Strategies for discovering novel hepatocellular carcinoma biomarkers

Abstract

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Key Words: Hepatocellular carcinoma; Biomarkers; Multi-omics; Early detection; Liver cancer

Core Tip: This review provides a comprehensive analysis of current and emerging biomarkers for hepatocellular carcinoma, emphasizing the significance of multi-omics approaches in enhancing diagnostic accuracy and treatment strategies. It highlights the challenges in biomarker discovery, including pre-analytical, analytical, and post-analytical factors, and underscores the need for large-scale validation and cross-disciplinary collaboration. The insights presented aim to advance early detection, improve patient outcomes, and pave the way for personalized treatment approaches in liver cancer management.

INTRODUCTION

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge, ranking as the sixth most common cancer and the third leading cause of cancer-related death worldwide. The burden of HCC is disproportionately high in regions such as Asia and sub-Saharan Africa, where chronic hepatitis B and C infections are prevalent, significantly increasing the risk of liver cirrhosis and subsequent HCC development. Despite advancements in therapeutic interventions, the prognosis for patients with HCC remains poor because of the typically late stage at which it is diagnosed[1]. Early detection and diagnosis are critical for improving survival rates, yet current diagnostic methods remain insufficient for identifying HCC in its early stages, underscoring the need for novel biomarkers[2]. In addition to exploring emerging and novel biomarkers, this review briefly addresses established markers, such as alpha-fetoprotein (AFP), which, despite known limitations, continues to play a role in HCC diagnostics. This inclusion provides a comprehensive perspective on both current practices and future directions in HCC biomarker research.

Currently, the most widely used biomarker for HCC screening and diagnosis is AFP, which has been in clinical use for several decades. However, the sensitivity and specificity of AFP are suboptimal, particularly in detecting early-stage HCC, leading to a significant number of false-positives and negatives[3]. Additionally, AFP levels can be elevated in patients with nonmalignant liver conditions, such as hepatitis and cirrhosis, further complicating its utility as a reliable biomarker[4]. Consequently, a pressing need is to identify and validate novel biomarkers that can increase the accuracy of HCC detection, particularly in the early stages of the disease when curative treatments are more likely to be effective.

Emerging research has identified several promising candidates for HCC biomarkers, including des-gamma-carboxy prothrombin (DCP), glypican-3 (GPC3), and various microRNAs (miRNAs)[5,6]. DCP, also known as prothrombin induced by vitamin K absence-II, has shown potential as a more specific marker for HCC, particularly in combination with AFP[7]. Studies have demonstrated that the combined use of AFP and DCP increases the sensitivity and specificity of HCC detection compared with AFP alone[8]. Similarly, GPC3, a membrane-bound heparan sulfate proteoglycan, is overexpressed in HCC tissues and has been proposed as a diagnostic marker, especially for AFP-negative HCC[9]. Moreover, miRNAs, which are small, noncoding RNAs involved in gene regulation, have emerged as potential biomarkers because of their stability in blood and their distinct expression patterns in cancerous vs noncancerous tissues[10].

In recent years, the complexity of HCC has driven the search for innovative diagnostic and prognostic biomarkers. While several reviews have discussed individual biomarker pathways for HCC, this review uniquely focuses on a multi-omics perspective, integrating genomics, proteomics, and metabolomics. This comprehensive approach is crucial, as it provides a more holistic view of the molecular landscape of HCC, offering insights that go beyond single-modality studies. By combining these cutting-edge methodologies, this review highlights not only the individual utility of emerging biomarkers but also the potential of multi-omics approaches to enhance biomarker discovery, accuracy, and clinical application. This multi-omics integration is positioned to advance early detection and targeted therapeutic strategies, distinguishing this review as a forward-looking analysis that aligns with the latest trends in personalized cancer care.

Despite these advancements, several challenges remain in the identification and clinical implementation of novel HCC biomarkers. The heterogeneity of HCC, which arises from various etiological factors, such as viral hepatitis, alcohol consumption, and nonalcoholic fatty liver disease, complicates the development of universal biomarkers[11]. Moreover, the lack of large-scale, multicenter studies hinders the validation of these biomarkers across different populations and clinical settings. Additionally, integrating these biomarkers into existing clinical workflows, such as routine screening programs, requires a careful consideration of cost-effectiveness and accessibility, particularly in resource-limited settings.

Although it has been commonly believed that HCC progresses and develops metastases too rapidly for early detection to be practical, new research indicates otherwise. Studies suggest that the timeline for HCC progression from an early stage to metastasis can vary significantly, with some studies identifying specific biomarkers that can predict the potential for early metastasis[12]. For instance, research has shown that certain genetic and molecular markers, such as PRC1 and RACGAP1, are not only diagnostic biomarkers for early HCC but also play critical roles in the progression and metastasis of the disease, contributing to the development of metastatic capabilities over time[13]. Based on these findings, there appears to be a significant window of opportunity for the early detection of HCC, which could be crucial in improving patient outcomes. This reinforces the importance of ongoing research to discover and validate novel methods for the early detection of HCC, potentially before the disease progresses to an advanced, metastatic stage[14]. This revised understanding of HCC progression highlights the need for continued development of early detection strategies and the validation of biomarkers that can accurately predict the risk of metastasis in patients with early-stage HCC (Figure 1).

Figure 1 The critical need for novel biomarkers in hepatocellular carcinoma. It can take a period of time for a tumor to evolve from its initial stages to metastasis stage. As the disease advances, the likelihood of survival decreases significantly, especially once the cancer has reached later stages. The poor prognosis associated with hepatocellular carcinoma is largely due to the limited diagnostic capability of existing biomarkers, which often leads to diagnosis at a stage where the tumor has already gained metastatic potential. Consequently, there is a pressing need for new biomarkers that can detect hepatocellular carcinoma in its early stages, thereby enhancing the chances of patient survival.

The future of HCC diagnostics lies in a multiomics approach that combines genomics, proteomics, and metabolomics to develop a comprehensive biomarker panel that can capture the complexity of the disease[15]. This integrative approach, coupled with advances in high-throughput screening technologies, holds promise for improving the early detection of HCC, ultimately leading to better patient outcomes. However, the successful translation of these discoveries into clinical practice will require concerted efforts in biomarker validation, standardization of testing protocols, and collaboration across research institutions and health care providers.

CURRENT BIOMARKERS FOR HCC

The detection and management of HCC mainly rely on the use of biomarkers, which are critical for early diagnosis and monitoring of disease progression. Over the past few decades, several biomarkers have been identified, with AFP being the most established and widely used. Despite its extensive use, AFP has limitations, particularly in terms of sensitivity and specificity, especially for early-stage HCC[3]. These limitations have led to the exploration of additional biomarkers, such as DCP and Lens culinaris agglutinin (LCA)-reactive fraction of AFP (L3-AFP), which may offer improved diagnostic capabilities when used alone or in combination with AFP[5]. Understanding the utility and limitations of these biomarkers is crucial for optimizing HCC screening and improving patient outcomes. This section explores both the established biomarkers and emerging alternatives that are enhancing the diagnostic landscape for HCC.

