Clinic-genomic association mining for colorectal cancer using publicly available datasets.
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ABSTRACT:
SUBMITTER: Liu F
PROVIDER: S-EPMC4060771 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
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Project description:IntroductionBreast cancer is a complex heterogeneous disease for which a substantial resource of transcriptomic data is available. Gene expression data have facilitated the division of breast cancer into, at least, five molecular subtypes, namely luminal A, luminal B, HER2, normal-like and basal. Once identified, breast cancer subtypes can inform clinical decisions surrounding patient treatment and prognosis. Indeed, it is important to identify patients at risk of developing aggressive disease so as to tailor the level of clinical intervention.MethodsWe have developed a user-friendly, web-based system to allow the evaluation of genes/microRNAs (miRNAs) that are significantly associated with survival in breast cancer and its molecular subtypes. The algorithm combines gene expression data from multiple microarray experiments which frequently also contain miRNA expression information, and detailed clinical data to correlate outcome with gene/miRNA expression levels. This algorithm integrates gene expression and survival data from 26 datasets on 12 different microarray platforms corresponding to approximately 17,000 genes in up to 4,738 samples. In addition, the prognostic potential of 341 miRNAs can be analysed.ResultsWe demonstrated the robustness of our approach in comparison to two commercially available prognostic tests, oncotype DX and MammaPrint. Our algorithm complements these prognostic tests and is consistent with their findings. In addition, BreastMark can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the cost and complexity of these multi-index assays. Known miRNA prognostic markers, mir-205 and mir-93, were used to confirm the prognostic value of this tool in a miRNA setting. We also applied the algorithm to examine expression of 58 receptor tyrosine kinases in the basal-like subtype, identifying six receptor tyrosine kinases associated with poor disease-free survival and/or overall survival (EPHA5, FGFR1, FGFR3, VEGFR1, PDGFRβ, and TIE1). A web application for using this algorithm is currently available.ConclusionsBreastMark is a powerful tool for examining putative gene/miRNA prognostic markers in breast cancer. The value of this tool will be in the preliminary assessment of putative biomarkers in breast cancer. It will be of particular use to research groups with limited bioinformatics facilities.
| S-EPMC3978487 | biostudies-literature
Project description:Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.
| S-EPMC6511704 | biostudies-literature
Project description:The genomic grade (GG) for breast cancer is thought to be the genomic counterpart of histopathological grade (HG). The motivation behind this study was to see whether HG retains its prognostic impact even when adjusted for GG, or whether it can be replaced by the latter. Four publicly available gene expression datasets were analyzed. Kaplan-Meier curves, log rank test, and Cox regression were used to study recurrence-free survival (RFS) and distant metastasis-free survival (DMFS). HG remained a significant prognostic indicator in low GG tumors (P = 0.003 for DMFS, P< 0.001 for RFS) but not in high GG tumors. HG grade 2 tumors differed significantly from HG grade 1 tumors, underlining the prognostic role of intermediate HG tumors. Additionally, GG could stratify HG 1 as well as HG 2 tumors into distinct prognostic groups. HG and GG add independent prognostic information to each other. However, the prognostic effects of both HG and GG are time varying, with the hazard ratios of high HG and GG tumors being markedly attenuated over time.
| S-EPMC3154579 | biostudies-literature
Project description:Differential gene expression analysis is widely used to study changes in gene expression profiles between two or more groups of samples (e.g., physiological versus pathological conditions, pre-treatment versus post-treatment, and infected versus non-infected tissues). This protocol aims to identify gene expression changes in a pre-selected set of genes associated with severe acute respiratory syndrome coronavirus 2 viral infection and host cell antiviral response, as well as subsequent gene expression association with phenotypic features using samples deposited in public repositories. For complete details on the use and outcome of this informatics analysis, please refer to Bizzotto et al. (2020).
| S-EPMC8061314 | biostudies-literature
Project description:Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data.
| S-EPMC7327722 | biostudies-literature
Project description:RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses. ARCHS4 is a web resource that makes the majority of published RNA-seq data from human and mouse available at the gene and transcript levels. For developing ARCHS4, available FASTQ files from RNA-seq experiments from the Gene Expression Omnibus (GEO) were aligned using a cloud-based infrastructure. In total 187,946 samples are accessible through ARCHS4 with 103,083 mouse and 84,863 human. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene pages that provide average expression across cell lines and tissues, top co-expressed genes for each gene, and predicted biological functions and protein-protein interactions for each gene based on prior knowledge combined with co-expression.
| S-EPMC5893633 | biostudies-other
Project description:Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45 years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45 years-old) had four-fold more basal tumors and worst progression-free survival (median of 101 months), compared with the 45-65 years group (median of 168 months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.
| S-EPMC9845274 | biostudies-literature
Project description:IntroductionMicroarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.AimThe aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets.MethodsFour microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA).ResultsGene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed.ConclusionTo the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.
| S-EPMC4469303 | biostudies-literature
Project description:Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the US Centers for Disease Control and Prevention recommend that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the existing literature. In this paper we propose a novel causal inference framework for cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we focus our approach on a causal inference estimand which we call the causal SIR (cSIR). The cSIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence for the same population under the (counterfactual) scenario of no exposure. To estimate the cSIR we need to overcome two main challenges: 1) identify unexposed populations that are as similar as possible to the exposed one to inform estimation of the expected cancer incidence under the counterfactual scenario of no exposure, and 2) publicly available data on cancer incidence for these unexposed populations are often available at a much higher level of spatial aggregation (e.g. county) than what is desired (e.g. census block group). We overcome the first challenge by relying on matching. We overcome the second challenge by building a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired level of spatial aggregation. In simulations, our statistical approach was shown to provide dramatically improved results, i.e., less bias and better coverage, than the current approach to SIR analyses. We apply our proposed approach to investigate whether trichloroethylene vapor exposure has caused increased cancer incidence in Endicott, New York.
| S-EPMC8276584 | biostudies-literature
Project description:IntroductionCardiac presentation of autoimmune sarcoidosis, known as cardiac sarcoidosis (CS), is a poorly understood disease with high mortality and low diagnosis rate. While CS is an immunological syndrome, little is known about how cardiac parenchymal and stromal cells mediate its pathogenesis. Moreover, while most current sarcoidosis research is based on research in pulmonary sarcoidosis (PS), it remains unclear how much both presentations of sarcoidosis overlap. To tackle these concerns, we leveraged publicly available sarcoidosis transcriptomic datasets.MethodsTwo publicly available bronchoalveolar lavage single-cell RNA sequencing datasets were integrated to analyze PS relative to control. Additionally, two publicly available cardiac single-nucleus RNA sequencing datasets were integrated to analyze CS relative to control. Following integration, we ran cell-cell communication, transcription factor, and differential expression analyses on parenchymal, stromal, and immune subsets identified in our analysis.ResultsOur analysis revealed that there was an expansion of stromal and immune cells in PS and CS. We also observed upregulation of Th17.1 and attenuated activation transcriptional profiles in the immune cells of CS and PS relative to control. Additionally, we found upregulation of pro-inflammatory and pro-fibrotic transcriptional profiles in the cardiac stromal cells of CS relative to control. We also found that cardiomyocytes exhibited upregulated cardiac stress and proliferation transcriptional profiles in CS relative to control.ConclusionsOur integrative transcriptomic analysis shows that despite tissue-specific differences, there are shared transcriptional trends between CS and PS. It also shows that stromal and parenchymal populations exhibit transcriptional trends that could explain their pathogenic role in CS.
| S-EPMC10419306 | biostudies-literature