scholarly journals 4058 Enhanced efficiency of large-scale clinical proteomic studies: when less is more

2020 ◽  
Vol 4 (s1) ◽  
pp. 107-107
Author(s):  
Stefani Thomas ◽  
Betty Friedrich ◽  
Michael Schnaubelt ◽  
Daniel W. Chan ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Large Scale ◽  
Molecular Taxonomy ◽  
The Cancer Genome Atlas ◽  
Biological Information ◽  
Data Sets ◽  
Less Is More ◽  
Gene Correlation ◽  
The Stability ◽  
Cancer Tissues

OBJECTIVES/GOALS: Large-scale clinical proteomic studies of cancer tissues often entail complex workflows and are resource-intensive. In this study we analyzed ovarian tumors using an emerging, high-throughput proteomic technology termed SWATH. We compared SWATH with the more widely used iTRAQ workflow based on robustness, complexity, ability to detect differential protein expression, and the elucidated biological information. METHODS/STUDY POPULATION: Proteomic measurements of 103 clinically-annotated high-grade serous ovarian cancer (HGSOC) tumors previously genomically characterized by The Cancer Genome Atlas were conducted using two orthogonal mass spectrometry-based proteomic methods: iTRAQ and SWATH. The analytical differences between the two methods were compared with respect to relative protein abundances. To assess the ability to classify the tumors into subtypes based on proteomic signatures, an unbiased molecular taxonomy of HGSOC was established using protein abundance data. The 1,599 proteins quantified in both datasets were classified based on z-score-transformed protein abundances, and the emergent protein modules were characterized using weighted gene-correlation network analysis and Reactome pathway enrichment. RESULTS/ANTICIPATED RESULTS: Despite the greater than two-fold difference in the analytical depth of each proteomic method, common differentially expressed proteins in enriched pathways associated with the HGSOC Mesenchymal subtype were identified by both methods. The stability of tumor subtype classification was sensitive to the number of analyzed samples, and the statistically stable subgroups were identified by the data from both methods. Additionally, the homologous recombination deficiency-associated enriched DNA repair and chromosome organization pathways were conserved in both data sets. DISCUSSION/SIGNIFICANCE OF IMPACT: SWATH is a robust proteomic method that can be used to elucidate cancer biology. The lower number of proteins detected by SWATH compared to iTRAQ is mitigated by its streamlined workflow, increased sample throughput, and reduced sample requirement. SWATH therefore presents novel opportunities to enhance the efficiency of clinical proteomic studies.

The glycobiological landscape of breast cancer: insights into galectins and O-GlcNAc homeostasis-related genes

2021 ◽  
Author(s):  
Rada Tazhitdinova ◽  
Alexander V Timoshenko
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Breast Cancer Subtype ◽  
The Cancer Genome Atlas ◽  
Correlation Relationship ◽  
Cancer Data ◽  
Normal Tissues ◽  
Genes Encoding ◽  
Genetic Landscape ◽  
Cancer Tissues

Abstract Purpose This study aimed to assess the functional associations between genes of the glycobiological landscape encoding galectins and O-GlcNAc cycle enzymes in the context of breast cancer biology and clinical applications. Methods An in silico analysis of the breast cancer data from The Cancer Genome Atlas was conducted comparing expression, pairwise correlations, and prognostic value for 17 genes encoding galectins, O-GlcNAc cycle enzymes, and cell stemness-related transcription factors. Results Multiple general and breast cancer subtype-specific differences in galectin/O-GlcNAc genetic landscape markers were observed and classified. Specifically, LGALS12 was found to be significantly downregulated in breast cancer tissues across all subtypes while LGALS2 and GFPT1 showed potential as prognostic markers. Remarkably, there was an overall loss of both correlation strength and correlation relationship between expression of galectin/O-GlcNAc landscape genes in the breast cancer samples versus normal tissues. Six gene pairs (GFPT1/LGALS1, GFPT1/LGALS3, GFPT1/LGALS12, GFPT1/KLF4, OGT/LGALS12, and OGT/KLF4) were found to be potential diagnostic markers for breast cancer. Conclusions These findings indicate that the glycobiological landscape of breast cancer underwent significant remodeling, which might be associated with switching galectin gene regulation within a framework of O-GlcNAc homeostasis.

scholarly journals High Expression of miR-34a Associated with Less Aggressive Cancer Biology but Not with Survival in Breast Cancer

2020 ◽  
Vol 21 (9) ◽  
pp. 3045 ◽  
Author(s):  
Yoshihisa Tokumaru ◽  
Eriko Katsuta ◽  
Masanori Oshi ◽  
Judith C. Sporn ◽  
Li Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cell Proliferation ◽  
Cancer Biology ◽  
Epithelial Mesenchymal Transition ◽  
Molecular Taxonomy ◽  
The Cancer Genome Atlas ◽  
P53 Pathway ◽  
Mesenchymal Transition ◽  
Gene Sets ◽  
Aggressive Cancer

