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. 2025 Nov 10;15(22):2848.
doi: 10.3390/diagnostics15222848.

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

Geeitha Senthilkumar  1 , Renuka Pitchaimuthu  1 , Prabu Sankar Panneerselvam  2 , Rama Prasath Alagarswamy  3 , Seshathiri Dhanasekaran  4
Affiliations

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

Geeitha Senthilkumar et al. Diagnostics (Basel). .

Abstract

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.

Keywords: biomarker; long non-coding RNA; prognosis; recurrent cervical cancer; recurrent neural network.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed research method.
Figure 2
Figure 2
Correlation matrix of RCC.
Figure 3
Figure 3
A recurrent neural network LSTM to find the relevant features.
Figure 4
Figure 4
Training and validation accuracy loss.
Figure 5
Figure 5
Kaplan Meier survival curve by stages.
Figure 6
Figure 6
Violin plot for disease-free survival vs. staging categories.
Figure 7
Figure 7
Comparison of common features between tumor size and disease-free survival.
Figure 8
Figure 8
Kaplan Meier survival curve Comparison.
Figure 9
Figure 9
Risk score Distribution and survival time prediction.
Figure 10
Figure 10
Heatmap of Nine–lncRNA Signature expression level.
Figure 11
Figure 11
Time-dependent ROC curve for different follow-up times.
Figure 12
Figure 12
Mean-variance trend plot.
Figure 13
Figure 13
Moderated t-statistic plot.
Figure 14
Figure 14
LIMMA Differential expression.
See this image and copyright information in PMC

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