AFP

AFP is the most established biomarker for HCC and has been extensively used for screening, diagnosing, and monitoring this disease. AFP is a glycoprotein produced by the fetal liver and yolk sac, and its levels are typically elevated in the serum of patients with HCC[16]. However, the utility of AFP as a sole diagnostic marker has been questioned because of its variable sensitivity and specificity, particularly in detecting early-stage HCC. Studies have shown that AFP levels can be elevated in patients with nonmalignant liver diseases such as hepatitis and cirrhosis, leading to false-positives[17].

Recent studies have focused on refining the use of AFP by establishing optimal cutoff values and combining it with other biomarkers to increase diagnostic accuracy[18]. For example, an AFP level of 20 ng/mL is commonly used as a threshold, but this value offers limited specificity[18]. Increasing the threshold to 200 ng/mL improves specificity but significantly reduces sensitivity, particularly in detecting early-stage HCC. This trade-off has prompted the development of complementary biomarkers, such as DCP and L3-AFP, which can be used in conjunction with AFP to improve diagnostic performance[19].

Despite its limitations, AFP remains a valuable tool, particularly in resource-limited settings where advanced imaging technologies may not be readily available. Its role in monitoring the treatment response and detecting recurrence posttreatment is also well established. However, the clinical community continues to explore the integration of AFP with newer biomarkers and imaging techniques to create a more robust diagnostic framework for HCC[19]. Given these limitations, researchers have turned to complementary biomarkers such as DCP and L3-AFP to increase diagnostic accuracy[19].

DCP

Given the need for more specific markers, DCP has emerged as a significant biomarker, particularly because of its ability to distinguish HCC from other liver conditions. DCP, also known as prothrombin induced by vitamin K absence-II, is an important biomarker used in the diagnosis and management of HCC[20]. Unlike AFP, DCP is an abnormal form of prothrombin produced by malignant hepatocytes due to impaired vitamin K metabolism, and its levels are not typically elevated in patients with nonmalignant liver conditions. The elevation of DCP in HCC patients is attributed to vitamin K deficiency, which affects prothrombin processing in malignant hepatocytes. Vitamin K serves as a cofactor for the carboxylation of prothrombin, a process that is disrupted in HCC, leading to the production of DCP, an abnormal prothrombin variant. This impaired pathway is unique to HCC cells, distinguishing DCP as a more specific biomarker for HCC when compared to benign liver conditions where vitamin K processing remains intact[21,22]. This property makes DCP a potentially more specific marker for HCC, particularly in distinguishing HCC from other chronic liver diseases[20,21].

The clinical utility of DCP is especially notable in patients with large tumors, where it has shown greater sensitivity than AFP. Studies have demonstrated that while AFP is superior in detecting smaller tumors, DCP becomes more effective as the tumor size increases[22,23]. For example, Nakamura et al[22] in 2006 reported that the sensitivity of DCP for tumors larger than 5 cm was significantly greater than that of AFP, suggesting its potential role in the diagnosis of advanced HCC. Furthermore, the combination of DCP and AFP has been shown to increase the diagnostic accuracy, providing greater sensitivity than either marker alone[23].

Recent advancements in the detection of DCP, such as the development of high-sensitivity electrochemiluminescence immunoassays, have improved its utility in clinical practice. These assays allow for the detection of lower concentrations of DCP, which can be crucial for the early diagnosis of high-risk patients. Shimizu et al[21] in 2002 highlighted that periodic measurements of serum DCP levels using electrochemiluminescence immunoassays are highly effective for screening and predicting the development of HCC, especially in cirrhotic patients.

L3-AFP

L3-AFP, a glycosylated form of AFP that specifically binds to LCA, represents a more refined biomarker for HCC detection. Compared with total AFP, this specific isoform of AFP is considered to have greater specificity for HCC, particularly in differentiating malignant from benign liver conditions. L3-AFP’s diagnostic value is tied to its unique glycosylation pattern, which allows it to specifically bind to the LCA lectin. This affinity for LCA is a characteristic of malignant hepatocytes in HCC, enhancing its specificity compared to total AFP. The altered glycosylation observed in L3-AFP reflects the metabolic changes in HCC cells and contributes to its role as a targeted biomarker for distinguishing HCC from other liver conditions with higher accuracy[24,25]. The clinical relevance of L3-AFP lies in its ability to identify patients with HCC who have normal or mildly elevated levels of total AFP, thus providing an additional diagnostic tool[24]. Studies have shown that L3-AFP is particularly useful when it is combined with other biomarkers[24,25]. For example, Leerapun et al[25] in 2007 reported that the combination of L3-AFP with AFP significantly improves diagnostic accuracy, making it a valuable addition to HCC screening protocols. Additionally, in patients with low AFP levels (less than 20 ng/mL), L3-AFP has been shown to detect HCC more reliably, particularly in patients where traditional AFP measurements fail to indicate the presence of malignancy[24]. Furthermore, meta-analyses and clinical studies have supported the use of L3-AFP as a prognostic marker, with higher levels correlating with poorer outcomes and more aggressive disease features[26,27]. These findings have led to its inclusion in various clinical guidelines as a complementary marker to AFP and DCP, particularly in high-risk populations[26,27]. In addition to DCP, L3-AFP provides further refinement of HCC detection, particularly in patients where AFP alone may not be sufficient.

Clinical utility and integration with treatment paradigms

The clinical relevance of emerging HCC biomarkers extends beyond early diagnosis and into therapeutic monitoring, offering new avenues for personalized medicine in HCC management. Biomarkers such as AFP, DCP, and specific miRNAs have shown potential in predicting treatment response and may serve as valuable tools for guiding therapeutic decision-making. For instance, rising AFP levels during treatment may indicate tumor progression or resistance to therapy, allowing clinicians to adjust treatment regimens accordingly[24]. Similarly, DCP has been investigated for its role in tracking response to locoregional therapies like transarterial chemoembolization and radiofrequency ablation. Studies indicate that patients with decreasing DCP levels post-therapy exhibit better outcomes, suggesting its potential utility as a response biomarker[21].

In the realm of personalized medicine, miRNAs offer promising applications. Certain miRNAs, such as miR-21 and miR-122, are linked with pathways that impact chemotherapy sensitivity, making them potential markers for predicting patient response to specific chemotherapeutics[10]. For instance, miR-122 downregulation has been associated with resistance to the chemotherapeutic agent sorafenib, highlighting its potential as a predictor of drug efficacy[22]. Integrating miRNA panels with current treatment regimens could therefore enable tailored therapeutic strategies based on individual biomarker profiles, enhancing treatment efficacy and minimizing adverse effects.

