肝纤维化无创血清标志物的研究现状 与未来展望
Current Research Status and Future Outlook of Non-Invasive Serum Biomarkers for Liver Fibrosis
DOI: 10.12677/acm.2026.162536, PDF, HTML, XML,   
作者: 郑瑞燥*, 俞慧宏#:重庆医科大学附属第二医院消化内科,重庆
关键词: 肝纤维化血清标志物无创诊断Liver Fibrosis Serum Markers Noninvasive Diagnosis
摘要: 肝纤维化是慢性肝病进展中的一种可逆性病理过程,其早期准确评估对于阻止肝硬化、肝衰竭及肝癌的发生至关重要。传统的肝活检具有创伤性,而血清标志物作为无创诊断工具,近年来受到广泛关注。本文首先梳理了血清标志物的分类及发展,探讨了整合模型与人工智能在提升诊断效能中的作用。随后,分析了不同病因导致的肝纤维化中血清标志物的差异与挑战。最后,展望未来研究方向,强调血清标志物应从诊断分期向预后预测及临床转化拓展,以推动肝纤维化的精准医疗进程。
Abstract: Liver fibrosis represents a reversible pathological process in the progression of chronic liver disease. Accurate early assessment is crucial for preventing the development of cirrhosis, liver failure, and hepatocellular carcinoma. While traditional liver biopsy is invasive, serum biomarkers have garnered significant attention in recent years as non-invasive diagnostic tools. This paper first reviews the classification and development of serum biomarkers, exploring the role of integrated models and artificial intelligence in enhancing diagnostic efficacy. Subsequently, it analyzes the differences and challenges in serum biomarkers across liver fibrosis caused by various etiologies. Finally, it outlines future research directions, emphasizing that serum biomarkers should expand from diagnostic staging to prognostic prediction and clinical translation to advance precision medicine for liver fibrosis.
文章引用:郑瑞燥, 俞慧宏. 肝纤维化无创血清标志物的研究现状 与未来展望[J]. 临床医学进展, 2026, 16(2): 1485-1494. https://doi.org/10.12677/acm.2026.162536

1. 引言

肝纤维化是多种慢性肝病向肝硬化、肝衰竭及肝细胞癌进展的关键病理阶段,且在一定条件下具有可逆性。荟萃分析显示普通人群中晚期肝纤维化与肝硬化的全球患病率分别约为3.3%与1.3% [1]。其核心特征为肝脏细胞外基质(ECM)的过度沉积,导致肝脏结构和功能异常[2]。传统的肝活检虽被视为评估肝纤维化程度的“金标准”,但其侵入性、潜在并发症(如出血、疼痛)、抽样误差及观察者差异等局限性,限制了其在临床中的广泛应用[3]。在这一背景下,血清标志物凭借其易于获取、创伤小、成本较低且可重复性高,已成为评估肝纤维化的重要工具[4]-[6]

不同病因(HBV/HCV、MASLD、ALD、AIH等)在炎症活动度、代谢背景与纤维化动态上差异显著,同一血清指标在不同人群中的阈值与诊断效能并不一致。本文在简要概述血清标志物的临床定位后,重点围绕“不同病因肝纤维化血清标志物的应用与挑战”进行综述,并讨论未来研究方向。

2. 肝纤维化血清标志物的分类与研究进展

血清标志物用于在血液中捕捉与纤维化发生发展相关的生物信号,现有研究通常按“间接指标与综合评分–直接分子标志物–整合模型”进行描述。为避免将已进入临床路径的检测与研究性候选分子混为一谈,本文同时按证据成熟度与可及性将其划分为Standard of Care (临床常用的无创检测与评分)与Investigational (研究性)两类。

2.1. 传统间接血清标志物与综合评分模型

传统间接血清标志物主要基于常规肝功能、血常规等临床生化指标,通过反映肝细胞损伤、炎症水平、合成功能下降或门脉高压相关表现,间接推测肝纤维化程度。常用指标包括丙氨酸氨基转移酶(ALT)、天冬氨酸氨基转移酶(AST)、血小板计数(PLT)、白蛋白(ALB)、胆红素(TBIL)等。其中,ALT和AST是最常用的肝损伤指标,其升高通常提示活跃性炎症;而血小板减少与白蛋白降低则可能反映肝功能减退和门静脉高压,常见于晚期肝纤维化或肝硬化[7] [8]。然而,这些单一指标的特异性有限,易受炎症、营养状态、感染或合并疾病等多种因素干扰,限制了其在肝纤维化精准分期中的应用价值。

