Long noncoding RNAs (lncRNAs) have emerged as potential prognostic markers in various human cancers as they participate in many malignant behaviors. However, the value of lncRNAs as prognostic markers among diverse human cancers is still under investigation, and a systematic signature based on these transcripts that related to pan-cancer prognosis has yet to be reported. In this study, we proposed a framework to incorporate statistical power, biological rationale, and machine learning models for pan-cancer prognosis analysis. The framework identified a 5-lncRNA signature (ENSG00000206567, PCAT29, ENSG00000257989, LOC388282, and LINC00339) from TCGA training studies (n = 1,878). The identified lncRNAs are significantly associated (all P ≤ 1.48E-11) with overall survival (OS) of the TCGA cohort (n = 4,231). The signature stratified the cohort into low- and high-risk groups with significantly distinct survival outcomes (median OS of 9.84 years versus 4.37 years, log-rank P = 1.48E-38) and achieved a time-dependent ROC/AUC of 0.66 at 5 years. After routine clinical factors involved, the signature demonstrated better performance for long-term prognostic estimation (AUC of 0.72). Moreover, the signature was further evaluated on two independent external cohorts (TARGET, n = 1,122; CPTAC, n = 391; National Cancer Institute) which yielded similar prognostic values (AUC of 0.60 and 0.75; log-rank P = 8.6E-09 and P = 2.7E-06). An indexing system was developed to map the 5-lncRNA signature to prognoses of pan-cancer patients. In silico functional analysis indicated that the lncRNAs are associated with common biological processes driving human cancers. The five lncRNAs, especially ENSG00000206567, ENSG00000257989 and LOC388282 that never reported before, may serve as viable molecular targets common among diverse cancers.

Research has shown that in-network caching for named data in ICN is an effective way to eliminate redundant network traffic. The caching strategy is the key to improve the efficiency of content distribution in the ICN. However, current complicated caching strategies have high latency and couple with network forwarding, which results in terrible forwarding performance and poor scalability of throughput. In this paper, we propose a novel mechanism for making caching decisions in the source. In our mechanism, to improve the forwarding speed, the decision result is encoded in the cache control field of the content packet so the content packet can be quickly filtered and forwarded by on-path routers with an almost-zero latency. In order to improve the accuracy of caching decision in the mechanism, we further present a lightweight caching strategy based on popularity ranking. In the experiment, we set up a POF-based ICN soft router with a split architecture and implement our caching decision mechanism inside. The result of the experiment shows that on-path decision latency can be reduced by at most 96.7 percentage in our mechanism and a scalable line-speed of at least 10 Gbps is guaranteed. Our mechanism is also lightweight which achieves similar performance with less than 1/20 memory consumption of other popularity-based strategies.