Relevant bibliographies by topics / The Cancer Genome Atlas (TCGA) dataset

Academic literature on the topic 'The Cancer Genome Atlas (TCGA) dataset'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "The Cancer Genome Atlas (TCGA) dataset"

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Tu, Juchuanli, Xiaolu Li, and Jianjun Wang. "Characterization of bidirectional gene pairs in The Cancer Genome Atlas (TCGA) dataset." PeerJ 7 (June 17, 2019): e7107. http://dx.doi.org/10.7717/peerj.7107.

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The “bidirectional gene pair” indicates a particular head-to-head gene organization in which transcription start sites of two genes are located on opposite strands of genomic DNA within a region of one kb. Despite bidirectional gene pairs are well characterized, little is known about their expression profiles and regulation features in tumorigenesis. We used RNA-seq data from The Cancer Genome Atlas (TCGA) dataset for a systematic analysis of the expression profiles of bidirectional gene pairs in 13 cancer datasets. Gene pairs on the opposite strand with transcription end site distance within one kb or on the same strand with the distance of two genes between 1–10 kb and gene pairs comprising two randomly chosen genes were used as control gene pairs (CG1, CG2, and random). We identified and characterized up-/down-regulated genes by comparing the expression level between tumors and adjacent normal tissues in 13 TCGA datasets. There were no consistently significant difference in the percentage of up-/down-regulated genes between bidirectional and control/random genes in most of TCGA datasets. However, the percentage of bidirectional gene pairs comprising two up- or two down-regulated genes was significantly higher than gene pairs from CG1/2 in 12/11 analyzed TCGA datasets and the random gene pairs in all 13 TCGA datasets. Then we identified the methylation correlated bidirectional genes to explore the regulatory mechanism of bidirectional genes. Like the differentially expressed gene pairs, the bidirectional genes in a pair were significantly prone to be both hypo- or hyper-methylation correlated genes in 12/13 TCGA datasets when comparing to the CG2/random gene pairs despite no significant difference between the percentages of hypo-/hyper-methylation correlated genes in bidirectional and CG2/random genes in most of TCGA datasets. Finally, we explored the correlation between bidirectional genes and patient’s survival, identifying prognostic bidirectional genes and prognostic bidirectional gene pairs in each TCGA dataset. Remarkably, we found a group of prognostic bidirectional gene pairs in which the combination of two protein coding genes with different expression level correlated with different survival prognosis in survival analysis for OS. The percentage of these gene pairs in bidirectional gene pair were significantly higher than the gene pairs in controls in COAD datasets and lower in none of 13 TCGA datasets.
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Neapolitan, Richard, and Xia Jiang. "Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data." Cancer Informatics 13s1 (January 2014): CIN.S13881. http://dx.doi.org/10.4137/cin.s13881.

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This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 ( P = 0.047), but SPIA did not ( P = 0.083).
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Kim, In Ah, and Bum Sup Jang. "TMIC-52. RELATIONSHIP BETWEEN MACROPHAGE AND RADIOSENSITIVITY IN HUMAN PRIMARY AND RECURRENT GLIOBLASTOMA: IN SILICO ANALYSIS WITH PUBLICLY AVAILABLE DATASETS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii283. http://dx.doi.org/10.1093/neuonc/noac209.1096.

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Abstract The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), the Ivy Glioblastoma Atlas Project dataset, a single-cell RNA sequencing dataset (GSE84465), Glioma Longitudinal Analysis Consortium (GLASS), and an immunotherapy trial dataset (GSE121810) were included. RSI-high radioresistant tumors were associated with worse overall survival in TCGA and CGGA than RSI-low tumors. M1/M2 macrophage ratios and RSI scores were inversely associated, indicating that radioresistant glioblastoma tumor microenvironments contain more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the mean RSI of neoplastic cells was positively correlated with high M2 macrophages proportions. A favorable response to programmed cell death protein 1 (PD-1) therapy was observed in recurrent glioblastomas with high M1/M2 macrophage ratios and low RSI scores. In patients with recurrent glioblastoma, fewer M2 macrophages and low RSI scores were associated with improved overall survival. High M2 macrophage proportions may be involved in radioresistant glioblastoma.
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Jang, Bum-Sup, and In Ah Kim. "Relationship between Macrophage and Radiosensitivity in Human Primary and Recurrent Glioblastoma: In Silico Analysis with Publicly Available Datasets." Biomedicines 10, no. 2 (January 27, 2022): 292. http://dx.doi.org/10.3390/biomedicines10020292.

