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Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

ABSTRACT: BACKGROUND:Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. RESULTS:The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. CONCLUSIONS:Using predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients. REVIEWERS:This article was reviewed by Subharup Guha and Isabel Nepomuceno.

SUBMITTER: Grimes T

PROVIDER: S-EPMC5977759 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

Grimes Tyler T Walker Alejandro R AR Datta Susmita S Datta Somnath S

Biology direct 20180530 1

<h4>Background</h4>Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining the ...[more]

PMID: 29848365

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Project description:BackgroundCircular RNA (circRNA) is one type of noncoding RNA that forms a covalently closed continuous loop. Similar to long noncoding RNA (lncRNA), circRNA can act as microRNA (miRNA) 'sponges' to regulate gene expression, and its abnormal expression is related to diseases such as atherosclerosis, nervous system disorders and cancer. So far, there have been no systematic studies on circRNA abundance and expression profiles in human adult and fetal tissues.ResultsWe explored circRNA expression profiles using RNA-seq data for six adult and fetal normal tissues (colon, heart, kidney, liver, lung, and stomach) and four gland normal tissues (adrenal gland, mammary gland, pancreas, and thyroid gland). A total of 8120, 25,933 and 14,433 circRNAs were detected by at least two supporting junction reads in adult, fetal and gland tissues, respectively. Among them, 3092, 14,241 and 6879 circRNAs were novel when compared to the published results. In each adult tissue type, we found at least 1000 circRNAs, among which 36.97-50.04% were tissue-specific. We reported 33 circRNAs that were ubiquitously expressed in all the adult tissues we examined. To further explore the potential "housekeeping" function of these circRNAs, we constructed a circRNA-miRNA-mRNA regulatory network containing 17 circRNAs, 22 miRNAs and 90 mRNAs. Furthermore, we found that both the abundance and the relative expression level of circRNAs were higher in fetal tissue than adult tissue. The number of circRNAs in gland tissues, especially in mammary gland (9665 circRNA candidates), was higher than that of other adult tissues (1160-3777).ConclusionsWe systematically investigated circRNA expression in a variety of human adult and fetal tissues. Our observation of different expression level of circRNAs in adult and fetal tissues suggested that circRNAs might play their role in a tissue-specific and development-specific fashion. Analysis of circRNA-miRNA-mRNA network provided potential targets of circRNAs. High expression level of circRNAs in mammary gland might be attributed to the rich innervation.

Project description:Lung adenocarcinoma is caused by the combination of genetic and environmental effects, and smoking plays an important role in the disease development. Exploring the gene expression profile and identifying genes that are shared or vary between smokers and nonsmokers with lung adenocarcinoma will provide insights into the etiology of this complex cancer. We obtained RNA-seq data from paired normal and tumor tissues from 34 nonsmoking and 34 smoking patients with lung adenocarcinoma (GEO: GSE40419). R Bioconductor, edgeR, was adopted to conduct differential gene expression analysis between paired normal and tumor tissues. A generalized linear model was applied to identify genes that were differentially expressed in nonsmoker and smoker patients as well as genes that varied between these two groups. We identified 2273 genes that showed differential expression with FDR?<?0.05 and |logFC| >1 in nonsmoker tumor versus normal tissues; 3030 genes in the smoking group; and 1967 genes were common to both groups. Sixty-eight and 70% of the identified genes were downregulated in nonsmoking and smoking groups, respectively. The 20 genes such as SPP1, SPINK1, and FAM83A with largest fold changes in smokers also showed similar large and highly significant fold changes in nonsmokers and vice versa, showing commonalities in expression changes for adenocarcinomas in both smokers and nonsmokers for these genes. We also identified 175 genes that were significantly differently expressed between tumor samples from nonsmoker and smoker patients. Gene expression profile varied substantially between smoker and nonsmoker patients with lung adenocarcinoma. Smoking patients overall showed far more complicated disease mechanism and have more dysregulation in their gene expression profiles. Our study reveals pathogenetic differences in smoking and nonsmoking patients with lung adenocarcinoma from transcriptome analysis. We provided a list of candidate genes for further study for disease detection and treatment in both smoking and nonsmoking patients with lung adenocarcinoma.

Project description:BACKGROUND Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer. MATERIAL AND METHODS The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs. RESULTS A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism. CONCLUSIONS Our study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.

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Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

Publications

Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

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