为提高诊断准确性,研究者将多个传统间接血清标志物进行组合,建立了多种综合评分模型。其中,AST与血小板比率指数(APRI)和纤维化-4指数(FIB-4)是目前临床中应用最广泛、验证最充分的无创模型,具有计算简便、数据易得、适用于基层初筛等临床优势[9]-[11]。因其基于常规检验、可重复且成本低,多用于临床分层管理与初筛路径,属于当前无创评估的Standard of Care组成部分。Bera等人(2024)指出,APRI与FIB-4常作为慢性乙型肝炎(CHB)患者肝纤维化初筛的重要血清学工具[9]。Castellana等人(2021)对代谢功能障碍相关脂肪性肝病(MASLD)患者的荟萃分析显示,FIB-4在排除晚期纤维化方面具有较好表现,但对中间阶段的识别能力有限[10]。Allam等人(2023)研究发现,在慢性丙型肝炎(CHC)患者接受直接抗病毒药物(DAAs)治疗后,FIB-4与APRI评分显著下降,提示其可用于疗效监测及动态评估纤维化的改善[12]

除APRI与FIB-4外,还有其他多种基于间接指标的评分系统,如MASLD纤维化评分(NFS)、增强型肝纤维化评分(ELF)、Fibrotest (FT)等在不同肝病中的引用(表1)。这类组合评分多被用于二线确认或特定场景的风险分层,其临床使用场景应与研究性候选分子标志物明确区分。这些模型虽提升了肝纤维化初筛的便捷性,但普遍存在特异性不足、易受非肝纤维化因素干扰及对中度纤维化识别能力欠佳等问题。此外,它们在不同病因所致的肝纤维化中表现不一,进一步限制了其在精准分期、疗效评估及长期随访中的广泛应用,也提示亟需更敏感、特异的无创指标加以补充。

Table 1. Comparison of noninvasive serum biomarkers for liver fibrosis

1. 肝纤维化血液生物标志物的比较

评分模型

组成成分

疾病

AUC

截断值

敏感性

特异性

参考文献

APRI

AST, PLT

CHB

0.63

0.56

0.56

0.73

Wang et al. [13]

CHC

0.77

0.70

0.77

0.72

Lin et al. [14]

MASLD

0.85

1.0

0.83

0.81

Kouvari et al. [15]

FIB-4

AST, ALT, PLT, age

CHB

0.78

2.8~3.5

0.31

0.95

Li et al. [16]

Liguori et al. [17]

MASLD

0.82

2.67

0.32

0.96

Park et al. [18]

Castera et al. [19]

NFS

Age, BMI, AST, ALT,

IFG/diabetes, PLT, ALB

MASLD

0.67

0.675

0.29

0.88

Kouvari et al. [15]

ELF

HA, PIIINP, TIMP‑1

ALD

0.923

10.5

0.83

0.73

Connoley et al. [20]

Moreno et al. [21]

MASLD

0.83

9.8

0.65

0.86

Kang et al. [22]

Castera et al. [19]

FT

Age, sex, bilirubin, GGT, α2M, haptoglobin, apo-A1

CHB

0.90

0.32

0.79

0.93

Kim et al. [23]

CHC

0.83

0.55

0.70

0.79

Huang et al. [24]

注:AST,天冬氨酸氨基转移酶;ALT,丙氨酸氨基转移酶;PLT,血小板计数,BMI,身体质量指数;IFG,空腹血糖受损;diabetes,糖尿病;ALB,白蛋白;HA,透明质酸;PIIINP,前Ⅲ型胶原氨基端肽;TIMP-1,金属蛋白酶组织抑制因子1;bilirubin,胆红素;GGT,谷氨酰转移酶;α2M,α2-巨球蛋白;haptoglobin,触珠蛋白;apo-A1,脂蛋白A1。

