HOPMCLDA: predicting lncRNA–disease associations based on high-order proximity and matrix completion

Background: It has been shown in numerous recent studies that long non-coding RNAs (lncRNAs) play a vital role in the regulation of various biological processes, as well as serve as a basis for understanding the causes of human illnesses. Thus, many researchers have developed matrix completion approaches to infer lncRNA–disease connections and enhance prediction performance by using similarity information. Objective: Most matrix completion approaches are solely based on the first-order or second-order similarity between nodes, and higher-order similarity is rarely considered. In view of this, we developed a computational method to incorporate higher-order similarity information into the similarity network with different weights using a decay function designed by a random walk with restart (DHOSGR). Methods: First, considering that the information will decay as the distance increases during network propagation, we defined a novel decay high-order similarity by combining the similarity matrix and its high-order similarity information through a decay function to construct a similarity network. Then, we applied the similarity network to the objective function as a graph regularization term. Finally, a proximal splitting algorithm was used to perform matrix completion to infer relationships between diseases and lncRNAs. Results: In the experiment, DHOSGR achieves a superior performance in leave-one-out cross validation (LOOCV) and 100 times 5-fold cross validation (5-fold-CV), with AUC values of 0.9459 and 0.9334±0.0016, respectively, which are better than other five previous models. Moreover, case studies of three diseases (leukemia, lymphoma, and squamous cell carcinoma) demonstrated that DHOSGR can reliably predict associated lncRNAs. Conclusion: DHOSGR can serve as a high efficiency calculation model for predicting lncRNA-disease associations.

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Comprehensive Analysis of Characteristics of Cuproptosis-Related LncRNAs Associated with Prognosis of Lung Adenocarcinoma and Tumor Immune Microenvironment

Chen,

Wen,

et al. 2024

Pharmaceuticals

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As a novel discovered mechanism of cell death, cuproptosis is copper-dependent and induces protein toxicity related to advanced tumors, disease prognosis, and human innate and adaptive immune response. However, it has not yet been fully established how the prognosis of lung adenocarcinoma (LUAD) is related to the immune microenvironment of cuproptosis-related lncRNAs using several bioinformatic techniques. In the study, 19 genes related to cuproptosis were collected. Subsequently, 783 lncRNAs related to the co-expression of cuproptosis were obtained. Moreover, the Cox model revealed and constructed four lncRNA (AC012020.1, AC114763.1, AL161431.1, AC010260.1) prognostic markers related to cuproptosis. Based on the median risk score (RS) values, patients were categorized into two groups: high risk and low risk. The Kaplan–Meier (KM) survival curve depicted a statistically significant overall survival (OS) rate among two groups. Principal component analysis (PCA) and receiver operator characteristic curve (ROC) proved that the model had promising ability in prognosis. The analysis of univariate and multivariate Cox regression revealed that RS served as an independent prognostic factor. Moreover, multivariate Cox regression was employed for the establishment of a nomogram of prognostic indicators. The tumor mutational burden (TMB) depicted a considerable difference between the two risk groups. The immunotherapy response of LUAD patients with high risk was improved compared to low risk patients. The study also revealed that drug sensitivity associated with LUAD was significantly linked to RS. The findings could be helpful to establish a good diagnosis, prognosis, and management regime for patients with LUAD.

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HOPMCLDA: predicting lncRNA–disease associations based on high-order proximity and matrix completion

Abstract: In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) is essential to biological processes, diagnosis, and treatments associated with complex diseases. However, infering the associations between diseases...

Cited by 3 publications

References 48 publications

NFMCLDA: Predicting miRNA-based lncRNA-disease associations by network fusion and matrix completion

NFMCLDA: Predicting miRNA-based lncRNA-disease associations by network fusion and matrix completion

DHOSGR: lncRNA-disease Association Prediction Based on Decay High-order Similarity and Graph-regularized Matrix Completion

Comprehensive Analysis of Characteristics of Cuproptosis-Related LncRNAs Associated with Prognosis of Lung Adenocarcinoma and Tumor Immune Microenvironment

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