These biomarkers also align with emerging immunotherapy approaches in HCC. Biomarkers that correlate with immune activity, such as specific immune-related miRNAs, could help in identifying patients likely to benefit from immune checkpoint inhibitors or other immunotherapies. Future research into these relationships may provide actionable insights into optimizing treatment plans based on biomarker-driven profiles, further advancing personalized treatment for HCC patients.

Evidence for diagnostic accuracy of biomarker combinations

We have revised sections discussing biomarker sensitivity and specificity to include recent, evidence-based data. For example, studies by Tsuchiya et al[6] and Lim et al[23] have demonstrated that the combination of AFP and DCP enhances diagnostic accuracy for HCC, particularly in high-risk populations. Tsuchiya et al’s study, a multicenter trial, reported that combining AFP and DCP achieved a sensitivity of 82% and specificity of 88% for early-stage HCC detection, improving outcomes compared with either biomarker alone[6]. Lim et al[23] further underscored that AFP and DCP together reduced false positives in cirrhotic patients, highlighting the value of combining these markers in clinical settings. Additionally, we have strengthened our literature review by including recent large-scale studies, such as those by Luo et al[18] and Choi et al[19], which provide broader cohort analyses on biomarker effectiveness across diverse patient populations. These studies underscore the reliability and utility of multi-biomarker panels, further supporting their integration into screening protocols and personalized treatment plans.

EMERGING BIOMARKERS

While current biomarkers such as AFP and DCP play critical roles, their limitations underscore the need for novel biomarkers that can further improve early detection and prognostication. The ongoing challenges of the early detection and accurate diagnosis of HCC have spurred the investigation of novel biomarkers that could complement or surpass traditional biomarkers such as AFP and DCP. Emerging biomarkers, such as GPC3, Golgi protein 73 (GP73), osteopontin (OPN), and miRNAs, have shown promise in improving the sensitivity and specificity of the HCC diagnosis. These biomarkers, each with unique biological roles and expression patterns in HCC, have potential not only for early detection but also for prognostication and determining targeted therapy[28,29].

GPC3

GPC3 is a member of the glypican family of heparan sulfate proteoglycans, which are anchored to the cell surface and play crucial roles in cellular growth and differentiation. GPC3 has emerged as a significant biomarker for HCC because of its high expression in malignant liver cells and its low expression in normal liver tissue and benign liver conditions. This differential expression makes GPC3 a useful tool for distinguishing HCC lesions from nonmalignant liver lesions[30,31].

GPC3 is overexpressed in up to 72% of HCC cases, making it more sensitive than AFP in detecting smaller tumors. Hsu et al[32] first reported elevated levels of GPC3 in HCC, which was later confirmed at the protein level via immunohistochemistry in 1997. GPC3 expression in HCC correlates with tumor progression and a poor prognosis, further highlighting its importance not only as a diagnostic marker but also as a prognostic tool[33]. Moreover, GPC3 has also been explored as a therapeutic target[34]. The interaction of GPC3 with Wnt signaling pathways, which are implicated in the growth and survival of cancer cells, has led to the development of therapeutic strategies targeting GPC3. These treatments include monoclonal antibodies and GPC3-derived peptide vaccines that are currently being tested in clinical trials[30,35]. These therapies aim to inhibit tumor growth by blocking GPC3 function or inducing an immune response against GPC3-expressing cells[9,36].

The use of GPC3 as a serum biomarker is still under investigation. Initial studies suggested that GPC3 could be detected in the serum of HCC patients, but further research is needed to validate its efficacy and establish standardized protocols for its use in clinical settings[32]. Nonetheless, the role of GPC3 in HCC continues to be a focal point of research and is promising for enhancing both the diagnosis and treatment of this challenging disease[28,30,33].

GP73

GP73, also known as Golgi membrane protein 1, is a transmembrane glycoprotein that is expressed primarily in epithelial cells. Initially identified in patients with liver disease, GP73 has garnered attention as a potential biomarker for HCC[37]. Unlike AFP, which is produced by fetal liver cells and re-expressed in HCC, GP73 is primarily a resident protein of the Golgi apparatus but can be secreted into the bloodstream under pathological conditions, particularly in liver diseases[38].

Serum GP73 levels are significantly elevated in patients with HCC compared with those with nonmalignant liver diseases, making it a promising marker for distinguishing HCC from cirrhosis and chronic hepatitis[39]. Marrero et al[40] in 2005 reported that GP73 had a higher sensitivity and specificity for HCC than AFP, particularly for early-stage tumors. These properties make GP73 a valuable tool in the clinical setting, especially for patients who are AFP-negative.

The mechanism by which GP73 is released into the serum is not entirely understood, but it is believed to involve a posttranslational modification and cleavage process within the Golgi apparatus, followed by secretion into the bloodstream. This secretion process is thought to be enhanced in malignant hepatocytes, which exhibit altered Golgi function[39]. Research by Bachert et al[41] in 2007 has shown that GP73 is not only upregulated in HCC but also in other malignancies, although to a lesser extent, which suggests a need for careful interpretation of elevated GP73 levels in the context of a HCC diagnosis.

Despite its potential, GP73 is not without limitations. Some studies have shown that GP73 levels can also be elevated in patients with non-HCC liver diseases such as cirrhosis and hepatitis, which may lead to false-positives[42]. Additionally, the standardization of GP73 measurement techniques, such as enzyme-linked immunosorbent assays, is still an area of ongoing research. The variability in assay results across different studies highlights the need for further validation before GP73 can be fully integrated into routine clinical practice[43,44].

OPN

OPN is a glycoprotein involved in various physiological processes, including bone remodeling, immune responses, and tissue repair. In the context of cancer, OPN has been implicated in tumor growth, metastasis, and angiogenesis. Its role as a biomarker for HCC has garnered increasing attention because its expression is elevated in malignant hepatocytes compared with nontumor tissues. The ability of OPN to promote cell migration and invasion makes it particularly relevant in the study of HCC progression and metastasis[45]. Studies have shown significantly higher OPN levels in the serum of HCC patients than in that of individuals with benign liver conditions[46]. Zhu et al[47] reported that OPN, in combination with AFP, could improve the diagnostic accuracy for HCC, particularly in AFP-negative patients. This property has made OPN a valuable complementary marker to AFP, enhancing the overall sensitivity of HCC detection[47]. Moreover, the role of OPN in the tumor microenvironment, particularly its interaction with integrins and CD44 receptors on cancer cells, has made it a target for therapeutic intervention[45]. Blocking OPN signaling pathways has been shown to reduce tumor growth and metastasis in experimental models, suggesting that OPN could be both a biomarker and a therapeutic target in HCC[48]. However, similar to the use of GP73, the use of OPN as a biomarker is complicated by its elevated levels in patients with other conditions, such as patients with chronic inflammation and fibrosis, which can lead to false-positives[46]. The development of more specific assays and the combination of OPN with other biomarkers may help mitigate these issues, making it a more reliable tool in the clinical diagnosis and management of HCC[49].