Most breast cancer (BC) patients succumb to metastatic disease. MiR-34a is a well-known tumor suppressive microRNA which exerts its anti-cancer functions by playing a role in p53, apoptosis induction, and epithelial-mesenchymal transition (EMT) suppression. Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) cohorts were used to test our hypothesis that miR-34a high BCs translate to less aggressive cancer biology and better survival in large cohorts. There was no association between miR-34a expression levels and clinicopathological features of BC patients except for HER2 positivity. MiR-34a high expressing tumors were associated with lower Nottingham pathological grades and lower MKI67 expression. In agreement, high miR-34a tumors demonstrated lower GSVA scores of cell cycle and cell proliferation-related gene sets. High miR-34a tumors enriched the p53 pathway and apoptosis gene sets. Unexpectedly, high miR-34a tumors also associated with elevated EMT pathway score and ZEB1 and two expressions. MiR-34a expression did not associate with any distant metastasis. Further, high miR-34a tumors did not associate with better survival compared with miR-34a low tumors. In conclusion, the clinical relevance of miR-34a high expressing tumors was associated with suppressed cell proliferation, enhanced p53 pathway and apoptosis, but enhanced EMT and these findings did not reflect better survival outcomes in large BC patient cohorts.

scholarly journals CAFU: a Galaxy framework for exploring unmapped RNA-Seq data

2019 ◽  
Vol 21 (2) ◽  
pp. 676-686 ◽  
Author(s):  
Siyuan Chen ◽  
Chengzhi Ren ◽  
Jingjing Zhai ◽  
Jiantao Yu ◽  
Xuyang Zhao ◽  
...  
Keyword(s):  
Large Scale ◽  
Biological Information ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Rna Seq ◽  
Mixed Species ◽  
Short Reads ◽  
Comprehensive Collection ◽  
Expression Characterization ◽  
And Function

Abstract A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.

scholarly journals BIG: a large-scale data integration tool for renal physiology

2016 ◽  
Vol 311 (4) ◽  
pp. F787-F792 ◽  
Author(s):  
Yue Zhao ◽  
Chin-Rang Yang ◽  
Viswanathan Raghuram ◽  
Jaya Parulekar ◽  
Mark A. Knepper
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Relevant Information ◽  
Biological Information ◽  
Renal Physiology ◽  
Data Sets ◽  
Data Set ◽  
Large Scale Data ◽  
Quantify Gene Expression

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: “How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?” This is the type of problem that has motivated the “Big-Data” revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/ .

scholarly journals Convergent architecture of the transcriptome of human cancer

2015 ◽  
Author(s):  
Lihua Zou
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Human Cancer ◽  
Adaptive Immune Response ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
System Level ◽  
Data Sets ◽  
Using Data ◽  
Conserved Gene

Despite large-scale efforts to systematically map the cancer genome, little is known about how the interplay of genetic and epigenetic alternations shapes the architecture of the transcriptome of human cancer. With the goal of constructing a system-level view of the deregulated pathways in cancer cells, we systematically investigated the functional organization of the transcriptomes of 10 tumor types using data sets generated by The Cancer Genome Atlas project (TCGA). Our analysis indicates that the human cancer transcriptome is organized into well-conserved modules of co-expressed genes. In particular, our analysis identified a set of conserved gene modules with distinct cancer hallmark themes involving cell cycle regulation, angiogenesis, innate and adaptive immune response, differentiation, metabolism and regulation of protein phosphorylation. Our analysis provided global views of convergent transcriptome architecture of human cancer. The result of our analysis can serve as a foundation to link diverse genomic alternations to common transcriptomic features in human cancer.

scholarly journals Task-evoked activity quenches neural correlations and variability across cortical areas

2019 ◽  
Author(s):  
Takuya Ito ◽  
Scott L. Brincat ◽  
Markus Siegel ◽  
Ravi D. Mill ◽  
Biyu J. He ◽  
...  
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Brain Regions ◽  
Data Sets ◽  
Imaging Data ◽  
Information Coding ◽  
Evoked Activity ◽  
Cortical Regions ◽  
The Stability ◽  
Neural Variability

AbstractMany large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

scholarly journals TIGIT-related transcriptome profile and its association with tumor immune microenvironment in breast cancer

2021 ◽  
Vol 41 (3) ◽  
Author(s):  
Qin Zhang ◽  
Chaowei Gao ◽  
Jianqiang Shao ◽  
Zunyi Wang
Keyword(s):  
Breast Cancer ◽  
Immune Responses ◽  
Cancer Immunotherapy ◽  
Large Scale ◽  
Immune Cell ◽  
Molecular Taxonomy ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Breast Cancer Patients ◽  
Transcriptome Profile