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The glioblastoma microenvironment predominantly contains tumor-associated macrophages that support tumor growth and invasion. We investigated the relationship between tumor radiosensitivity and infiltrating M1/M2 macrophage profiles in public datasets of primary and recurrent glioblastoma. We estimated the radiosensitivity index (RSI) score based on gene expression rankings. Macrophages were profiled using the deconvolution algorithm CIBERSORTx. Samples from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), the Ivy Glioblastoma Atlas Project dataset, a single-cell RNA sequencing dataset (GSE84465), Glioma Longitudinal Analysis Consortium (GLASS), and an immunotherapy trial dataset (GSE121810) were included. RSI-high radioresistant tumors were associated with worse overall survival in TCGA and CGGA than RSI-low tumors. M1/M2 macrophage ratios and RSI scores were inversely associated, indicating that radioresistant glioblastoma tumor microenvironments contain more M2 than M1 macrophages. In the single-cell RNA sequencing dataset, the mean RSI of neoplastic cells was positively correlated with high M2 macrophages proportions. A favorable response to programmed cell death protein 1 (PD-1) therapy was observed in recurrent glioblastomas with high M1/M2 macrophage ratios and low RSI scores. In patients with recurrent glioblastoma, fewer M2 macrophages and low RSI scores were associated with improved overall survival. High M2 macrophage proportions may be involved in radioresistant glioblastoma.
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Torcivia, John, Kawther Abdilleh, Fabian Seidl, Owais Shahzada, Rebecca Rodriguez, David Pot, and Raja Mazumder. "Whole Genome Variant Dataset for Enriching Studies across 18 Different Cancers." Onco 2, no. 2 (June 17, 2022): 129–44. http://dx.doi.org/10.3390/onco2020009.

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Whole genome sequencing (WGS) has helped to revolutionize biology, but the computational challenge remains for extracting valuable inferences from this information. Here, we present the cancer-associated variants from the Cancer Genome Atlas (TCGA) WGS dataset. This set of data will allow cancer researchers to further expand their analysis beyond the exomic regions of the genome to the entire genome. A total of 1342 WGS alignments available from the consortium were processed with VarScan2 and deposited to the NCI Cancer Cloud. The sample set covers 18 different cancers and reveals 157,313,519 pooled (non-unique) cancer-associated single-nucleotide variations (SNVs) across all samples. There was an average of 117,223 SNVs per sample, with a range from 1111 to 775,470 and a standard deviation of 163,273. The dataset was incorporated into BigQuery, which allows for fast access and cross-mapping, which will allow researchers to enrich their current studies with a plethora of newly available genomic data.
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Martino, Francesco, Domenico D. Bloisi, Andrea Pennisi, Mulham Fawakherji, Gennaro Ilardi, Daniela Russo, Daniele Nardi, Stefania Staibano, and Francesco Merolla. "Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images." Applied Sciences 10, no. 22 (November 23, 2020): 8285. http://dx.doi.org/10.3390/app10228285.

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Oral squamous cell carcinoma is the most common oral cancer. In this paper, we present a performance analysis of four different deep learning-based pixel-wise methods for lesion segmentation on oral carcinoma images. Two diverse image datasets, one for training and another one for testing, are used to generate and evaluate the models used for segmenting the images, thus allowing to assess the generalization capability of the considered deep network architectures. An important contribution of this work is the creation of the Oral Cancer Annotated (ORCA) dataset, containing ground-truth data derived from the well-known Cancer Genome Atlas (TCGA) dataset.
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Zhou, Weige, Shijing Zhang, Zheyou Cai, Fei Gao, Wenhui Deng, Yi Wen, Zhen-wen Qiu, Zheng-kun Hou, and Xin-Lin Chen. "A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma." PeerJ 8 (September 29, 2020): e9944. http://dx.doi.org/10.7717/peerj.9944.

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Background Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis‑related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC. Methods Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets. Results Seventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e−14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e−02, and Asian: P = 6.342e−08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets. Conclusion The seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.
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Miller, Marina, Eric Devor, Erin Salinas, Andreea Newtson, Michael Goodheart, Kimberly Leslie, and Jesus Gonzalez-Bosquet. "Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA." International Journal of Molecular Sciences 20, no. 5 (March 8, 2019): 1192. http://dx.doi.org/10.3390/ijms20051192.

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In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4–6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.
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Sorgini, Alana, Hugh Andrew Jinwook Kim, Peter Y. F. Zeng, Mushfiq Hassan Shaikh, Neil Mundi, Farhad Ghasemi, Eric Di Gravio, et al. "Analysis of the TCGA Dataset Reveals that Subsites of Laryngeal Squamous Cell Carcinoma Are Molecularly Distinct." Cancers 13, no. 1 (December 31, 2020): 105. http://dx.doi.org/10.3390/cancers13010105.

