CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer

Abstract

Aims: This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression.

Background: Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients.

Objectives: Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition.

Methods: Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC.

Results: The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups.

Conclusion: Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.

Keywords: CDT1; Nonalcoholic fatty liver disease; early diagnosis; hepatocellular carcinoma; machine learning; single-cell sequencing.

Fig. (1)

Differential Gene Expression and Pathway Enrichment in Liver Tissues. (A) Volcano plot illustrating differential gene expression between normal liver tissue and non-alcoholic fatty liver (NAFLD) tissue. (B) Volcano plot illustrating differential gene expression between tumor liver tissue and normal liver tissue (C) Gene Set En-richment Analysis (GSEA) plot.

Fig. (2)

Overlap of Gene Expression and Functional Enrichment in NAFLD and HCC. (A) Venn diagram illustrating the overlap of differentially expressed genes between non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). (B) Dot plot of Gene Ontology (GO) term enrichment analysis for the union set of differentially expressed genes in both NAFLD and HCC.

Fig. (3)

Construction of LASSO Cox prognostic model and evaluation of the HCC Prognostic Model. (A) Lasso coefficient profiles of the prognostic features across the log(lambda) sequence. (B) Kaplan-Meier survival curves stratified by high and low prognostic risk scores. (C) Scatter plot of survival status of patients against their prog-nostic risk score. (D) Tuning parameter (lambda) selection in the lasso model used in cross-validation via mini-mum criteria and the 1-standard error rule. (E) Risk score distribution of patients. (F) Time-dependent Receiver Operating Characteristic (ROC) curves evaluating the predictive accuracy of the prognostic model at 1, 3, and 5 years.

Fig. (4)

External Validation of the Prognostic Model's Survival Predictions and Risk Stratification Accuracy. (A) Kaplan-Meier survival curves for external validation cohorts, stratified by derived risk scores. (B) Risk score distribution among the external validation cohort. (C) Time-dependent ROC curves showing the performance of the prognostic model in the external cohort at 1, 2, and 3 years.

Fig. (5)

Single gene Survival Analysis Stratified by Biomarker Expression. (A) Kaplan-Meier curve comparing the survival probability over time between patients expressing CDC6 biomarkers. (B) Kaplan-Meier curve comparing the survival probability over time between patients expressing IGSF3 biomarkers. (C) Survival probability over time for patients categorized by CDT1 expression. (D) Kaplan-Meier curve showcasing the difference in survival between patients with CENPW expression. The accompanying risk tables and p-value are displayed below the curves.

Fig. (6)

Gene Expression in Multiple GEO Datasets. (A) NFALD Liver Tissue Samples Comparing Normal. (B) Distinguishing Power of CDT1 Gene Between Normal and Disease. (C) Expression of CDT1 Gene Across Differ-ent NAFLD Activity Scores. *: 0.01<p<0.05.

Fig. (7)

Single-Cell RNA-Seq Analysis of Liver Tissue from NAFLD and HCC Patients. (A) UMAP (Uniform Manifold Approximation and Projection) visualization of clustered cell populations in liver tissue. (B) Violin plots displaying the expression distribution of selected marker genes across different cell identities. (C) UMAP visuali-zation of single-cell RNA-seq data showing the pseudo-temporal progression of cell states from paraneoplastic cells to cancer cells. (D) Ridge plots depicting the expression patterns of genes associated with NAFLD and HCC across different cell types. (E) Feature plots showing the pseudo-temporal expression trajectory of the same genes as in Panel D.

Fig. (8)

Immune cell infiltration score in high-risk vs. low-risk groups. This box plot displays the distribution of immune cell infiltration scores in high-risk (red) and low-risk (blue) patient groups.