表中APRI、FIB-4、NFS等基于常规检验的评分属于Standard of Care常用无创初筛工具;ELF、FT为商业化组合检测/评分,临床可及性因地区与机构而异;研究性候选分子标志物(Investigational)未纳入本表,详见正文相关部分。

2.2. 新型直接血清标志物

肝纤维化的核心病理过程是HSC的活化与ECM的过度沉积。α-平滑肌肌动蛋白(α-SMA)作为HSC活化的经典标志物,其血清水平与纤维化严重程度显著相关,提示其作为早期检测的潜力[25]。围绕ECM代谢的直接指标是当前研究重点,其中III型前胶原肽(PRO C3)可反映III型胶原合成并与纤维化活动度密切相关;研究表明PRO-C3等标志物与晚期慢性肝病患者疾病严重程度及预后密切相关,在预测首次失代偿和肝脏相关事件方面具有潜在价值[26]-[28]。透明质酸(HA)作为ECM重要成分,其血清水平与纤维化程度呈正相关,可用于无创评估[29]。IV型胶原(CIV)与层粘连蛋白(LN)等基底膜相关指标亦与纤维化 程度及失代偿风险相关[30]。上述标志物主要反映了ECM的“沉积状况”,而目前认为肝纤维化的进程取决于ECM“合成”与“降解”之间的动态平衡。组织蛋白酶S (Cathepsin S)作为一种在纤维化肝脏中高表达的蛋白酶,已被证实通过高效降解多种ECM成分(如弹性蛋白、层粘连蛋白和胶原)在肝脏ECM重塑中发挥关键作用[31],为新型标志物的发现提供了新方向。

慢性炎症是驱动纤维化持续进展的关键因素。壳多糖酶3样蛋白1 (CHI3L1)参与炎症与组织重塑,在MASLD等疾病中已被证实是显著纤维化的独立风险因素[32] [33]。高尔基体蛋白73 (GP73)在肝细胞损伤和肝纤维化中表达上调,在HBV及自身免疫性肝炎(AIH)中的重度纤维化展现出良好的诊断价值[34] [35]。半乳糖凝集素3结合蛋白(LGALS3BP)则被证实可通过调节TGF-β1信号通路在纤维化中发挥关键作用[36]。此外,基于N-聚糖谱图构建的评分模型,在评估显著纤维化方面显示出优于传统模型的潜力[37] [38]。其他新型标志物如生长分化因子-15 (GDF-15)、IV型胶原α1链(COL4alpha1)、YKL-40、α-谷胱甘肽-S-转移酶(alphaGST)和人附睾蛋白4 (HE4)等,在不同病因的肝纤维化中也展现出良好的诊断潜力[39]-[41]

总体而言,新型直接血清标志物有望更贴近纤维化分子机制,在早期识别、动态监测与预后评估方面补足传统间接指标的不足;但其临床转化仍面临检测标准化、阈值界定与多中心验证不足等挑战。鉴于不同病因(HBV/HCV、MASLD、ALD、AIH)下标志物的可用性、阈值与增益存在差异,具体推荐策略与未满足需求将在第2节分病因讨论。

2.3. 整合模型与人工智能辅助诊断

为提高肝纤维化无创评估的准确性与可靠性,近年来逐步形成将血清标志物、影像学 与人工智能(AI)整合的多模态评估思路。血清标志物与影像学整合是较成熟的策略之一,核心在于实现分子层面的病理信息与宏观结构改变的互补。肝脏弹性成像(如VCTE、SWE)可反映肝脏硬度,但在炎症、胆汁淤积等情况下特异性可能下降[9] [42]。与特异性血清指标联合可进一步提升诊断效能,例如FibroTouch联合HA、LN、C-IV、PC-III等在CHB肝纤维化评估中显示出更优表[43]。此外,部分磁共振相关技术可减少脂肪变性等混杂因素对评估的干扰,为纤维化分层提供补充信息[44]

