An Autophagy-related Long Non-coding RNA Prognostic Signature for Laryngeal Squamous Cell Carcinoma

Abstract

Background: Laryngeal squamous cell carcinoma (LSCC) is the second most common malignant tumor in the head and neck. Considering the role of autophagy in tumor development and drug resistance, we investigated the potential prognostic value of autophagy-related long no-coding RNAs (lncRNAs) in LSCC patients. Methods: Autophagy-related lncRNAs were screened out based on the Cancer Genome Atlas (TCGA) database. Subsequently, five autophagy-related lncRNAs with prognostic value were identified through univariate and multivariate Cox regression analysis. Based on these prognosis-related lncRNAs, a risk signature was established, and the patients with LSCC were divided into the low- and high-risk groups. Finally, a nomogram based on the prognosis signature was constructed. Results: The overall survival time of the high-risk group was significantly shorter than that of the low-risk one (p<0.0001). Receiver operating characteristic curve (ROC) analysis was performed to further confirm the validity of the model (ROC of training set: 0.841, ROC of test set: 0.718, ROC of entire set: 0.757). Univariate and multivariate regression analyses demonstrated that ours is an independent prognosis signature of LSCC. The nomogram integrating the five-lncRNA signature and clinical factors was then constructed for clinical application. Conclusion: In summary, our signature of the five autophagy-associated lncRNAs can serve as an effective prognostic indicator for patients with LSCC.

Failure diagnosis of a compressor subjected to surge events: A data-driven framework

“…The last one is a disturbing component that must be identified and removed during the preprocessing phase to improve the succeeding analysis. The EMD is a data-driven filtering approach whose introduction is based on the Hilbert-Huang transform [26]. The EMD decomposes the acquired signal into a series of components named IMFs and a residual term [59], as shown by Eq.…”

“…To overcome the aforementioned limitations, Huang et al [26] developed the Empirical Mode Decomposition (EMD), which is very suitable for dealing with the non-stationarity and nonlinearity of time series. Also, EMD does not need the indication of a basic function such as most WTs [27].…”