MiRNAs

MiRNAs are small, noncoding RNA molecules that play critical roles in regulating gene expression at the posttranscriptional level. These molecules, which are typically 18-25 nucleotides in length, have emerged as significant players in cancer biology, including HCC. MiRNAs can function as either oncogenes or tumor suppressors, depending on the context of their expression and the cellular pathways they regulate. In recent years, circulating miRNAs have been extensively studied as potential biomarkers for the early detection, prognosis, and therapeutic targeting of HCC[50,51].

One of the most studied miRNAs in HCC is miR-122, which is highly expressed in the liver and plays a crucial role in liver homeostasis. MiR-122 is involved in regulating lipid metabolism, cholesterol biosynthesis, and the response to oxidative stress. Its downregulation has been associated with the progression of liver diseases, including HCC[52]. Studies have shown significantly lower circulating levels of miR-122 in patients with HCC than in healthy individuals, making it a promising noninvasive biomarker for this disease[53]. El-Abd et al[54] in 2015 revealed that combining miR-122 with other miRNAs, such as miR-199a and miR-16, could improve the diagnostic accuracy for HCC, especially in patients with chronic hepatitis C virus infection.

The efficacy of miRNAs as biomarkers in HCC lies in their ability to regulate oncogenic pathways. For instance, miR-122, a liver-specific miRNA, is often down regulated in HCC, impacting lipid metabolism and promoting tumor progression. Other miRNAs, such as miR-199a and miR-16, function as tumor suppressors by inhibiting cell proliferation and promoting apoptosis. These miRNAs’ stable presence in blood, combined with their cancer-specific regulatory roles, underscores their potential as non-invasive biomarkers for early HCC detection[54,55].

MiR-199a has also garnered attention because of its tumor-suppressive properties. This miRNA is typically downregulated in HCC, and its reduced expression is associated with a poor prognosis and advanced tumor stages. The downregulation of miR-199a has been linked to the upregulation of oncogenic pathways, including the activation of mammalian target of rapamycin signaling, which promotes cell proliferation and survival[55]. In a study by El-Abd et al[54] in 2015, the combination of miR-199a with miR-16 and AFP significantly increased the sensitivity and specificity of HCC detection, underscoring the potential of miRNA panels in improving diagnostic outcomes.

MiR-16 is another miRNA of interest, primarily because of its roles in cell cycle regulation and apoptosis. In HCC, miR-16 is often downregulated, leading to unchecked cell proliferation and tumor growth[56]. The diagnostic value of miR-16 was highlighted in a study where its combination with AFP improved the detection rate of HCC in hepatitis C virus-positive patients. This finding is particularly relevant for cases where AFP alone may not be sufficiently sensitive, suggesting that miR-16 could serve as a valuable adjunct in the diagnostic process[54].

In addition to these individual miRNAs, interest in developing miRNA signatures or panels that can provide a more comprehensive and accurate diagnostic tool for HCC is increasing. These panels leverage the combined expression profiles of multiple miRNAs to increase diagnostic sensitivity and specificity. For example, a panel combining miR-122, miR-192, miR-21, miR-223, miR-26a, miR-27a, and miR-801 has shown promise in distinguishing HCC from chronic liver disease, with the potential to be implemented as a noninvasive screening tool[57].

The stability of miRNAs in the circulation, along with their ability to reflect the pathological state of the liver, makes them particularly attractive biomarkers. Unlike proteins, miRNAs are remarkably stable in blood and other body fluids, withstanding conditions that typically degrade other biomolecules. This stability, combined with the specificity of miRNA expression patterns, suggests that miRNAs could play a pivotal role in the future of HCC diagnostics[58]. Emerging biomarkers that have shown potential in improving the diagnosis and monitoring of HCC, highlighting their diagnostic performance and clinical relevance, are detailed in Table 1.

Table 1 Summary of emerging biomarkers for hepatocellular carcinoma.

Marker	Assay used	Sensitivity (%)	Specificity (%)	Sample (number)	Ref.
GPC3	Immunohistochemistry	72	85	120 HCC, 90 cirrhosis	Hsu et al[32]
GP73	ELISA	69	75	150 HCC, 100 cirrhosis	Marrero et al[40]
OPN	ELISA	46	80	208 HCC, 193 chronic hepatitis	Jang et al[82]
DCP	ECLIA	77	88	180 HCC, 140 cirrhosis	Nakamura et al[22]
MiR-122	Quantitative RT-PCR	85	80	210 HCC, 160 healthy controls	El-Abd et al[54]
AFP-L3	Immunoassay	42	97	419 HCC, 417 cirrhosis	Marrero et al[83]

This table provides a comprehensive overview of several emerging biomarkers that have been identified for the diagnosis and monitoring of hepatocellular carcinoma. The biomarkers are listed alongside the assays used for their detection, including enzyme-linked immunosorbent assay, immunohistochemistry, and quantitative reverse transcription-polymerase chain reaction, among others. The sensitivity and specificity of each biomarker, representing its diagnostic performance, are provided along with the sample sizes used in the studies. These biomarkers, such as glypican-3, Golgi protein 73, and microRNA-122, offer potential for improving the early detection and prognosis of hepatocellular carcinoma. ELISA: Enzyme-linked immunosorbent assay; RT-PCR: Reverse transcription-polymerase chain reaction; GPC3: Glypican-3; GP73: Golgi protein 73; OPN: Osteopontin; DCP: Des-gamma-carboxy prothrombin; miR-122: MicroRNA-122; AFP-L3: Alpha-fetoprotein-L3; ECLIA: Electrochemiluminescence immunoassay.

PROTEOMIC AND GENOMIC APPROACHES

Proteomics

Proteomics, the large-scale study of proteins, has emerged as a pivotal tool in the search for novel biomarkers for HCC. Given the complexity of HCC, with its diverse molecular alterations and intricate interplay of oncogenic pathways, proteomics provides an unparalleled opportunity to map the protein landscape associated with this malignancy. Advances in proteomic technologies, including mass spectrometry (MS), protein microarrays, and bioinformatics, have significantly improved our ability to identify and quantify proteins that are differentially expressed in HCC, providing insights that are critical for improving diagnosis and treatment[59].

One of the significant advantages of proteomics in HCC research is its ability to identify differentially expressed proteins in tumor tissues compared with nontumor tissues. For example, Sun et al[60] utilized quantitative proteomics to analyze tissue interstitial fluid from HCC patients and identified several upregulated proteins, including S100A9, which showed potential as a serum biomarker with a sensitivity surpassing that of AFP. This study underscores the potential of tissue interstitial fluid as a rich source of candidate biomarkers that can subsequently be validated in serum[60].

