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- Title
Identifying Survival Subtypes of Esophageal Squamous Cell Carcinoma Patients: An Application of Deep Learning in Gene Expression Data Analysis.
- Authors
Kousehlou, Zahra; HajiZadeh, Ebrahim; Tapak, Leili; Shalbaf, Ahmad
- Abstract
Background: Esophageal squamous cell carcinoma (ESCC) is one of the most lethal types of cancer. Late diagnosis significantly decreases patient survival rates. Objectives: The study aimed to identify survival groups for patients with ESCC and find predictive biomarkers of time-to-death from ESCC using state-of-the-art deep learning (DL) and machine learning algorithms. Methods: Expression profiles of 60 ESCC patients, along with their demographic and clinical variables, were downloaded from the GEO dataset. A DL autoencoder model was employed to extract IncRNA features. The univariate Cox proportional hazard (Cox-PH) model was used to select significant extracted features related to patient survival. Hierarchical clustering (HC) identified risk groups, followed by a decision trees algorithm which was used to identify IncRNA profiles. We used Python.3.7 and R.4.0.1 software. Results: Inputs of the autoencoder were 8,900 long noncoding RNAs (IncRNAs), of which 1000 features were extracted. Out of the features, 42 IncRNAs were significantly related to time-to-death using the Cox-PH model and used as input for clustering of patients into high and low-risk groups (P-value of log-rank test = 0.022). These groups were then labeled for supervised HC. The C5.0 algorithm achieved an overall accuracy of 0.929 on the test set and identified four hub IncRNAs associated with time-to-death. Conclusions: Novel discovered IncRNAs lnc-FAM84A-1, UNC01866, lnc-KCNE4-2 and lnc-NUDT12-4 implicated in the pathogenesis of death from ESCC. Our findings represent a significant advancement in understanding the role of IncRNAs on ESCC prognosis. Further research is necessary to confirm the potential and clinical application of these IncRNAs.
- Subjects
RNA analysis; RNA physiology; SQUAMOUS cell carcinoma; RISK assessment; DEATH; RESEARCH funding; AUTOENCODER; RESEARCH evaluation; GENE expression profiling; STATISTICS; MACHINE learning; DECISION trees; ESOPHAGEAL cancer; BIOMARKERS; ALGORITHMS; TIME; PROPORTIONAL hazards models
- Publication
International Journal of Cancer Management, 2024, Vol 17, Issue 1, p1
- ISSN
2538-4422
- Publication type
Academic Journal
- DOI
10.5812/ijcm-145929