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Laryngeal squamous cell carcinoma (LSCC) from different subsites have distinct presentations and prognosis. In this study, we carried out a multiomic comparison of LSCC subsites. The Cancer Genome Atlas (TCGA) LSCC cohort was analyzed in the R statistical environment for differences between supraglottic and glottic cancers in single nucleotide variations (SNVs), copy number alterations (CNAs), mRNA abundance, protein abundance, pathway overrepresentation, tumor microenvironment (TME), hypoxia status, and patient outcome. Supraglottic cancers had significantly higher overall and smoking-associated SNV mutational load. Pathway analysis revealed upregulation of muscle related pathways in glottic cancer and neural pathways in supraglottic cancer. Proteins involved in cancer relevant signaling pathways including PI3K/Akt/mTOR, the cell cycle, and PDL1 were differentially abundant between subsites. Glottic and supraglottic tumors have different molecular profiles, which may partially account for differences in presentation and response to therapy.
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Jang, Bum-Sup, and In Ah Kim. "TAMI-32. CORRELATION BETWEEN RADIOSENSITIVITY INDEX AND M2 MACROPHAGE PROPORTION IN TUMOR MICROENVIRONMENT OF GLIOBLASTOMA." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi204—vi205. http://dx.doi.org/10.1093/neuonc/noab196.816.

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Abstract BACKGROUND Tumor-associated macrophages (TAMs) Macrophage are predominant in glioblastoma tumor microenvironment (TME), supporting for neoplastic cell expansion and invasion. We investigated the relationship between radiosensitivity of glioblastoma and M1/M2 macrophage profiles in bulk and single cell RNA sequencing datasets. METHODS We used radiosensitivity index (RSI) gene signature and estimated RSI score based on the ranking of genes by expression level. Two large glioma datasets – The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) – were employed to identify whether RSI is clinically predictive of overall survival following radiation therapy. To analyze the association between M1/M2 macrophages and RSI within spatial context, the Ivy Glioblastoma Atlas Project dataset was investigated and single cell RNA sequencing dataset (GSE84465) was analyzed as well. Macrophages were profiled using a deconvolution algorithm, CIBERSORTx. RESULTS The RSI-high group having radioresistant tumors showed worse overall survival than the RSI-low group in both the TCGA (HR=1.87, 95% CI=1.06-3.29, P=0.031) and the CGGA (HR=1.61, 95% CI=1.04-2.50, P=0.031) glioblastoma population. In the Ivy Glioblastoma Atlas Project dataset, radiosensitive tumor having lower RSI was significantly more found in more vascular region including hyperplastic and microvascular region (coefficient=-0.07, P=0.001), meanwhile, radioresistant tumor was significantly clustered in necrotic region including perinecrotic and pseudopalisading regions (coefficient=0.07, P< 0.001). The proportion of M1/M2 macrophage and RSI score showed an inverse relationship (coefficient=-0.23, P=0.015), indicating that radioresistant glioblastomas are related with TME having more M2 than M1 macrophage. In single cell RNA sequencing dataset composed of immune and tumor cells collected from four patients, mean RSI of neoplastic cells was positively correlated with high proportion of M2 macrophages. CONCLUSION RSI can predict radiation response in terms of overall survival in glioblastoma patients. High proportion of M2 macrophage may play an important role in TME of radioresistant glioblastoma.
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Dissertations / Theses on the topic "The Cancer Genome Atlas (TCGA) dataset"

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Pavlik, Aaron, Phillip Schneider, and Cheryl Cropp. "Proposing Molecularly Targeted Therapies Using an Annotated Drug Database Querying Algorithm in Cutaneous Melanoma." The University of Arizona, 2015. http://hdl.handle.net/10150/614155.