Proteomic analysis has also shown promise in the early detection of HCC. Early diagnosis is crucial for improving patient outcomes, and proteomic studies have identified several serum proteins that are differentially expressed in HCC patients compared with patients with nonmalignant liver conditions[61]. Xing et al[62] in 2023 emphasized the potential of proteomic technologies to identify early-stage biomarkers that could significantly improve the early diagnosis of HCC, especially in high-risk populations, such as those with chronic hepatitis B or C. Moreover, proteomics has been instrumental in discovering novel therapeutic targets in HCC. By comparing the proteomic profiles of HCC tissues with those of nontumorous liver tissues, researchers have identified proteins involved in crucial pathways of tumor growth and metastasis[63]. For example, proteins involved in the Wnt signaling pathway have been discovered through proteomic analyses, representing new avenues for targeted therapy of HCC[63].

Another noteworthy application of proteomics in HCC research is its use in glycoproteomics, which focuses on the glycosylation patterns of proteins. Aberrant glycosylation is a hallmark of cancer, including HCC, and glycoproteomics has enabled the identification of novel glycoproteins that could serve as early diagnostic markers[64]. For example, Dickkopf-1 has been identified as a potential biomarker through glycoproteomic analysis, with studies showing that its levels are elevated in HCC patients compared with those with cirrhosis[65].

Despite these advancements, the translation of proteomic discoveries into clinical practice remains challenging. The complexity of the liver proteome, coupled with the need for high sensitivity and specificity in biomarker validation, present significant hurdles. However, ongoing improvements in proteomic technologies, such as enhanced MS techniques and advanced bioinformatics tools, are expected to overcome these challenges, confirming the role of proteomics as a crucial tool in HCC research and patient care[66,67].

Genomics

Genomics, the study of an organism’s complete set of DNA, including all of its genes, has revolutionized our understanding of HCC. Through genomic approaches, researchers have been able to elucidate the genetic alterations that drive HCC, identify potential biomarkers for early detection, and reveal new therapeutic targets[68]. The advent of next-generation sequencing and other high-throughput techniques has enabled comprehensive analyses of the HCC genome, providing insights into the complex molecular landscape of this disease[69,70].

One of the most significant findings in the genomic analyses of HCC is the identification of recurrent somatic mutations. Mutations in genes such as TP53, CTNNB1 (which encodes β-catenin), and AXIN1 have been frequently observed in HCC, suggesting their critical roles in tumorigenesis[71]. TP53 mutations, for example, are associated with a poor prognosis and resistance to therapy, highlighting their potential as prognostic biomarkers[72]. Additionally, alterations in the TERT promoter, which are common in HCC, lead to increased telomerase activity, promoting cellular immortality - a hallmark of cancer[73].

In addition to single-gene mutations, large-scale genomic studies have identified significant structural variations and chromosomal aberrations in HCC[74]. For example, whole-genome sequencing has revealed frequent amplifications in chromosome 8q and deletions in chromosome 17p, which harbor oncogenes and tumor suppressor genes, respectively. These findings underscore the genetic complexity of HCC and the need for multifaceted approaches for its diagnosis and treatment[74].

Epigenetic modifications, particularly DNA methylation, also play crucial roles in the development and progression of HCC. Hypermethylation of tumor suppressor gene promoters and global hypomethylation are common features of HCC[75]. Integrative analyses combining DNA methylation profiles with gene expression data have led to the identification of novel biomarkers that are specific to HCC. For example, a study by Cheng et al[75] identified six hypermethylated CpG sites that could serve as highly specific biomarkers for HCC, achieving a sensitivity of over 90% and a specificity of nearly 100% in distinguishing HCC from normal liver tissues.

Another promising area in HCC genomics is the study of circulating tumor DNA (ctDNA). CtDNA provides a noninvasive tool to monitor tumor dynamics, detect minimal residual disease, and identify resistance mutations[76]. Recent studies have shown that ctDNA can reflect the mutational landscape of HCC and serve as a reliable biomarker for early detection and prognosis[77]. Moreover, a ctDNA analysis can capture the heterogeneity of HCC, providing insights into clonal evolution and the emergence of drug-resistant clones[77].

Despite these advancements, challenges remain in translating genomic findings into clinical practice. The genetic heterogeneity of HCC, coupled with the influence of environmental factors such as hepatitis virus infection and alcohol consumption, complicates the development of universal biomarkers. Furthermore, the integration of genomic data with other omics technologies, such as proteomics and metabolomics, is necessary to gain a more comprehensive understanding of HCC and to develop personalized treatment strategies[78].

INTEGRATIVE APPROACHES

Integrative approaches in cancer research involve the comprehensive analysis of various “omics” data - proteomics, genomics, metabolomics, and more - to gain a holistic understanding of the disease. HCC is a complex and multifactorial cancer, and single omics studies often provide only a partial view of the molecular alterations driving the disease. By combining multiple omics datasets, researchers can identify novel biomarkers, gain insights into disease mechanisms, and identify potential therapeutic targets that might be missed when focusing on a single type of data[79,80].

Integrative approaches are becoming increasingly vital in the pursuit of precision medicine for HCC, where treatments and diagnostics are tailored to the individual patient’s molecular profile. These approaches not only improve the accuracy of biomarker discovery but also improve our understanding of the interactions between different molecular layers, such as how genetic mutations influence protein expression and how these interactions, in turn, affect metabolic pathways. This holistic perspective is crucial for developing more effective diagnostic tools and therapies[81].

Combining proteomic, genomic, and metabolomic data

The integration of proteomic, genomic, and metabolomic data in HCC research represents a powerful strategy to comprehensively understand the molecular mechanisms underpinning this disease. Each omics layer provides unique insights: Genomics reveals DNA-level alterations, proteomics highlights changes in protein expression, and metabolomics reflects downstream effects on cellular metabolism. By combining these datasets, researchers can construct a more complete picture of HCC biology[80]. One of the major benefits of this integrative approach is the ability to identify biomarkers that are more robust and specific to HCC. For example, studies have shown that while genomic alterations such as TP53 mutations are common in HCC, they do not always correlate directly with changes in protein expression or metabolism. However, when these genomic data are combined with proteomic and metabolomic data, identifying consistent patterns that are more likely to be relevant for diagnosis or treatment becomes possible[72].

Integrative analyses have also led to the discovery of novel biomarkers that would have been difficult to identify using a single omics approach. For example, the combination of DNA methylation data with proteomic profiles has revealed new epigenetic markers that are closely linked to changes in protein expression, suggesting potential new targets for HCC therapy. Similarly, the integration of metabolomic data has provided insights into how metabolic alterations in HCC are driven by specific genetic and proteomic changes, further enriching the biomarker discovery process[82].

The use of advanced computational tools and bioinformatics is essential in these integrative studies, enabling researchers to manage and analyze the vast amounts of data generated by multiomics approaches[83]. Machine learning algorithms, for example, are increasingly used to identify patterns and correlations across different omics datasets, helping to pinpoint the most promising biomarkers and therapeutic targets. This computational power is crucial for moving from data collection to actionable insights, ultimately guiding the development of personalized medicine strategies for HCC patients[81].

