Multiple Linear Regression Analysis of lncRNA-Disease Association Prediction Based on Clinical Prognosis Data

Prognostic value of lncRNA HOXA-AS3 in cervical cancer by targeting miR-29a-3p and its regulatory effect on tumor progression

BACKGROUND

With the promotion of human papillomavirus (HPV) vaccine, cervical cancer has become a current research hotspot, and lncRNA has been confirmed to be used in the research of different diseases. This article systematically expounds the regulation and potential mechanisms of HOXA cluster antisense RNA 3 (HOXA-AS3) in cervical cancer, and discusses its possibility as a prognostic biomarker for cervical cancer.

METHODS

Relative expression levels of HOXA-AS3 and miR-29a-3p in tissues and cells were determined by real-time quantitative polymerase chain reaction (RT-qPCR). The survival of cervical cancer patients was analyzed by Kaplan-Meier method and the cumulative survival function table was drawn. The proliferation, migration, and invasion levels of HOXA-AS3 in cells were detected according to cell counting kit-8 (CCK-8) and transwell method. The dual-luciferase reporter gene assay confirmed the mechanism of action between HOXA-AS3 and miR-29a-3p.

RESULTS

HOXA-AS3 was elevated and miR-29a-3p was decreased in tissues and cells of cervical cancer patients. Knockdown of HOXA-AS3 could inhibit the progression of cervical cancer and was more conducive to patient survival. Bioinformatics analysis confirmed that HOXA-AS3 negatively regulates cervical cancer development by sponging miR-29a-3p.

CONCLUSION

In this research, knockdown of HOXA-AS3 could alleviate the process of cervical cancer by sponging miR-29a-3p, suggesting that HOXA-AS3 may be a potential prognostic target of cervical cancer, which could provide a theoretical basis for future clinical research of cervical cancer.

A novel reliability-based regression model for medical modeling and forecasting

BACKGROUND AND AIMS

In recent decades, modeling and forecasting have played a significant role in the diagnosis and treatment of different diseases. Various forecasting models have been developed to improve data-based decision-making processes in medical systems. Although these models differ in many aspects, they all originate from the assumption that more generalizable results are achieved by more accurate models. This means that accuracy is considered as the only prominent feature to evaluate the generalizability of forecasting models. On the other side, due to the changeable medical situations and even changeable models' results, making stable and reliable performance is necessary to adopt appropriate medical decisions. Hence, reliability and stability of models' performance is another effective factor on the model's generalizability that should be taken into consideration in developing medical forecasting models.

METHODS

In this paper, a new reliability-based forecasting approach is developed to address this gap and achieve more consistent performance in making medical predictions. The proposed approach is implemented on the classic regression model which is a common accuracy-based statistical method in medical fields. To evaluate the effectiveness of the proposed model, it has been performed by using two medical benchmark datasets from UCI and obtained results are compared with the classic regression model.

RESULTS

Empirical results show that the proposed model has outperformed the classic regression model in terms of error criteria such as MSE and MAE. So, the presented model can be utilized as an appropriate alternative for the traditional regression model in making effective medical decisions.

CONCLUSIONS

Based on the obtained results, the proposed model can be an appropriate alternative for traditional multiple linear regression for modeling in real-world applications, especially when more generalization and/or more reliability is needed.

Machine learning approaches for predicting biomolecule-disease associations

Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule-disease prediction methods.

LncRNA-MYL2-2 and miR-124-3p Are Associated with Perioperative Neurocognitive Disorders in Patients after Cardiac Surgery

BACKGROUND

Perioperative neurocognitive disorders (PND) resulting from cardiac surgery is a complication with high morbidity and mortality. However, the pathogenesis is unknown.

METHODS

For the sake of investigating the risk factors and mechanism of PND, we collected the characteristics and neurological scores of patients undergoing cardiac surgery in the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University and Affiliated Hospital of Southwest Medical University from Jan 1, 2016 to Dec 11, 2018.

RESULTS

We found that age and left atrial thrombus are independent risk factors for PND after cardiac surgery. Furthermore, the serum of 29 patients was collected on the 7th day after cardiac surgery for detecting the expression of lncRNA-MYL2-2 and miR-124-3p. Increased lncRNA-MYL2-2 and decreased miR-124-3p in serum were associated with the decline of patients' cognition.

CONCLUSIONS

LncRNA-MYL2-2 and miRNA-124-3p may jointly participate in the occurrence and development of PND after cardiac surgery. These important findings are advantaged to further understand the pathogenesis of PND and prevent it, provide new biomarkers for the diagnosis and monitoring of PND.