力学的计算机模型的背景下进行了分析,以说明细胞固有的适应适应度如何可预测地约束肿瘤的克隆进化,这将对适应性癌症治疗的设计具有重要意义。
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引用次数: 0
In Silico Analysis of MicroRNA Expression Data in Liver Cancer. 肝癌组织MicroRNA表达数据的计算机分析。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231171743
Nourhan Abu-Shahba, Elsayed Hegazy, Faiz M Khan, Mahmoud Elhefnawi

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

异常miRNA表达已被证明与HCC的发生和发展直接相关。本研究旨在通过mirna表达的计算分析来检测HCC可能的预后、诊断和/或治疗mirna。方法:采用YM500v2服务器对miRNA表达数据集进行meta分析,比较正常和癌变肝组织中miRNA的表达。我们研究中最显著的差异调节mirna使用mirWalk工具进行靶基因分析,以获得其验证和预测的靶标。组合目标预测工具;miRror Suite用于获得共同调控的靶基因。使用DAVID工具对得到的目标进行功能富集分析。基于microrna、它们的靶标和转录因子之间的相互作用,构建了一个网络。使用网络拓扑分析确定了集线器节点和守门人。此外,我们根据确定的枢纽和守门人节点的低表达和高表达进行了患者数据生存分析,将患者分为低和高生存概率组。结果:在YM500v2服务器中使用荟萃分析选项,发现34个mirna受到显著差异调节(p值≥0.05);5个mirna下调,29个mirna上调。得到了每个miRNA的验证和预测的靶基因,以及组合预测的靶基因。DAVID富集分析得出了几个与主要癌症标志直接相关的重要细胞功能。这些功能包括局灶黏附、细胞周期、PI3K-Akt信号通路、胰岛素信号通路、Ras和MAPK信号通路。几个枢纽基因和看门人被发现可以作为肝细胞癌的潜在药物靶点。在HCC患者中,POU2F1和PPARA在低生存率和高生存率之间存在显著差异(p值≤0.05)。我们的研究揭示了肝细胞癌的重要生物标志物mirna及其靶基因和调控功能。
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引用次数: 0
Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model. 使用惩罚加性风险回归模型识别乳腺癌转移的预后生物标志物。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231157942
Leili Tapak, Omid Hamidi, Payam Amini, Saeid Afshar, Siamak Salimy, Irina Dinu

Background: Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.

Methods: A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes.

Results: Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis.

Conclusion: This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.

背景:乳腺癌(BC)已被报道为全世界女性最常见的癌症之一。BC患者的生存率受转移的影响。因此,探索其潜在机制和识别相关生物标志物,利用新的统计方法监测BC复发/复发是必要的。本研究使用惩罚加性风险回归模型研究了BC患者的高维基因表达谱。方法:使用与BC患者转移时间相关的公开数据集(GSE2034)。286例BC患者共获得22 283个基因表达谱信息。采用LASSO、SCAD、SICA、MCP和Elastic net等惩罚性加性风险回归模型识别转移相关基因。结果:在加性危害模型中应用了5个带惩罚的回归模型,共发现了SNU13、CLINT1、MAPK9、ABCC5、NKX3-1、NCOR2、COL2A1、ZNF219等9个基因。根据惩罚加性危险模型回归系数计算的预后指数中位数,将患者标记为高/低风险组。鉴定组的生存曲线有显著差异。选择的基因使用验证数据进行检查,并与转移的危险显著相关。结论:本研究显示MAPK9、NKX3-1、NCOR1、ABCC5和CD44是乳腺癌复发和转移的潜在预测因子,可作为进一步研究乳腺癌发生、侵袭、转移和上皮间质转移的候选因子。
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引用次数: 0
RNA-seq and Single-Cell Transcriptome Analyses of TRAIL Receptors Gene Expression in Human Osteosarcoma Cells and Tissues. 人骨肉瘤细胞和组织中TRAIL受体基因表达的RNA-seq和单细胞转录组分析。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231161478
Wenyu Feng, Haiyingjie Lin, Emel Rothzerg, Dezhi Song, Wenxiang Zhao, Tingting Ning, Qingjun Wei, Jinmin Zhao, David Wood, Yun Liu, Jiake Xu