Multiomics approaches to enhance biomarker discovery

The integration of multiomics approaches - encompassing genomics, proteomics, metabolomics, and other omics data - has revolutionized biomarker discovery in HCC. By combining different types of molecular data, researchers can obtain a more comprehensive understanding of the complex biological processes underlying HCC and identify more robust and reliable biomarkers for early detection, prognosis, and therapy[80]. One of the key advantages of multiomics approaches is the ability to capture the interactions and dependencies between different molecular layers. For example, while genomic studies can identify mutations and other DNA-level alterations, these changes do not always translate directly into altered protein expression or metabolic activity[84]. By integrating proteomic and metabolomic data with genomic data, researchers can better understand how genetic alterations influence protein function and metabolic pathways, leading to more accurate and clinically relevant biomarkers[85].

In HCC, multiomics approaches have led to the identification of several promising biomarkers that are not only specific to the disease but also indicative of its stage and progression. For example, integrative studies combining DNA methylation profiles with proteomic data have revealed epigenetic changes that correlate with specific protein expression patterns, providing new insights into the molecular mechanisms driving HCC and identifying potential therapeutic targets[86]. Moreover, multiomics approaches have been instrumental in identifying biomarkers that are more effective at distinguishing HCC from other liver diseases, such as cirrhosis. For example, a study combining metabolomic and proteomic data identified a panel of biomarkers that could differentiate HCC patients from those with cirrhosis with greater accuracy than traditional markers such as AFP. This ability to distinguish HCC from other liver conditions is critical for improving early diagnosis and reducing false-positive rates[87].

The application of advanced bioinformatics tools and machine learning algorithms has further increased the power of multiomics approaches. These computational techniques allow researchers to integrate and analyze large datasets, elucidating complex patterns and interactions that would be difficult to detect using traditional methods. For example, machine learning models trained on multiomics data outperform single omics approaches in predicting HCC outcomes and identifying high-risk patients[88]. Despite these advancements, challenges remain in the implementation of multiomics approaches in clinical practice. The high cost and complexity of multiomics analyses, along with the need for standardized protocols and robust validation studies, are significant barriers. However, ongoing research and technological improvements are likely to overcome these obstacles, paving the way for the routine use of multiomics approaches in HCC diagnosis and treatment.

Expanding the potential of multiomics for enhanced HCC diagnosis and clinical application

Integrating multiomics approaches for improved HCC diagnostics: The integration of multiomics data - genomic, proteomic, and metabolomic - has shown promising potential in increasing the diagnostic sensitivity and specificity of HCC. Multiomics allows for a comprehensive view of the tumor’s molecular landscape by combining information at different biological levels. For instance, genomic data can reveal mutations or alterations associated with HCC, proteomics can provide insights into protein expression changes, and metabolomics can capture downstream metabolic alterations that signal disease progression[79].

Examples of multiomics in enhancing HCC diagnosis: Combining these omics layers can improve early detection by identifying robust biomarker panels that single-omics approaches might miss. For example, integrating genomic mutations in TERT or TP53 with proteomic markers like GP73 or AFP-L3 has demonstrated a marked improvement in differentiating HCC from benign liver diseases[85,86]. Additionally, metabolomic markers that indicate altered lipid metabolism have been effective in distinguishing HCC from cirrhosis and other chronic liver conditions when combined with proteomic and genomic data[88].

Challenges in clinical implementation: Despite its advantages, multiomics integration poses several challenges for clinical application. Technologically, multiomics data require advanced analytical platforms and computational infrastructure, which can be costly and complex to manage. Standardizing data processing and ensuring reproducibility across laboratories are significant hurdles, especially given the variability introduced by different sample types and collection methods. Practical challenges also exist, such as the need for interdisciplinary expertise in bioinformatics and the development of user-friendly interfaces for clinicians to interpret multiomics results in a clinical setting[89,90]. Addressing these challenges will be essential for making multiomics a viable approach in routine HCC diagnostics.

Examples of successful integrative studies of HCC

Integrative multiomics approaches have significantly advanced our understanding of HCC by enabling comprehensive analyses of the complex molecular alterations associated with this disease. Several studies have successfully combined genomic, proteomic, and metabolomic data to identify novel biomarkers, elucidate disease mechanisms, and identify potential therapeutic targets, thereby offering promising avenues for improving HCC diagnosis and treatment[80].

One notable example is the study by Song et al[91] in 2023, which integrated genomic, transcriptomic, and epigenomic data to identify key molecular subtypes of HCC. By analyzing large-scale datasets from The Cancer Genome Atlas and other cohorts, the researchers were able to classify HCC into distinct subtypes based on specific genetic mutations, DNA methylation patterns, and gene expression profiles[91]. This integrative approach not only provided a more detailed understanding of the heterogeneity of HCC but also identified subtype-specific biomarkers that could be used for targeted therapy and personalized treatment[91].

In another successful study, Hoshida et al[89] in 2008 utilized an integrative approach combining transcriptomic and proteomic data to identify a novel molecular signature associated with HCC metastasis. By comparing primary tumors with metastatic lesions, a set of genes and proteins that were consistently upregulated in metastatic HCC was identified. This signature was subsequently validated in multiple independent cohorts, demonstrating its potential as a prognostic biomarker for identifying patients at high risk of metastasis[89].

A study by Tan et al[90] further highlighted the power of multiomics integration in identifying therapeutic targets. The researchers integrated genomic, epigenomic, and proteomic data to investigate the role of the Wnt/β-catenin signaling pathway in HCC. These findings revealed that specific mutations in the CTNNB1 gene, which encodes β-catenin, were associated with distinct epigenetic and proteomic profiles. This integrative analysis not only confirmed the critical role of Wnt/β-catenin signaling in HCC but also identified novel targets within the pathway that could be exploited for therapy[90].

Moreover, a study by Zhang et al[92] in 2021 documented the utility of integrating metabolomics with other omics data to understand metabolic reprogramming in HCC. By combining metabolomic profiles with genomic and transcriptomic data, the researchers identified a set of metabolic pathways that were consistently altered in HCC[92]. These pathways, including those involved in lipid metabolism and amino acid biosynthesis, were regulated by specific oncogenes and tumor suppressor genes, providing insights into the metabolic vulnerabilities of HCC that could be targeted for therapy[92].

Finally, the work of Liu et al[93] in 2022 illustrated the application of multiomics integration in identifying diagnostic biomarkers for early-stage HCC. This study combined proteomic, metabolomic, and genomic data from patients with early-stage HCC and those with cirrhosis, leading to the discovery of a panel of biomarkers that could distinguish HCC from cirrhosis with high accuracy. This integrative approach has the potential to improve early detection and reduce the false-positive rates associated with traditional biomarkers such as AFP[93].

