A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs.

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer.

OBJECTIVE: The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer. METHOD: From the Surveillance, Epidemiology, and End Results (SEER) database, AYA women with primary breast cancer diagnosed from 2010 to 2018 were included in our study. A deep learning algorithm, referred to as DeepSurv, was used to construct a prognostic predictive model based on 19 variables, including demographic and clinical information. Harrell's C-index, the receiver operating characteristic (ROC) curve, and calibration plots were adopted to comprehensively assess the predictive performance of the prognostic predictive model. Then, a novel clinical risk stratification was constructed based on the total risk score derived from the prognostic predictive model. The Kaplan-Meier method was used to plot survival curves for patients with different death risks, using the log-rank test to compared the survival disparities. Decision curve analyses (DCAs) were adopted to evaluate the clinical utility of the prognostic predictive model. RESULTS: Among 14,243 AYA women with breast cancer finally included in this study, 10,213 (71.7%) were White and the median (interquartile range, IQR) age was 36 (32-38) years. The prognostic predictive model based on DeepSurv presented high C-indices in both the training cohort [0.831 (95% CI 0.819-0.843)] and the test cohort [0.791 (95% CI 0.764-0.818)]. Similar results were observed in ROC curves. The excellent agreement between the predicted and actual OS at 3 and 5 years were both achieved in the calibration plots. The obvious survival disparities were observed according to the clinical risk stratification based on the total risk score derived from the prognostic predictive model. DCAs also showed that the risk stratification possessed a significant positive net benefit in the practical ranges of threshold probabilities. Lastly, a user-friendly Web-based calculator was generated to visualize the prognostic predictive model. CONCLUSION: A prognostic predictive model with sufficient prediction accuracy was construct for predicting OS of AYA women with breast cancer. Given its public accessibility and easy-to-use operation, the clinical risk stratification based on the total risk score derived from the prognostic predictive model may help clinicians to make better-individualized management.

Effects of Tamoxifen vs. Toremifene on fatty liver development and lipid profiles in breast Cancer.

BACKGROUND: Tamoxifen (TAM) and Toremifene (TOR), two kinds of selective estrogen receptor modulators (SERMs), have equal efficacy in breast cancer patients. However, TAM has been proved to affect serum lipid profiles and cause fatty liver disease. The study aimed to compare the effects of TAM and TOR on fatty liver development and lipid profiles. METHODS: This study performed a retrospective analysis of 308 SERMs-treated early breast cancer patients who were matched 1:1 based on propensity scores. The follow-up period was 3 years. The primary outcomes were fatty liver detected by ultrasonography or computed tomography (CT), variation in fibrosis indexes, and serum lipid profiles change. RESULTS: The cumulative incidence rate of new-onset fatty liver was higher in the TAM group than in the TOR group (113.2 vs. 67.2 per 1000 person-years, p < 0.001), and more severe fatty livers occurred in the TAM group (25.5 vs. 7.5 per 1000 person-years, p = 0.003). According to the Kaplan-Meier curves, TAM significantly increased the risk of new-onset fatty liver (25.97% vs. 17.53%, p = 0.0243) and the severe fatty liver (5.84% vs. 1.95%, p = 0.0429). TOR decreased the risk of new-onset fatty liver by 45% (hazard ratio = 0.55, p = 0.020) and showed lower fibrotic burden, independent of obesity, lipid, and liver enzyme levels. TOR increased triglycerides less than TAM, and TOR increased high-density lipoprotein cholesterol, while TAM did the opposite. No significant differences in total cholesterol and low-density lipoprotein cholesterol are observed between the two groups. CONCLUSIONS: TAM treatment is significantly associated with more severe fatty liver disease and liver fibrosis, while TOR is associated with an overall improvement in lipid profiles, which supports continuous monitoring of liver imaging and serum lipid levels during SERM treatment.

Long Non-Coding RNA NLIPMT as a Tumor Suppressor and Inhibitor of Cell Proliferation and Metastasis in Papillary Thyroid Carcinoma.

INTRODUCTION: Gene expression association studies of tumor samples have uncovered several long non-coding RNAs (lncRNAs) closely related to various types of cancer. Several lncRNAs have been reported to play essential roles in the progression of papillary thyroid carcinoma (PTC). Novel lncRNA inhibiting proliferation and metastasis (lnc-NLIPMT) is a known regulator of mammary cell proliferation and motility, but its involvement in PTC is unclear. MATERIALS AND METHODS: We investigated the role of lnc-NLIPMT in PTC by quantitative real-time polymerase chain reaction (qRT-PCR) on various PTC tissue samples and cell lines. We assessed the effects of overexpression or knockdown of lnc-NLIPMT on the proliferation, migration, and invasion of PTC cells using CCK-8, cell clone formation, and transwell assays. Changes in the expression of N-cadherin and vimentin were detected by immunoblotting. RESULTS: Our results revealed a downregulation of the expression of lnc-NLIPMT in PTC and a negative correlation between lnc-NLIPMT expression and tumor size (P=0.006). Overexpression of lnc-NLIPMT in TPC-1 and B-CPAP cells significantly suppressed cell proliferation, migration, and invasion, while lnc-NLIPMT knockdown had the opposite effect. In addition, lnc-NLIPMT played an important role in the regulation of the expression of N-cadherin and vimentin. CONCLUSION: lnc-NLIPMT inhibits cell proliferation and metastasis of PTC cells and is a potential diagnostic and prognostic biomarker in PTC.