In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

Input: Consider Input: Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed to develop a lncRNA signature to improve prognosis prediction of colorectal cancer (CRC). Using a lncRNA-mining approach, we performed lncRNA expression profiling in large CRC cohorts from Gene Expression Ominus (GEO), including GSE39582 test series(N=436), internal validation series (N=117); and two independent validation series GSE14333 (N=197) and GSE17536(N=145). We established a set of six lncRNAs that were significantly correlated with the disease free survival (DFS) in the test series. Based on this six-lncRNA signature, the test series patients could be classified into high-risk and low-risk subgroups with significantly different DFS (HR=2.670; P<0.0001). The prognostic value of this six-lncRNA signature was confirmed in the internal validation series and another two independent CRC sets. Gene set enrichment analysis (GSEA) analysis suggested that risk score positively correlated with several cancer metastasis related pathways. Functional experiments demonstrated three dysregulated lncRNAs, AK123657, BX648207 and BX649059 were required for efficient invasion and proliferation suppression in CRC cell lines. Our results might provide an efficient classification tool for clinical prognosis evaluation of CRC.

Output: A long non-coding RNA signature to improve prognosis prediction of colorectal cancer

Input: Consider Input: Integrin-mediated platelet adhesion and aggregation are essential for sealing injured blood vessels and preventing blood loss, and excessive platelet aggregation can initiate arterial thrombosis, causing heart attacks and stroke. To ensure that platelets aggregate only at injury sites, integrins on circulating platelets exist in a low-affinity state and shift to a high-affinity state (in a process known as integrin activation or priming) after contacting a wounded vessel. The shift is mediated through binding of the cytoskeletal protein Talin to the subunit cytoplasmic tail. Here we show that platelets lacking the adhesion plaque protein Kindlin-3 cannot activate integrins despite normal Talin expression. As a direct consequence, Kindlin-3 deficiency results in severe bleeding and resistance to arterial thrombosis. Mechanistically, Kindlin-3 can directly bind to regions of -integrin tails distinct from those of Talin and trigger integrin activation. We have therefore identified Kindlin-3 as a novel and essential element for platelet integrin activation in hemostasis and thrombosis.

Output: Kindlin-3 is essential for integrin activation and platelet aggregation

Input: Consider Input: Robust multiarray analysis (RMA) is the most widely used preprocessing algorithm for Affymetrix and Nimblegen gene expression microarrays. RMA performs background correction, normalization, and summarization in a modular way. The last 2 steps require multiple arrays to be analyzed simultaneously. The ability to borrow information across samples provides RMA various advantages. For example, the summarization step fits a parametric model that accounts for probe effects, assumed to be fixed across arrays, and improves outlier detection. Residuals, obtained from the fitted model, permit the creation of useful quality metrics. However, the dependence on multiple arrays has 2 drawbacks: (1) RMA cannot be used in clinical settings where samples must be processed individually or in small batches and (2) data sets preprocessed separately are not comparable. We propose a preprocessing algorithm, frozen RMA (fRMA), which allows one to analyze microarrays individually or in small batches and then combine the data for analysis. This is accomplished by utilizing information from the large publicly available microarray databases. In particular, estimates of probe-specific effects and variances are precomputed and frozen. Then, with new data sets, these are used in concert with information from the new arrays to normalize and summarize the data. We find that fRMA is comparable to RMA when the data are analyzed as a single batch and outperforms RMA when analyzing multiple batches. The methods described here are implemented in the R package fRMA and are currently available for download from the software section of http://rafalab.jhsph.edu.
Output: Frozen robust multiarray analysis (fRMA).