Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
Patients were assigned to monitoring for rejection after cardiac transplantation either according to the standard practice of endomyocardial biopsies or with gene-expression profiling. At 19 months, the rates of rejection with hemodynamic compromise, graft dysfunction, death, or retransplantation were similar in the two groups, although the power of the trial was limited. Patients were assigned to monitoring for rejection after cardiac transplantation either according to the standard practice of endomyocardial biopsies or with gene-expression profiling. At 19 months, the rates of rejection were similar in the two groups. Advances in immunosuppression after cardiac transplantation have increased the rates of 1-year survival among recipients to nearly 90%. However, acute cellular rejection is still observed during the first year after transplantation (at a rate of approximately 30 to 40%) and occurs at a lower rate thereafter. 1 – 4 Rejection episodes are associated with an increased risk of allograft vasculopathy and loss. 5 – 7 Endomyocardial biopsy has remained the primary method of monitoring for rejection, despite the discomfort and the rare but potentially serious complications of the procedure. 8 – 12 Quantitative assessment of mononuclear-cell gene expression in peripheral-blood specimens has been explored as a . . .
Background & Aims Gene expression profiling provides an opportunity for definitive diagnosis but has not yet been well applied to inflammatory diseases. Here we describe an approach for diagnosis of an emerging form of esophagitis, eosinophilic esophagitis (EoE), which is currently diagnosed by histology and clinical symptoms. Methods We developed an EoE diagnostic panel (EDP) comprising a 96-gene quantitative polymerase chain reaction array and an associated dual-algorithm that uses cluster analysis and dimensionality reduction using a cohort of randomly selected esophageal biopsy samples from pediatric patients with EoE (n = 15) or without EoE (non-EoE controls, n = 14) and subsequently vetted the EDP using a separate cohort of 194 pediatric and adult patient samples derived from both fresh or formalin-fixed, paraffin-embedded tissue: active EoE (n = 91), control (non-EoE and EoE remission, n = 57), histologically ambiguous (n = 34), and reflux (n = 12) samples. Results The EDP identified adult and pediatric patients with EoE with approximately 96% sensitivity and approximately 98% specificity, and distinguished patients with EoE in remission from controls, as well as identified patients exposed to swallowed glucorticoids. The EDP could be used with formalin-fixed, paraffin-embedded tissue RNA and distinguished patients with EoE from those with reflux esophagitis, identified by pH-impedance testing. Preliminary evidence showed that the EDP could identify patients likely to have disease relapse after treatment. Conclusions We developed a molecular diagnostic test (referred to as the EDP) that identifies patients with esophagitis in a fast, objective, and mechanistic manner, offering an opportunity to improve diagnosis and treatment, and a platform approach for other inflammatory diseases.
We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tag-seq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.
Massively parallel sequencing holds great promise for expression profiling, as it combines the high throughput of SAGE with the accuracy of EST sequencing. Nevertheless, until now only very limited information had been available on the suitability of the Current technology to meet the requirements. Here, we evaluate the potential of 454 sequencing technology for expression profiling using Drosophila melanogaster. We show that short ( similar to 300-400 bp) cDNA fragments are Under-represented in 454 sequence reads. Nevertheless, sequencing of 3' cDNA fragments generated by nebulization could be used to overcome the length bias of the 454 sequencing technology. Gene expression measurements generated by restriction analysis and nebulization for fragments within the 80- to 300-bp range showed correlations similar to those reported for replicated microarray experiments (0.83-0.91); 97% of the cDNA fragments could be unambiguously mapped to the genomic DNA, demonstrating the advantage of longer Sequence reads. Our analyses suggest that the 4,54 technology has a large potential for expression profiling, and the high mapping accuracy indicates that it should be possible to compare expression profiles across species.
High-throughput sequencing (HTS) has proven to be an invaluable tool for the discovery of thousands of microRNA genes across multiple species. At present, the throughput of HTS platforms is sufficient to combine discovery with quantitative expression analysis allowing for digital gene expression (DGE) profiling. Methods for small RNA DGE profiling are strongly biased toward certain small RNAs, preventing the accurate determination of absolute numbers of small RNAs. The observed bias is largely independent of the sequencing platform but strongly determined by the method used for small RNA library preparation. However, as the biases are systematic and highly reproducible, DGE profiling is suited for determining relative expression differences between samples.
