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To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Kinases become one of important groups of drug targets. To identify more kinases being potential for cancer therapy, we developed an integrative approach for the large-scale screen of functional genes capable of regulating the main traits of cancer metastasis. We first employed self-assembled cell microarray to screen functional genes that regulate cancer cell migration using a human genome kinase siRNA library. We identified 81 genes capable of significantly regulating cancer cell migration. Following with invasion assays and bio-informatics analysis, we discovered that 16 genes with differentially expression in cancer samples can regulate both cell migration and invasion, among which 10 genes have been well known to play critical roles in the cancer development. The remaining 6 genes were experimentally validated to have the capacities of regulating cell proliferation, apoptosis and anoikis activities besides cell motility. Together, these findings provide a new insight into the therapeutic use of human kinases., From the Clinical Editor: This team of authors have utilized a self-assembled cell microarray to screen genes that regulate cancer cell migration using a human genome siRNA library of kinases. They validated previously known genes and identified novel ones that may serve as therapeutic targets., (Copyright © 2013 Elsevier Inc. All rights reserved.)