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Class of 2015 Abstract
Objectives: The aim of this study was to develop a computational process capable of hypothesizing potential chemotherapeutic agents for the treatment of skin cutaneous melanoma given an annotated chemotherapy molecular target database and patient-specific genetic tumor profiles. Methods: Aberrational profiles for a total of 246 melanoma patients indexed by the Cancer Genome Atlas (TCGA) for whom complete somatic mutational, mRNA expression, and protein expression data was available were queried against an annotated targeted therapy database using Visual Basic for Applications and Python in conjunction with Microsoft Excel. Identities of positively and negatively associated therapy-profile matches were collected and ranked. Results: Subjects included in the analysis were predominantly Caucasian (93%), non-Hispanic (95.9%), female (59%), and characterized as having stage III clinical disease (37.4%). The most frequently occurring positive and negative therapy associations were determined to be 17-AAG (tanespimycin; 42.3%) and sorafenib (41.9%), respectively. Mean total therapy hypotheses per patient did not differ significantly with regard to either positive or negative associations (p=0.1951 and 0.4739 by one-way ANOVA, respectively) when stratified by clinical melanoma stage. Conclusions: The developed process does not appear to offer discernably different therapy hypotheses amongst clinical stages of cutaneous melanoma based upon genetic data alone. The therapy-matching algorithm may be useful in quickly retrieving potential therapy hypotheses based upon the genetic characteristics of one or many subjects specified by the user.
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Saleem, Muhammad, Shanmukha S. Padmanabhuni, Ngomo Axel-Cyrille Ngonga, Aftab Iqbal, Jonas S. Almeida, Stefan Decker, and Helena F. Deus. "TopFed: TCGA tailored federated query processing and linking to LOD." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-157845.

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Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.
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Saleem, Muhammad, Shanmukha S. Padmanabhuni, Ngomo Axel-Cyrille Ngonga, Aftab Iqbal, Jonas S. Almeida, Stefan Decker, and Helena F. Deus. "TopFed: TCGA tailored federated query processing and linking to LOD." Journal of Biomedical Semantics 2014, 5:47 doi:10.1186/2041-1480-5-47, 2014. https://ul.qucosa.de/id/qucosa%3A13048.

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Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.
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Rendleman, Michael. "Machine learning with the cancer genome atlas head and neck squamous cell carcinoma dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality." Thesis, University of Iowa, 2019. https://ir.uiowa.edu/etd/6841.

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In recent years, more data is becoming available for historical oncology case analysis. A large dataset that describes over 500 patient cases of Head and Neck Squamous Cell Carcinoma is a potential goldmine for finding ways to improve oncological decision support. Unfortunately, the best approaches for finding useful inferences are unknown. With so much information, from DNA and RNA sequencing to clinical records, we must use computational learning to find associations and biomarkers. The available data has sparsity, inconsistencies, and is very large for some datatypes. We processed clinical records with an expert oncologist and used complex modeling methods to substitute (impute) data for cases missing treatment information. We used machine learning algorithms to see if imputed data is useful for predicting patient survival. We saw no difference in ability to predict patient survival with the imputed data, though imputed treatment variables were more important to survival models. To deal with the large number of features in RNA expression data, we used two approaches: using all the data with High Performance Computers, and transforming the data into a smaller set of features (sparse principal components, or SPCs). We compared the performance of survival models with both datasets and saw no differences. However, the SPC models trained more quickly while also allowing us to pinpoint the biological processes each SPC is involved in to inform future biomarker discovery. We also examined ten processed molecular features for survival prediction ability and found some predictive power, though not enough to be clinically useful.
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Kohlruß, Meike [Verfasser], Gisela [Akademischer Betreuer] Keller, Michael [Gutachter] Groll, Jens H. L. [Gutachter] Neumann, and Gisela [Gutachter] Keller. "Molecular subtypes based on The Cancer Genome Atlas (TCGA) classification in gastric carcinoma: Prognostic and therapeutic implications / Meike Kohlruß ; Gutachter: Michael Groll, Jens H. L. Neumann, Gisela Keller ; Betreuer: Gisela Keller." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1241740178/34.

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Book chapters on the topic "The Cancer Genome Atlas (TCGA) dataset"

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Wang, Zhining, Mark A. Jensen, and Jean Claude Zenklusen. "A Practical Guide to The Cancer Genome Atlas (TCGA)." In Methods in Molecular Biology, 111–41. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3578-9_6.

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Kim, Jaegil, Gordon Robertson, Rehan Akbani, Seth P. Lerner, John N. Weinstein, Gad Getz, and David J. Kwiatkowski. "Genomic Assessment of Muscle-Invasive Bladder Cancer: Insights from the Cancer Genome Atlas (TCGA) Project." In Molecular Pathology Library, 43–64. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64769-2_3.

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Lamere, Alicia Taylor. "Cluster Analysis in R With Big Data Applications." In Open Source Software for Statistical Analysis of Big Data, 111–36. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2768-9.ch004.

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This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical and model-based clustering methods on larger datasets. These methods are illustrated through an application to a dataset of RNA-sequencing expression data for cancer patients obtained from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) data collection from The Cancer Imaging Archive (TCIA).
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Manuel Lopes de Sousa, Hugo, Joana Patrícia Costa Ribeiro, and Mafalda Basílio Timóteo. "Epstein-Barr Virus-Associated Gastric Cancer: Old Entity with New Relevance." In Epstein-Barr Virus [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.93649.