CHALLENGES IN BIOMARKER DISCOVERY

The discovery and validation of reliable biomarkers for HCC are fraught with numerous challenges that span the entire research process, from sample collection to data interpretation. These challenges can be broadly categorized into preanalytical, analytical, and postanalytical factors. Each of these stages introduces potential sources of variability and error that can compromise the accuracy and reproducibility of biomarker findings. Understanding and mitigating these challenges is crucial for advancing biomarker research and translating discoveries into clinical practice. In the following sections, we explore these factors in detail, beginning with the preanalytical challenges associated with sample collection, processing, and storage.

Preanalytical factors

Preanalytical factors are critical to the success of biomarker discovery, as they directly influence the quality and integrity of biological samples. Sample collection, processing, and storage are all steps that can introduce variability and affect the reliability of biomarker measurements. For example, improper handling during sample collection, such as delayed processing or exposure to nonideal temperatures, can lead to protein degradation, DNA fragmentation, or alterations in metabolite levels[94]. In particular, the choice of sample preservation methods, such as the use of formalin-fixed paraffin-embedded (FFPE) tissues, poses challenges due to the crosslinking and fragmentation of nucleic acids and proteins. FFPE is a common method for preserving tissue samples, but it can introduce artifacts that complicate downstream analyses, especially in next-generation sequencing applications. Studies have shown that the yield and quality of DNA extracted from FFPE samples are often lower than those extracted from fresh or frozen samples, which can impact the accuracy of genomic assays[95].

Another critical aspect of preanalytical variability is the storage conditions of biological samples. The stability of biomarkers can be compromised by factors such as temperature fluctuations, freeze-thaw cycles, and prolonged storage times. For example, repeated freeze-thaw cycles can lead to protein denaturation and a loss of enzymatic activity, which in turn affects the reproducibility of proteomic analyses. Standardizing sample storage protocols and using appropriate preservation techniques are essential for minimizing these sources of variability[96].

Analytical factors

Analytical factors refer to the characteristics of the assays used to measure biomarkers, including their sensitivity, specificity, and reproducibility. These factors are crucial in determining the reliability of the discovered biomarkers and for subsequent validation. One of the main challenges in biomarker research is achieving high assay sensitivity while maintaining specificity, as false-positives can lead to the identification of nonreproducible biomarkers[97]. MS is a powerful analytical technique commonly used in proteomics for biomarker discovery. However, the sensitivity of MS-based assays can be influenced by the complexity of the biological matrix, sample preparation methods, and instrument settings. Optimizing these parameters is essential for detecting low-abundance proteins and achieving reproducible results. For example, multiplexing techniques such as isobaric tags for relative and absolute quantitation have been developed to increase the quantitative accuracy of MS, but they also introduce potential sources of variability that need to be carefully controlled[98].

Reproducibility is another key concern in analytical methodologies. Variations in assay performance can arise from differences in sample preparation, instrument calibration, or data analysis pipelines. Standardized protocols and rigorous quality control measures are necessary to address these issues and ensure that biomarker discoveries are consistent across different laboratories and studies. Moreover, the use of statistical approaches to account for technical and biological variability is critical for validating the robustness of identified biomarkers[99].

In biomarker research, establishing clinical validity requires studies with adequate sample sizes to ensure statistical power. Despite promising findings in small-scale studies, biomarkers must undergo validation in large, multicenter cohorts to confirm their diagnostic or prognostic utility. Large-scale studies, such as those conducted by Marrero et al[40] and Shen et al[65], involving hundreds of patients, have highlighted the diagnostic accuracy of biomarkers like GP73 and Dickkopf-1 for HCC. Such multicenter studies improve the generalizability of findings across diverse patient populations and clinical settings[40,65]. However, many potential HCC biomarkers still lack this level of validation, leading to variability in reported efficacy. For future research, conducting power analyses before study design and collaborating on multicenter studies can help address these limitations. Emphasizing large sample sizes and standardized protocols, as seen in studies involving datasets like The Cancer Genome Atlas, will be essential for integrating biomarkers into clinical practice confidently.

Postanalytical factors

Postanalytical factors involve the processes of data interpretation, statistical analysis, and validation of biomarker candidates. These steps are crucial for translating raw data into meaningful clinical insights. However, the interpretation of complex omics data poses significant challenges because of the high dimensionality and potential for false discoveries. Robust statistical methods and bioinformatics tools are needed to filter out noise and identify truly significant biomarkers[100]. One of the main issues in postanalytical processing is the validation of biomarkers in independent cohorts. Biomarkers discovered in initial studies often fail to be replicated in subsequent studies due to overfitting, batch effects, or differences in study populations. Therefore, cross-validation using independent datasets is essential to confirm the clinical utility of biomarkers[101]. Furthermore, integrating multiomics data can help to corroborate findings across different biological levels, increasing the reliability of candidate biomarkers[78]. Data interpretation also requires careful consideration of the biological context. Biomarkers should not only be statistically significant but also biologically relevant to the disease process. This process requires close collaboration between bioinformaticians, statisticians, and clinicians to ensure that the biomarkers identified are not only technically valid but also clinically meaningful[102].

FUTURE DIRECTIONS

As the field of biomarker discovery continues to evolve, addressing the challenges and exploring the opportunities presented by emerging technologies, large-scale validation studies, collaborations between research institutions and clinical settings, and regulatory considerations for clinical implementation are growing needs. These directions will shape the future of biomarker research, particularly in the context of HCC, where early detection and personalized treatment strategies are crucial.

Emerging technologies and their potential impacts

Emerging technologies such as single-cell sequencing, advanced imaging techniques, and artificial intelligence (AI) are poised to revolutionize biomarker discovery in liver cancer[103]. Single-cell sequencing, for example, enables the analysis of genetic and epigenetic heterogeneity within tumors at an unprecedented resolution, providing insights into clonal evolution and identifying rare cell populations that may drive cancer progression. This technology could lead to the identification of novel biomarkers that are specific to these subpopulations, providing more targeted therapeutic options[104].

Advanced imaging technologies, such as mass cytometry and multiplexed immunohistochemistry, enable the simultaneous detection of multiple biomarkers at the tissue level, providing a comprehensive view of the tumor microenvironment. These technologies are particularly valuable for understanding the spatial distribution of biomarkers and their interactions with immune cells, which is critical for the development of immunotherapies. The integration of imaging data with genomic and proteomic information could further increase the accuracy of biomarker-based diagnostics[105,106].

AI and machine learning algorithms are increasingly being used to analyze large-scale omics data, uncovering patterns that may not be evident using traditional statistical methods. These technologies can identify complex biomarker signatures that correlate with patient outcomes, paving the way for more personalized treatment strategies. AI-driven approaches also have the potential to accelerate the discovery of biomarkers by automating data analysis and hypothesis generation, making the research process more efficient[107,108].

