Advancing prognostic understanding in hepatocellular carcinoma through the integration of genomic instability and lncRNA signatures: GILncSig model

Abstract

The recently published study by Duan et al introduces a promising method that combines genomic instability and long non-coding RNAs to improve the prognostic evaluation of hepatocellular carcinoma (HCC), a deadly cancer associated with considerable morbidity and mortality. This editorial aims to analyze the methodology, key findings, and broader implications of the study within the fields of gastroenterology and oncological surgery, highlighting the shift towards precision medicine in the management of HCC.

Key Words: Genomic instability; Long non-coding RNA; Hepatocellular carcinoma; Prognosis

Core Tip: The clinical approach used in hepatocellular carcinoma (HCC) is variable due to its heterogeneity and the unpredictable outcomes, and many patients unfortunately have a poor prognosis. Thus, the discovery of new biomarker candidates is necessary to aid in HCC clinical practice. In light of this, genomic instability (GI) derived HCC genetic biomarkers have not been fully explored as a novel prognostic signature, and may have a key role in this field. Therefore, the potential use of GI and long non-coding RNAs in refining risk stratification and guiding therapeutic decision-making has emerged.

INTRODUCTION

Hepatocellular carcinoma (HCC) remains one of the leading causes of mortality among all malignancies[1]. Traditional treatment often requires a combination of tyrosine kinase inhibitors, characterization of the tumor microenvironment, and immune checkpoint inhibitors. The clinical trajectory of HCC is notoriously difficult to predict, due to its heterogeneous nature and the variable clinical outcomes observed in patients. Traditional prognostic indicators provide a limited understanding of individual patient risk profiles, necessitating advancements in prognostic modeling. Most patients show poor clinical outcomes and the discovery of new biomarker candidates, together with the determination of genomic instability (GI) is necessary for predicting HCC prognosis. GI origin has not yet been clarified, but despite this we know that it is a critical feature in tumors, conferring heterogeneity and increasing aggressiveness and drug resistance. On the other hand, a high burden of genetic mutations also provides neoantigens that might induce antitumoral immune responses. However, GI-derived genetic biomarkers for HCC have not been fully explored as a novel prognostic signature, which might also have predictive value. In light of this, long non-coding RNAs (lncRNAs) are promising diagnostic and prognostic biomarkers and several studies have shown their potential application as valid biomarkers for early diagnosis and as therapeutic targets[2]. Many lncRNAs have been associated with HCC such as: Metastasis-associated lung adenocarcinoma transcript 1, HOX transcript antisense intergenic RNA, highly upregulated in liver cancer, HCC up-regulated EZH2-associated lncRNA, BRAF-activated non-protein coding RNA, and nuclear enriched abundant transcript 1[3]. LncRNAs are involved in different processes, including modulation of the liver microenvironment and the response to GI. In consideration of this, a computational framework is provided to identify GI-related lncRNAs by combining the deregulation of these noncoding RNAs with tumor mutant phenotype. Several research groups have attempted to develop in silico derived signatures of lncRNAs related to GI to predict the prognosis and the immune landscape of HCC[4,5]. In this regard, Lee et al[6] recently described the function of the lncRNA “NORAD”, an important regulator of the activity of PUMs (PUF proteins, belonging to the family of RNA binding proteins in human cells). NORAD inactivation can induce chromosomal instability in previously stable diploid human cell lines. Moreover, in a recent report Lian et al[7] investigated the role of sixteen ferroptosis-related lncRNAs related to overall survival, tumor immune environment, and GI in HCC. In this scenario, Duan et al[8] added their contribution on GI, known to play a key role in the progression of HCC, proposing a lens through which the disease can be evaluated. GI, characterized by an increased rate of mutations within the genome, has been implicated in tumorigenesis, disease progression, and therapeutic resistance. By operationalizing GI through the quantification of lncRNA expression, this study sheds light on a previously unexplored facet of HCC pathophysiology.

CONSTRUCTING THE GILncSig AND ITS CLINICAL VALUE

The authors developed a GI-derived lncRNAs signature (GILncSig), linked with the TP53 mutation status, a well-established independent prognostic biomarker in HCC. This process utilizes extensive genomic and transcriptomic datasets from The Cancer Genome Atlas (TCGA), although it is acknowledged that this dataset alone may not entirely validate the efficacy of the GILncSig formula. In detail, Duan et al[8] exploited the TCGA liver cancer dataset splitting it into a training and testing set to build and test the obtained signatures. Patient stratification was obtained according to the somatic mutation profile into genomic unstable (GU) and genomic stable (GS), associated with the lncRNA expression. Hierarchical cluster analysis was then implemented to link lncRNA expression with the GU group and GS group and finally, 88 GI (GU) lncRNAs were then associated with mRNA expression using Pearson correlation. The identified transcripts were used to infer the biological landscape with GO and KEGG pathways analysis. The training set scoring formula was based on the HCC patients’ prognostic risk score and the lncRNA expression associated with the hazard ratio. Kaplan-Meier curves, receiver operating characteristic curves and Cox regression were crucial in validating and establishing the final GILncSig both in the training set and in the complete dataset, in combination with TP53 status, as well. Although this approach has been widely used in scientific literature, the specific scoring equations are becoming more and more reliable, resulting in an increase in precision and the number of reliable lncRNAs signatures discovered. The GILncSig introduces a refined prognostic model that categorizes HCC patients into distinct risk groups (divided in high and low risk) with unprecedented precision. This stratification facilitates the tailoring of therapeutic strategies to individual patient profiles, marking a significant advance towards personalized medicine in HCC treatment. Furthermore, the GILncSig provides a valuable resource for future research aimed at clarifying the molecular mechanisms underlying HCC, potentially uncovering new therapeutic targets within the landscape of GI and lncRNA biology. It is also worth noting that p53 mediated apoptosis is influenced by hepatitis B virus (HBV) infection, which represents one of the most relevant HCC risk factors. In particular, it is known that hepatitis B e-antigen can protect hepatocytes from apoptosis leading to the survival of infected hepatocytes toward the development of HBV chronicity[9]. Therefore, this might be a confounding factor to take into account while applying these signatures. The implications of this research showed a reevaluation of conventional approaches to HCC prognostication and treatment. By challenging existing paradigms, the work by Duan et al[8] stimulates the already critical debate within the scientific community, encouraging a complete reconsideration of how genomic data can be harnessed to enhance patient care. This editorial further amplifies the call for an integrated approach to cancer prognosis, one that embraces the complexities of GI and empowers the potential of lncRNAs as prognostic biomarkers.

CONCLUSION

The study by Duan et al has contributed to the paradigm shift in the prognostic evaluation of HCC, underscoring the potential of GI and lncRNAs to refine risk stratification and guide therapeutic decision-making[8]. On the other hand, it would be important to see how those newly found GILncSig behave in “real world” clinical practice with prediction of the prognosis and response to immunotherapy in HCC, as no validated biomarker is yet available to guide clinical decision-making. This is particularly interesting given the fact that the advantages and clinical response to programmed death-1 (PD-1)/programmed death-ligand 1 (PDL1) immunotherapy in HCC is becoming more and more significant, as also highlighted by a recent work[10]. As we advance towards a future where oncology is increasingly informed by molecular insights, the integration of comprehensive genomic signatures into clinical practice promises to enhance the precision and efficacy of HCC management. The journey toward this future will undoubtedly be complex, requiring continued innovation, validation, and collaboration across the fields of oncology, gastroenterology, and genomics, but today we are moving in the right direction.