Abstract Immune checkpoints are intensively investigated as targets in cancer immunotherapy. T-cell immunoreceptor with immunoglobulin (Ig) and ITIM domains (TIGIT) are recently emerging as a novel promising target in cancer immunotherapy. Herein, we systematically investigated TIGIT-related transcriptome profile and relevant clinical information derived from a total of 2994 breast cancer patients recorded in The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We uncovered the relationship between TIGIT and major molecular and clinical characteristics in breast cancer. More importantly, we depicted the landscape of associations between TIGIT and other immune cell populations. Gene ontology analyses and Gene Set Variation Analysis (GSVA) of genes correlated with TIGIT revealed that TIGIT were mainly involved in immune responses and inflammatory activities. In summary, TIGIT expression was tightly related to the aggressiveness of breast cancer; TIGIT might manipulate anti-tumor immune responses by impacting not only T cells but also other immune cells. To the best of our knowledge, this is by far the most comprehensive and largest study characterizing the molecular and clinical features of TIGIT in breast cancer through large-scale transcriptome data.

scholarly journals Molecular and Clinical Characterization of CCT2 Expression and Prognosis via Large-Scale Transcriptome Profile of Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiang Liu ◽  
Yihang Qi ◽  
Xiangyi Kong ◽  
Xiangyu Wang ◽  
Wenxiang Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Molecular Chaperone ◽  
Large Scale ◽  
Molecular Taxonomy ◽  
Prognostic Significance ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Worse Prognosis

Molecular chaperones play important roles in regulating various cellular processes and malignant transformation. Expression of some subunits of molecular chaperone CCT/TRiC complex have been reported to be correlated with cancer development and patient survival. However, little is known about the expression and prognostic significance of Chaperonin Containing TCP1 Subunit 2 (CCT2). CCT2 is a gene encoding a molecular chaperone that is a member of the chaperonin containing TCP1 complex (CCT), also known as the TCP1 ring complex (TRiC). Through the Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases, we systematically reviewed a total of 2,994 cases with transcriptome data and analyzed the functional annotation of CCT2 by Gene ontology and KEGG analysis. Univariate and multivariate survival analysis were performed to investigate the prognostic value of CCT2 in breast cancer. We found CCT2 was significantly upregulated in various tumors. In breast cancer, CCT2 expression was significantly upregulated in HER2-positive (HER2+) group, and more malignant group. In addition, we investigated correlations between CCT2 and other CCT members. Interestingly, almost all CCTs expression were positively correlated with each other, but not CCT6B. Survival analysis suggested that CCT2 overexpression was independently associated with worse prognosis of patients with breast cancer, especially in luminal A subtype. In summary, our results revealed that CCT2 might be involved in regulating cell cycle pathway, and independently predicted worse prognosis in breast cancer patients. These findings may expand understanding of potential anti-CCT2 treatments. To our knowledge, this is the largest and most comprehensive study characterizing the expression pattern of CCT2 together with its prognostic values in breast cancer.

scholarly journals Attenuation of PITPNM1 Signaling Cascade Can Inhibit Breast Cancer Progression

Biomolecules ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1265
Author(s):  
Zihao Liu ◽  
Yu Shi ◽  
Qun Lin ◽  
Wenqian Yang ◽  
Qing Luo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Molecular Taxonomy ◽  
Prognostic Indicator ◽  
Normal Breast ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Phosphatidylinositol Transfer ◽  
Cancer Tissues

Phosphatidylinositol transfer protein membrane-associated 1 (PITPNM1) contains a highly conserved phosphatidylinositol transfer domain which is involved in phosphoinositide trafficking and signaling transduction under physiological conditions. However, the functional role of PITPNM1 in cancer progression remains unknown. Here, by integrating datasets of The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer (METABRIC), we found that the expression of PITPNM1 is much higher in breast cancer tissues than in normal breast tissues, and a high expression of PITPNM1 predicts a poor prognosis for breast cancer patients. Through gene set variation analysis (GSEA) and gene ontology (GO) analysis, we found PITPNM1 is mainly associated with carcinogenesis and cell-to-cell signaling ontology. Silencing of PITPNM1, in vitro, significantly abrogates proliferation and colony formation of breast cancer cells. Collectively, PITPNM1 is an important prognostic indicator and a potential therapeutic target for breast cancer.

scholarly journals Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

2018 ◽  
Author(s):  
Ermin Hodzic ◽  
Raunak Shrestha ◽  
Kaiyuan Zhu ◽  
Kuoyuan Cheng ◽  
Colin C. Collins ◽  
...  
Keyword(s):  
Large Scale ◽  
Simultaneous Detection ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Data Sets ◽  
Combinatorial Method ◽  
Binary Information ◽  
Cancer Genome Atlas ◽  
Tumor Sequencing ◽  
Tumor Types

AbstractBackgroundAdvances in large scale tumor sequencing have lead to an understanding that there are combinations of genomic and transcriptomic alterations speciflc to tumor types, shared across many patients. Unfortunately, computational identiflcation of functionally meaningful shared alteration patterns, impacting gene/protein interaction subnetworks, has proven to be challenging.FindingsWe introduce a novel combinatorial method, cd-CAP, for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not), and simultaneously detects multiple alteration proflle conserved subnetworks.ConclusionsIn a number of The Cancer Genome Atlas (TCGA) data sets, cd-CAP identifled large biologically signiflcant subnetworks with conserved alteration patterns, shared across many tumor samples.

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