Osteosarcoma (OS) is the most common primary cancer in the skeletal system, characterized by a high incidence of lung metastasis, local recurrence and death. Systemic treatment of this aggressive cancer has not improved significantly since the introduction of chemotherapy regimens, underscoring a critical need for new treatment strategies. TRAIL receptors have long been proposed to be therapeutic targets for cancer treatment, but their role in osteosarcoma remains unclear. In this study, we investigated the expression profile of four TRAIL receptors in human OS cells using total RNA-seq and single-cell RNA-seq (scRNA-seq). The results revealed that TNFRSF10B and TNFRSF10D but not TNFRSF10A and TNFRSF10C are differentially expressed in human OS cells as compared to normal cells. At the single cell level by scRNA-seq analyses, TNFRSF10B, TNFRSF10D, TNFRSF10A and TNFRSF10C are most abundantly expressed in endothelial cells of OS tissues among nine distinct cell clusters. Notably, in osteoblastic OS cells, TNFRSF10B is most abundantly expressed, followed by TNFRSF10D, TNFRSF10A and TNFRSF10C. Similarly, in an OS cell line U2-OS using RNA-seq, TNFRSF10B is most abundantly expressed, followed by TNFRSF10D, TNFRSF10A and TNFRSF10C. According to the TARGET online database, poor patient outcomes were associated with low expression of TNFRSF10C. These results could provide a new perspective to design novel therapeutic targets of TRAIL receptors for the diagnosis, prognosis and treatment of OS and other cancers.

骨肉瘤(Osteosarcoma, OS)是骨骼系统中最常见的原发肿瘤,其特点是肺转移、局部复发和死亡的发生率高。自从引入化疗方案以来,这种侵袭性癌症的全身治疗并没有显著改善,这强调了对新治疗策略的迫切需要。TRAIL受体长期以来被认为是癌症治疗的靶点,但其在骨肉瘤中的作用尚不清楚。在这项研究中,我们使用总RNA-seq和单细胞RNA-seq (scRNA-seq)研究了四种TRAIL受体在人OS细胞中的表达谱。结果显示,与正常细胞相比,TNFRSF10B和TNFRSF10D在人OS细胞中存在差异表达,而TNFRSF10A和TNFRSF10C不存在差异表达。在单细胞水平上,通过scRNA-seq分析,在9个不同的细胞簇中,TNFRSF10B、TNFRSF10D、TNFRSF10A和TNFRSF10C在OS组织内皮细胞中表达量最高。值得注意的是,在成骨OS细胞中,TNFRSF10B的表达量最高,其次是TNFRSF10D、TNFRSF10A和TNFRSF10C。同样,在使用RNA-seq的OS细胞系U2-OS中,TNFRSF10B的表达量最高,其次是TNFRSF10D、TNFRSF10A和TNFRSF10C。根据TARGET在线数据库,不良的患者预后与TNFRSF10C的低表达有关。这些结果可以为设计新的TRAIL受体治疗靶点为OS等肿瘤的诊断、预后和治疗提供新的视角。
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引用次数: 0
Melanoma and Human Leukocyte Antigen (HLA): Immunogenicity of 69 HLA Class I Alleles With 11 Antigens Expressed in Melanoma Tumors. 黑色素瘤与人类白细胞抗原(HLA):在黑色素瘤肿瘤中表达的69个HLA I类等位基因的免疫原性。
IF 2 Q3 Medicine Pub Date : 2023-01-01 DOI: 10.1177/11769351231172604
Apostolos P Georgopoulos, Lisa M James, Spyros A Charonis, Matthew Sanders

Host immunogenetics play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes that stimulate T cell response hinge on binding affinity and immunogenicity of human leukocyte antigen (HLA) with melanoma antigen epitopes. Here, we use an in silico approach to characterize binding affinity and immunogenicity of 69 HLA Class I human leukocyte antigen alleles to epitopes of 11 known melanoma antigens. The findings document a significant proportion of positively immunogenic epitope-allele combinations, with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B and C genes. The findings are discussed in terms of a personalized precision HLA-mediated adjunct to immune checkpoint blockade immunotherapy to maximize tumor elimination.

宿主免疫遗传学在人类对黑色素瘤的免疫反应中起关键作用,影响黑色素瘤的患病率和免疫治疗结果。刺激T细胞应答的有益结果取决于人白细胞抗原(HLA)与黑色素瘤抗原表位的结合亲和力和免疫原性。在这里,我们使用计算机方法来表征69个HLA I类人白细胞抗原等位基因与11种已知黑色素瘤抗原表位的结合亲和力和免疫原性。研究结果表明,免疫原性阳性的表位-等位基因组合占很大比例,其中Q13072/BAGE1黑色素瘤抗原和HLA B和C基因等位基因的免疫原性阳性比例最高。研究结果在个性化的精确hla介导的辅助免疫检查点阻断免疫治疗方面进行了讨论,以最大限度地消除肿瘤。
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引用次数: 3
期刊
Cancer Informatics
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