BackgroundRecently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.ResultsIn this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.ConclusionSpearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0778-7) contains supplementary material, which is available to authorized users.
Cervical cancer is the leading cause of death among women with cancer worldwide. Here, we performed an integrative analysis of Illumina HumanMethylation450K and RNA-seq data from TCGA to identify cervical cancer-specific DNA methylation markers. We first identified differentially methylated and expressed genes and examined the correlation between DNA methylation and gene expression. The DNA methylation profiles of 12 types of cancers, including cervical cancer, were used to generate a candidate set, and machine-learning techniques were adopted to define the final cervical cancer-specific markers in the candidate set. Then, we assessed the protein levels of marker genes by immunohistochemistry by using tissue arrays containing 93 human cervical squamous cell carcinoma samples and cancer-adjacent normal tissues. Promoter methylation was negatively correlated with the local regulation of gene expression. In the distant regulation of gene expression, the methylation of hypermethylated genes was more likely to be negatively correlated with gene expression, while the methylation of hypomethylated genes was more likely to be positively correlated with gene expression. Moreover, we identified four cervical cancer-specific methylation markers, cg07211381 (RAB3C), cg12205729 (GABRA2), cg20708961 (ZNF257), and cg26490054 (SLC5A8), with 96.2% sensitivity and 95.2% specificity by using the tenfold cross-validation of TCGA data. The four markers could distinguish tumors from normal tissues with a 94.2, 100, 100, and 100% AUC in four independent validation sets from the GEO database. Overall, our study demonstrates the potential use of methylation markers in cervical cancer diagnosis and may boost the development of new epigenetic therapies.
Metabolic reprogramming to fulfill the biosynthetic and bioenergetic demands of cancer cells has aroused great interest in recent years. However, metabolic reprogramming for cancer metastasis has not been well elucidated. Here, we screened a subpopulation of breast cancer cells with highly metastatic capacity to the lung in mice and investigated the metabolic alternations by analyzing the metabolome and the transcriptome, which were confirmed in breast cancer cells, mouse models, and patients’ tissues. The effects and the mechanisms of nucleotide de novo synthesis in cancer metastasis were further evaluated in vitro and in vivo. In our study, we report an increased nucleotide de novo synthesis as a key metabolic hallmark in metastatic breast cancer cells and revealed that enforced nucleotide de novo synthesis was enough to drive the metastasis of breast cancer cells. An increased key metabolite of de novo synthesis, guanosine-5'-triphosphate (GTP), is able to generate more cyclic guanosine monophosphate (cGMP) to activate cGMP-dependent protein kinases PKG and downstream MAPK pathway, resulting in the increased tumor cell stemness and metastasis. Blocking de novo synthesis by silencing phosphoribosylpyrophosphate synthetase 2 (PRPS2) can effectively decrease the stemness of breast cancer cells and reduce the lung metastasis. More interestingly, in breast cancer patients, the level of plasma uric acid (UA), a downstream metabolite of purine, is tightly correlated with patient’s survival. Our study uncovered that increased de novo synthesis is a metabolic hallmark of metastatic breast cancer cells and its metabolites can regulate the signaling pathway to promote the stemness and metastasis of breast cancer.
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