Integrative Long Non-Coding RNA Analysis and Recurrence Prediction in Cervical Cancer Using a Recurrent Neural Network.

Background: Recurrent cervical cancer is one of the most defining threats to patient longevity, underscoring the need for prognostic models to identify high-risk patients. Objectives: The aim of the study is to integrate clinical data with the GSE44001 Dataset to identify key risk factors associated with the recurrence of cervical cancer. Patients are stratified into high-, moderate-, and low-risk groups using selected clinical and molecular features. Identifying a long non-coding RNA (lncRNA) gene signature associated with recurrent cervical cancer. Methods: From the total data collected, 138 recurrent cervical cancer patients were identified. GSE44001 Dataset is downloaded from the NCBI GEO Database. When using the GENCODE Annotation tool, the long non-coding RNA is filtered. The dataset is then linked with filtered long non-coding RNA. The Least Absolute Shrinkage Selection Operator (LASSO) is employed to find attributes in gene expression analysis. Risk factors of recurrent cervical cancer are identified. Risk value is assigned to each individual based on the selected lncRNAs and the corresponding overfitting coefficients. Result: The RNN Long Short-Term Memory model demonstrates a prognostic value, where high-risk patients experience a shorter duration of recurrence-free survival (p < 0.05). Individuals with a recurrence of cervical carcinoma, a progressive disease, were associated with the ATXN8OS marker, the C5orf60 indicator, and the INE1 index gene. In contrast, patients diagnosed at earlier stages are aligned with the KCNQ1DN marker, LOH12CR2 gauge, RFPL1S value, and KCNQ1OT1 indicator. Patients in moderate stages were primarily associated with the EMX2OS score. Conclusions: The research findings demonstrate that the nine-lncRNA signature, when combined with deep learning, offers a powerful approach for recurrence risk stratification in cervical cancer.