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Gastric cancer (GC) represents a major public health issue worldwide, being the fifth most common cancer and one of the leading causes of death by cancer. In 2014, The Cancer Genome Atlas (TCGA) established that tumors positive for Epstein-Barr virus (EBV) are considered a specific subtype of GC (EBVaGC). Several meta-analyses have shown that EBVaGC represents almost 10% of all gastric cancer worldwide, with small differences in the geographic distribution. This tumor subtype has a high potential of being clinically relevant and studies have shown that it has specific features, a better prognosis, and increased overall survival. In this review, we summarize some of the most frequent aspects of EBVaGC, including the specific features of this GC subtype, data regarding the potential steps of EBVaGC carcinogenesis, and perspectives on treatment opportunities.
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Conference papers on the topic "The Cancer Genome Atlas (TCGA) dataset"

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Dookeran, Keith A., and Maria Argos. "Abstract B07: Two-pore domain potassium (K+) channel genes and triple-negative (TN) subtype in The Cancer Genome Atlas (TCGA) breast cancer dataset." In Abstracts: Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; September 25-28, 2016; Fort Lauderdale, FL. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7755.disp16-b07.

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Akbani, Rehan, Kwok-Shing Ng, Henrica M. Werner, Fan Zhang, Zhenlin Ju, Wenbin Liu, Ji-Yeon Yang, Yiling Lu, John N. Weinstein, and Gordon B. Mills. "Abstract 4262: A pan-cancer proteomic analysis of The Cancer Genome Atlas (TCGA) project." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-4262.

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Creighton, C. "SR2-3: Integrative Genomic Analyses of Breast Cancer from The Cancer Genome Atlas (TCGA)." In Abstracts: Thirty-Fourth Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 6‐10, 2011; San Antonio, TX. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/0008-5472.sabcs11-sr2-3.

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Akbani, Rehan, and Douglas A. Levine. "Abstract 133: Integrated molecular characterization of uterine carcinosarcoma in The Cancer Genome Atlas (TCGA) project." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-133.

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Yau, C., S. Benz, JZ Sanborn, J. Stuart, D. Haussler, and C. Benz. "PD03-04: SuperPathway Analyses of Luminal and Basaloid Breast Cancers from the Cancer Genome Atlas (TCGA) Program." In Abstracts: Thirty-Fourth Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 6‐10, 2011; San Antonio, TX. American Association for Cancer Research, 2011. http://dx.doi.org/10.1158/0008-5472.sabcs11-pd03-04.

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Kulkarni, Diptee A., Karl Guo, Junping Jing, Mugdha Khaladkar, Kijoung Song, Coco Dong, David Cooper, and Benjamin Schwartz. "Abstract 236: Identification of novel cancer target genes by combining data from the cancer genome-wide association studies (GWAS), regulatory DNA elements and The Cancer Genome Atlas (TCGA)." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-236.

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Ben-Zvi, Ido, Ido Sloma, Tin Khor, Daniel Ciznadija, Amanda Katz, David Vasquez, David Sidransky, and Keren Paz. "Abstract A14: Molecular fidelity of patient derived xenograft (PDX) models to original human tumor and to the cancer genome atlas (TCGA)." In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; November 5-9, 2015; Boston, MA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1535-7163.targ-15-a14.

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Weinstein, John N., Seth P. Lerner, David J. Kwiatkowski, Gad Getz, Jaegil Kim, Hikmat A. Al-ahmadie, Andrew D. Cherniack, et al. "Abstract 128: Comprehensive molecular characterization of 412 muscle-invasive urothelial bladder carcinomas: final analysis of The Cancer Genome Atlas (TCGA) project." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-128.

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Wilcox, Amber, Debra Silverman, Melissa Friesen, Sarah Locke, Daniel Russ, Noorie Hyun, Joanne Colt, et al. "P034 Smoking status, primary adult occupation and risk of recurrent urothelial bladder carcinoma: data from the cancer genome atlas (TCGA) project." In Occupational Health: Think Globally, Act Locally, EPICOH 2016, September 4–7, 2016, Barcelona, Spain. BMJ Publishing Group Ltd, 2016. http://dx.doi.org/10.1136/oemed-2016-103951.359.

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Dookeran, Keith A., Jacob K. Kresovich, Maria Argos, and Garth H. Rauscher. "Abstract B49: The role of KCNK9 and TP53 on the racial disparity in biologically aggressive breast cancer subtype in The Cancer Genome Atlas (TCGA)." In Abstracts: Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; November 13-16, 2015; Atlanta, Georgia. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7755.disp15-b49.

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