Abstract
An increasing amount of studies have found that LncRNA plays an important role in various life processes of the body. In current prediction research on lncRNA-disease associations, correlation analysis of disease prognosis is overlooked. In this study, a logistic regression prediction model based on tumor clinical stage data and the expression quantity of lncRNA transcript is constructed. The proposed model is based on unknown human lncRNA-disease associations combining with the clinical stage data. Firstly, the importance of the characteristic variable is calculated by the proposed CVSgC-RF algorithm. Secondly, 95 lncRNAs, which are most closely related to prostate cancer, are calculated from 480 alternative lncRNAs by CASO and CVSe-CS-CF. On the basis of the above 95 lncRNAs, the CSPA-PL algorithm is used to select a further 22 lncRNAs that are most closely related to the tumor clinical stage for prostate cancer. Finally, 22 lncRNAs are used to construct a logistic regression prediction model. Additionally, this method is applied to lung cancer data; 16 lncRNAs are selected to construct a logistic regression prediction model for lung cancer. Experimental results show that the best results for ROC Area, the accuracy and recall rate of the prediction model are achieved by the proposed method for prostate cancer and lung cancer, which provides a promising basis for subsequent prediction studies of lncRNA-disease associations.
Highlights
Long non-coding RNA is non-coding RNA with more than 200 nucleotides in length [1]
Studies show that Long non-coding RNA (lncRNA) is closely correlated with many diseases, such as lung cancer [4], [5], Alzheimer’s disease [6], osteosarcoma [7], breast cancer [8], gastric cancer [9], colon cancer [10], prostate cancer [11], cervical cancer [12], etc
PERFORMANCE EVALUATION OF CVSGC-RF When constructing RF in the CVSgC-RF algorithm, the number of decision trees τ in the random forest has a large impact on the performance and efficiency of the algorithm
Summary
Long non-coding RNA (lncRNA) is non-coding RNA with more than 200 nucleotides in length [1]. It has very important biological functions and is another important area in the bioinformatics field [2], [3]. It is extremely important to study the relationship. Between lncRNA and the prognosis of cancer patients by utilizing clinical data. Current research in lncRNA is in the initial stages; people still know little about the deep mechanisms in the occurrence and development of cancer. It is important to study lncRNA, which has a significant impact on the prognosis of cancer patients by using bioinformatics combined with clinical data
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