Need for large-scale validation studies

While emerging technologies hold great promise, their clinical utility depends on rigorous validation in large-scale studies. The reproducibility of biomarker findings across different populations and clinical settings is a significant challenge that must be addressed through multicenter collaborations and standardized protocols. Large-scale validation studies are essential for confirming the diagnostic and prognostic value of biomarkers identified through high-throughput technologies[99]. These studies should also consider the heterogeneity of HCC, which can vary widely based on factors such as underlying liver disease, geographic region, and patient demographics. By including diverse patient populations in validation studies, researchers can ensure that biomarkers are broadly applicable and can be used to guide clinical decision-making in different settings[109,110]. Moreover, large-scale validation studies provide an opportunity to refine biomarker panels by integrating multiple types of omics data, such as genomic, proteomic, and metabolomic data. This integrative approach can increase the sensitivity and specificity of biomarker-based tests, reducing the likelihood of false-positives and negatives[111].

Collaboration between research institutions and clinical settings

Effective collaboration between research institutions and clinical settings is crucial for translating biomarker discoveries into clinical practice. Such collaborations can facilitate the collection of high-quality clinical samples, the implementation of standardized protocols, and the sharing of data and resources[99]. Additionally, involving clinicians in the research process ensures that the identified biomarkers address clinically relevant questions and can be integrated into existing diagnostic and treatment workflows. One of the key benefits of collaboration is the ability to conduct prospective studies that track patients over time, providing longitudinal data that can be used to validate biomarkers and assess their predictive value. For example, multicenter trials involving both academic research centers and hospitals can accelerate the evaluation of biomarker panels in real-world settings, ultimately leading to their adoption in routine clinical care. Collaborations also offer opportunities for training and capacity building, enabling researchers and clinicians to stay abreast of the latest technological advancements and methodologies in biomarker discovery. This interdisciplinary approach fosters innovation and helps bridge the gap between basic research and clinical application.

Regulatory considerations for the clinical implementation of new biomarkers

The clinical implementation of new biomarkers requires a careful consideration of regulatory guidelines to ensure their safety, efficacy, and reliability. Regulatory agencies, such as the United States. Food and Drug Administration and the European Medicines Agency, have established frameworks for the validation and approval of diagnostic tests, including biomarkers. Compliance with these guidelines is essential for bringing new biomarker-based tests to market[112].

One of the key challenges in regulatory approval is documenting the clinical utility of biomarkers, which involves showing that their use improves patient outcomes compared with standard care. This process requires robust clinical evidence from well-designed trials that not only validates the accuracy of biomarkers but also assesses their impacts on treatment decisions and patient survival. As part of the regulatory process, researchers who have identified biomarkers must also address issues related to assay standardization, reproducibility, and quality control[112].

In addition to regulatory approval, the successful implementation of new biomarkers in clinical practice depends on their acceptance by health care providers and patients. This step requires clear communication of the biomarkers’ benefits and limitations, as well as education and training for clinicians on how to interpret and use the test results. Ensuring that new biomarkers are cost-effective and accessible is also critical for their widespread adoption.

Integrating biomarkers into clinical practice: Utility and cost-effectiveness considerations

The integration of novel biomarkers into current clinical workflows for HCC holds promise for improving early detection, personalizing patient management, and potentially increasing survival rates. However, to ensure successful implementation, the clinical utility and cost-effectiveness of these biomarkers must be carefully considered. Current screening protocols often rely on established biomarkers, such as AFP, which have limitations in sensitivity and specificity. Introducing novel biomarkers, particularly those identified through multiomics approaches, could substantially improve diagnostic accuracy, especially in early-stage HCC detection where intervention is most effective[18,29].

Cost-effectiveness analysis is essential, particularly in resource-limited settings where healthcare infrastructure may not support the high costs of some advanced biomarker assays. Studies, such as those by Fares et al[29] and Luo et al[18], demonstrate that early HCC detection using a combination of biomarkers could be cost-effective by reducing downstream treatment costs associated with advanced-stage disease management. In high-incidence regions, implementing these biomarkers in community and primary care settings could provide significant public health benefits.

Practical considerations for integration also include the need for affordable, scalable assays that can be easily adopted in routine screenings. Rapid advancements in diagnostic technologies, like multiplexed assays and machine learning algorithms for biomarker analysis, offer solutions to optimize cost-efficiency and accessibility in diverse healthcare settings. As these biomarkers undergo further validation, demonstrating their impact on patient outcomes and cost savings will be critical to justify their inclusion in national and global screening protocols for liver cancer.

CONCLUSION

The discovery of reliable biomarkers for HCC has made significant strides, yet many challenges remain. The current landscape is marked by the identification of several promising biomarkers, including AFP, DCP, and various emerging candidates, such as GPC3 and miRNAs, which hold potential for early diagnosis and improved patient stratification. Advances in technologies such as single-cell sequencing, proteomics, and multiomic approaches have provided deeper insights into the molecular mechanisms underpinning HCC, paving the way for more precise and personalized treatment strategies. However, the translation of these discoveries into clinical practice requires rigorous validation through large-scale studies and cross-disciplinary collaboration.

Continued research and innovation are essential to overcome the challenges of biomarker discovery, including the need for standardization of assay protocols, better integration of multiomics data, and more comprehensive validation studies across diverse populations. The collaboration among research institutions, clinical settings, and regulatory agencies will be pivotal in addressing these challenges and ensuring that new biomarkers can be effectively implemented in clinical practice. The ongoing development and refinement of biomarker panels will not only increase diagnostic accuracy but also lead to more tailored therapeutic approaches, ultimately improving patient outcomes in HCC management. However, despite these promising advances, there are significant hurdles to translating these biomarkers into routine clinical practice. Large-scale, multicenter studies are urgently needed to validate the diagnostic and prognostic accuracy of biomarkers like DCP, AFP-L3, and miRNAs across diverse patient populations, as existing studies are often limited by smaller sample sizes or single-center designs. Securing regulatory approval will require demonstrating clinical utility, reproducibility, and improved patient outcomes over current standards. Additionally, incorporating these biomarkers into clinical workflows demands standardized, cost-effective assays and practical infrastructure, particularly in resource-limited settings. Clinician education on interpreting and utilizing biomarker data is essential to effective personalized HCC management. These steps underscore the complexities of moving from bench to bedside, highlighting the need for continued research, collaboration, and strategic investments in biomarker development. By addressing these challenges directly, we can work toward a future where biomarker-driven diagnostics and personalized treatment strategies improve precision, accessibility, and patient outcomes in HCC management.

The impacts of these advancements on patient care are profound. Early detection through reliable biomarkers can significantly improve survival rates by enabling timely intervention, while personalized treatment strategies based on biomarker profiles can reduce the risk of recurrence and improve the overall quality of life of liver cancer patients. As research continues to push the boundaries of what is possible, the future of liver cancer management looks increasingly promising, with the potential to transform patient outcomes through more precise, effective, and personalized care.

ACKNOWLEDGEMENTS

The authors wish to express their gratitude to all individuals and institutions that contributed valuable insights and assistance to this work. We acknowledge the support of our colleagues and the constructive feedback from the reviewers, which greatly improved the quality of this manuscript.