Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

Abstract

Background Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence. Methods Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted. Results A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC. Conclusion Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction

“…[39,40] In our study, a significant finding was that elevated AFP levels independently correlated with an increased risk of tumor recurrence in patients with HCC who underwent hepatectomy. This finding aligns with a substantial body of previous research consistently demonstrating the prognostic significance of AFP in HCC [28,41,42] Elevated AFP levels in patients with HCC often indicate underlying tumor biology and behavior, potentially reflecting increased tumor burden, dedifferentiation, or an aggressive tumor phenotype. [28] The association between higher AFP levels and tumor recurrence suggests that tumors producing elevated AFP might possess distinctive characteristics, such as enhanced angiogenesis, increased invasiveness, or resistance to treatment modalities, contributing to a higher of recurrence.…”

“…This finding aligns with a substantial body of previous research consistently demonstrating the prognostic significance of AFP in HCC [ 28 , 41 , 42 ] Elevated AFP levels in patients with HCC often indicate underlying tumor biology and behavior, potentially reflecting increased tumor burden, dedifferentiation, or an aggressive tumor phenotype. [ 28 ] The association between higher AFP levels and tumor recurrence suggests that tumors producing elevated AFP might possess distinctive characteristics, such as enhanced angiogenesis, increased invasiveness, or resistance to treatment modalities, contributing to a higher likelihood of recurrence. [ 42 ] Monitoring AFP levels during postoperative follow-up holds significant clinical implications.…”

“…For high-risk patients with recurrence, aggressive perioperative treatments such as immunotherapy, targeted therapy, and TACE might improve treatment outcomes. [18][19][20] Several noninvasive diagnostic methods have been used to predict early recurrence, including CT, [25] MRI, [26] positron emission tomography, [27] tumor genetic testing, [28] and serological markers. [29] Ultrasound is a routine procedure for liver assessment and is widely used in clinical practice.…”

Gut microbiome as a biomarker for predicting early recurrence of HBV‐related hepatocellular carcinoma

“… 9 Similarly, Derosa et al 40 discovered that gut Akkermansia muciniphila abundance was associated with the clinical efficacy of PD‐1 inhibitors in patients with non–small‐cell lung cancer. As ER accounts for more than 70% of HCC recurrence, 41 we focused our study on the impact of gut microbiota on ER of HCC. In this study, we developed a predictive model for ER of HCC based on gut microbiota.…”

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

“…Through a combination of AI and traditional statistical methods such as chi-squared tests and survival analysis, their model was able to achieve an accuracy of tumor recurrence prediction of 74.2%. Similarly, Fu et al built an AI model that combined machine learning-derived lncRNA signatures with TNM stages and AFP values to predict early HCC recurrence [62]. They employed three widely used machine learning methods, LASSO, random forest and SVM-RFE, as well as multivariate Cox analysis to select the correct lncRNA signatures.…”