Identification of KEY lncRNAs and mRNAs Associated with Oral Squamous Cell Carcinoma Progression

Identification of Key lncRNAs Associated with Immune Infiltration and Prognosis in Gastric Cancer

Long non-coding RNAs (lncRNAs), as promising novel biomarkers for cancer treatment and prognosis, can function as tumor suppressors and oncogenes in the occurrence and development of many types of cancer, including gastric cancer (GC). However, little is known about the complex regulatory system of lncRNAs in GC. In this study, we systematically analyzed lncRNA and miRNA transcriptomic profiles of GC based on bioinformatics methods and experimental validation. An lncRNA-miRNA interaction network related to GC was constructed, and the nine crucial lncRNAs were identified. These 9 lncRNAs were found to be associated with the prognosis of GC patients by Cox proportional hazards regression analysis. Among them, the expression of lncRNA SNHG14 can affect the survival of GC patients as a potential prognostic marker. Moreover, it was shown that SNHG14 was involved in immune-related pathways and significantly correlated with immune cell infiltration in GC. Meanwhile, we found that SNHG14 affected immune function in many cancers, such as breast cancer and esophageal carcinoma. Such information revealed that SNHG14 may serve as a potential target for cancer immunotherapy. As well, our study could provide practical and theoretical guiding significance for clinical application of non-coding RNAs.

Prognostic signature construction and immunotherapy response analysis for Uterine Corpus Endometrial Carcinoma based on cuproptosis-related lncRNAs

BACKGROUND

As a general female malignant tumor, Uterine Corpus Endometrial Carcinoma (UCEC) has high mortality and relapses. Cuproptosis was found to play an essential role in tumor by more and more researches. However, it is still unclear of the prognostic value and function of cuproptosis related Long non-coding RNA (lncRNA) in UCEC.

METHODS

Sequencing data with the corresponding clinical data and cuproptosis-related genes (CRGs) data were obtained from the Cancer Gene Atlas (TCGA) database and cuproptosis related studies. Pearson test was applied to select cuproptosis-related lncRNAs (CRLs). Prognosis associated CRLs was identified by univariate Cox analysis and the predictors were determined by least absolute shrinkage and selection operator (Lasso)-Cox and multivariate Cox analyses to construct the cuproptosis-related lncRNA prognostic signature (CRLPS). The performance of the CRLPs was evaluated by consistency index (C-index) and Kaplan-Meier analysis. A nomogram model was constructed for survival prediction and the accuracy of the model was evaluated by calibration curve. Finally, immune related analyses were applied to predict immune responses and identify drugs with potential efficacy for the overall survival (OS).

RESULTS

A total of 734 CRLs were found and 29 of them were identified as prognosis related lncRNAs. 12 CRLs were finally determined to build the CRLPS which revealed good ability on prognosis predicting. Subsequently, risk score of the CRLPS and grade were assessed as independent prognosis factors for UCEC, based on which the prognostic model provided the highest prediction accuracy of 99.7%. The calibration curve suggested that the prediction results consisted well with the observation. Enrichment analysis showed the CRLPS was mainly associated with tumor development and immune response. Patients in low tumor mutation burden (TMB) group had poorer OS. Significant difference was found in tumor immune dysfunction and exclusion (TIDE) score between different risk score groups. Finally, based on the CRLPs, drug sensitivity analysis identified nine anticancer drugs with potential efficacy on prognosis.

CONCLUSION

Cuproptosis-related lncRNA prognostic signature was constructed for UCEC for the first time. Its high reliability and accuracy on predicting prognosis and immunotherapy response provided new perspective to explore the tumor mechanism and improve clinical prognosis. Nine discovered sensitive drugs provided important clues for personalized treatment of UCEC.

MILNP: Plant lncRNA-miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation

Knowledge of the interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) is the basis of understanding various biological activities and designing new drugs. Previous computational methods for predicting lncRNA-miRNA interactions lacked for plants, and they suffer from various limitations that affect the prediction accuracy and their applicability. Research on plant lncRNA-miRNA interactions is still in its infancy. In this paper, we propose an accurate predictor, MILNP, for predicting plant lncRNA-miRNA interactions based on improved linear neighborhood similarity measurement and linear neighborhood propagation algorithm. Specifically, we propose a novel similarity measure based on linear neighborhood similarity from multiple similarity profiles of lncRNAs and miRNAs and derive more precise neighborhood ranges so as to escape the limits of the existing methods. We then simultaneously update the lncRNA-miRNA interactions predicted from both similarity matrices based on label propagation. We comprehensively evaluate MILNP on the latest plant lncRNA-miRNA interaction benchmark datasets. The results demonstrate the superior performance of MILNP than the most up-to-date methods. What's more, MILNP can be leveraged for isolated plant lncRNAs (or miRNAs). Case studies suggest that MILNP can identify novel plant lncRNA-miRNA interactions, which are confirmed by classical tools. The implementation is available on https://github.com/HerSwain/gra/tree/MILNP.

GBDTLRL2D Predicts LncRNA-Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA-disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA-disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA-disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.