Abstract
Introduction Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide. Due to varying underlying etiologies, the prognosis of HCC differs greatly among patients. It is important to develop ways to help stratify patients upon initial diagnosis to provide optimal treatment modalities and follow-up plans. The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. Methods The Cancer Genome Atlas (TCGA-LIHC) was analyzed for differentially expressed genes. Clinicopathological data were obtained from cBioPortal. ANN analysis of the 75 most significant genes predicting disease-free survival (DFS) was performed. Next, CTA results were used for creation of the scoring system. Cox regression was performed to identify the prognostic value of the scoring system. Results 363 patients diagnosed with HCC were analyzed in this study. ANN provided 15 genes with normalized importance >50%. CTA resulted in a set of three genes (NRM, STAG3, and SNHG20). Patients were then divided in to 4 groups based on the CTA tree cutoff values. The Kaplan–Meier analysis showed significantly reduced DFS in groups 1, 2, and 3 (median DFS: 29.7 months, 16.1 months, and 11.7 months, p < 0.01) compared to group 0 (median not reached). Similar results were observed when overall survival (OS) was analyzed. On multivariate Cox regression, higher scores were associated with significantly shorter DFS (1 point: HR 2.57 (1.38–4.80), 2 points: 3.91 (2.11–7.24), and 3 points: 5.09 (2.70–9.58), p < 0.01). Conclusion Long-term outcomes of patients with HCC can be predicted using a simplified scoring system based on tumor mRNA gene expression levels. This tool could assist clinicians and researchers in identifying patients at increased risks for recurrence to tailor specific treatment and follow-up strategies for individual patients.
Highlights
Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver malignancies and is currently the fourth most common cause of cancer-related death worldwide
A 10-fold cross validation methodology was used, in which the whole dataset was randomly divided and 90% of the patients were selected for the training step and 10% were selected for the final testing. e final model was the one that maximized the correct classification of patients by disease-free survival (DFS) outcomes. e importance of independent predictors represented a measure of how much the predicted values changed with variations of the independent variables
We used a simple scoring system (0 or 1 point) to give points for each gene based on the individual gene cutoff levels that were derived through Classification Tree Analysis (CTA)
Summary
Hepatocellular carcinoma (HCC) is the most common primary tumor of the liver and a leading cause of cancer death worldwide [1]. Canadian Journal of Gastroenterology and Hepatology fetoprotein (AFP) and AFP mRNA, have been found to be prognostic [7] They rely on significant tumor burden and often have poor sensitivity and specificity in relation to the cutoff value used; taking this into consideration, their usefulness is often questionable [8]. We found that the developed risk score was able to stratify patients into different risk groups for shorter DFS and OS. Such models should not be viewed as replacements for good clinical judgment but as additional instruments to assist clinicians in counseling and choosing individualized treatment strategies for every patient
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