The imbalance between energy intake and expenditure is the underlying cause of the current obesity and diabetes pandemics. Central to these pathologies is the fat depot: white adipose tissue (WAT) stores excess calories, and brown adipose tissue (BAT) consumes fuel for thermogenesis using tissue-specific uncoupling protein 1 (UCP1)(1,2). BAT was once thought to have a functional role in rodents and human infants only, but it has been recently shown that in response to mild cold exposure, adult human BAT consumes more glucose per gram than any other tissue(3). In addition to this nonshivering thermogenesis, human BAT may also combat weight gain by becoming more active in the setting of increased whole-body energy intake(4-7). This phenomenon of BAT-mediated diet-induced thermogenesis has been observed in rodents(8) and suggests that activation of human BAT could be used as a safe treatment for obesity and metabolic dysregulation(9). In this study, we isolated anatomically defined neck fat from adult human volunteers and compared its gene expression, differentiation capacity and basal oxygen consumption to different mouse adipose depots. Although the properties of human neck fat vary substantially between individuals, some human samples share many similarities with classical, also called constitutive, rodent BAT.
Background. Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P<.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.
Background. Gene expression profiling data for human primary cutaneous melanomas are scarce because of the lack of retrospective collections of frozen tumors. To identify differentially expressed genes that may be involved in melanoma progression and prognosis, we investigated the relationship between gene expression profiles and clinical outcome in a cohort of patients with primary melanoma. Methods: Labeled complementary RNA (cRNA) from each tissue sample was hybridized to a pangenomic 44K 60-mer oligonucleotide microarray. Class comparison and class prediction analyses were performed to identify genes whose expression in primary melanomas was associated with 4-year distant metastasis-free survival among 58 patients with at least 4 years of follow-up, distant metastasis, or death. Results were validated immunohistochemically at the protein level in 176 independent primary melanomas from patients with a median clinical follow-up of 8.5 years. Survival was analyzed with a Cox multivariable model and stratified log-rank test. All statistical tests were two-sided. Results: We identified 254 genes that were associated with distant metastasis-free survival of patients with primary melanoma. These 254 genes include genes involved in activating DNA replication origins, such as minichromosome maintenance genes and geminin. Twenty-three of these genes were studied at the protein level; expression of five (MCM4, P = .002; MCM3, P = .030; MCM6, P = .004; KPNA2, P = .021; and geminin, P = .004) was statistically significantly associated with overall survival in the validation set. In a multivariable Cox model adjusted for tumor thickness, ulceration, age, and sex, expression of MCM4 (hazard ratio [RR] of death = 4.04, 95% confidence interval [CI] = 1.39 to 11.76; P = .010) and MCM6 (HR of death = 7.42, 95% CI = 1.99 to 27.64; P = .003) proteins was still statistically significantly associated with overall survival. Conclusion: We identified 254 genes whose expression was associated with metastatic dissemination of cutaneous melanomas. These genes may shed light on the molecular mechanisms underlying poor prognosis in melanoma patients.
Microarray-based gene expression profiling has had a major effect on our understanding of breast cancer. Breast cancer is now perceived as a heterogeneous group of different diseases characterised by distinct molecular aberrations, rather than one disease with varying histological features and clinical behaviour. Gene expression profiling studies have shown that oestrogen-receptor (ER)-positive and ER-negative breast cancers are distinct diseases at the transcriptomic level, that additional molecular subtypes might exist within these groups, and that the prognosis of patients with ER-positive disease is largely determined by the expression of proliferation-related genes. On the basis of these principles, a molecular classification system and prognostic multigene classifiers based on microarrays or derivative technologies have been developed and are being tested in randomised clinical trials and incorporated into clinical practice. In this review, we focus on the conceptual effect and potential clinical use of the molecular classification of breast cancer, and discuss prognostic and predictive multigene predictors.