AI (机器学习/深度学习)为多模态数据整合提供了有效方法,能够从高维数据中挖掘非线性关系并构建更高精度的预测模型。已有研究显示,基于少量常规临床指标即可建立较高性能的CHB纤维化分期模型[45]。融合生物力学相关信息的机器学习框架亦可提升诊断准确性[46]。深度学习在超声图像特征提取方面具有优势,能够改善复杂人群(如CHB合并MASLD)的纤维化评估[47]

总体而言,整合模型与AI辅助诊断推动肝纤维化评估向智能化、精准化发展,并可能在早期识别与分层管理中发挥更大作用。但其临床转化仍受限于多中心数据标准化与共享、 模型泛化能力与可解释性、以及成本效益与临床工作流整合等问题,未来需通过跨学科合作与前瞻性验证推进落地。

3. 不同病因肝纤维化血清标志物的应用与挑战

尽管近年来血清标志物在肝纤维化无创评估中的研究不断深入,并已形成较为完善的分类体系和模型组合,但其诊断效能的普适性仍面临挑战。不同慢性肝病(如病毒性肝炎〔HBV/HCV〕、代谢相关脂肪性肝病〔MASLD〕、酒精性肝病〔ALD〕、自身免疫性肝炎〔AIH〕)在发病机制、炎症反应和纤维化进展路径上存在显著差异,导致相同血清标志物在不同病因背景下的敏感性、特异性及临床适用性各不相同。因此,有必要深入探讨上述血清标志物在几种主要慢性肝病病因背景下的诊断性能、适用条件与核心挑战,从而为实现肝纤维化的精准、个体化无创评估提供更具针对性的临床见解。

3.1. CHB-CHC (慢性乙肝/丙肝相关肝纤维化)

在慢性乙型肝炎(CHB)相关肝纤维化中,肝纤维化四项(HA、LN、C-IV、PC-III)以及ALT、AST水平随纤维化分期和肝组织炎症程度加重而升高,其中HA (透明质酸)和C-IV (IV型胶原)在反映肝组织损伤方面较为敏感。既往研究证明Fibrotouch联合HA、LN、C-IV、PC-III血清检测可显著提升CHB肝纤维化的诊断性能[9] [43]。相比之下,在慢性丙型肝炎(CHC)相关肝纤维化中,血清III型胶原(C-III)与透明质酸(HA)为有效的无创评估工具[29],且FIB-4、APRI与GUCI评分在纤维化分期中表现较好[11]

尽管上述无创测试在分期评估中具有可及性与一定准确性,但在抗病毒治疗相关的“动态变化监测”方面仍存在不足:非侵入性测试对抗病毒治疗引起的纤维化变化监测表现不佳;同时,HBV-DNA载量与血清标志物之间关系复杂,且HBeAg状态可能影响标志物诊断效能,均构成CHB场景下的重要应用挑战[9] [48]。在CHC场景中,DAAs治疗可显著降低肝纤维化程度,新型纤维化指数可用于评估DAAs治疗效果[12],但如何在治疗后阶段实现更精准、可重复的无创随访与风险再分层,仍是临床关注重点。

3.2. MASLD (代谢相关脂肪性肝病相关肝纤维化)

对于MASLD相关肝纤维化,由于人群基数庞大且肝活检风险高,临床实践通常首先依赖基于常规检验的间接评分进行风险分层(Standard of Care),如FIB-4和NFS等(计算方法见1.1)。其中FIB-4在排除晚期纤维化方面表现较好,但对中间阶段纤维化诊断准确性有限[10] [49] [50]

现有间接模型对“中间阶段纤维化”及“炎症活跃导致的假阳性、假阴性”仍存在局限,且MASLD患者群体高度异质;同时肝脏脂肪变性可能干扰弹性成像等无创评估方法的准确性,成为当前无创诊断的关键挑战[44] [51]。因此,临床更需要能够反映纤维化“活动性/动态重塑”的补充手段,以支持更精细的分层与随访。

在此基础上,PRO-C3、PRO-C4等ECM动态重塑相关指标,以及alphaGST等分子(Investigational)主要用于科研或特定队列研究,现有证据提示其可用于风险分层/疗效反应预测,但尚需统一检测方法、阈值界定与多中心前瞻性验证后方可进入常规临床决策[27] [40]。未来需重点回答其在真实世界队列中相对FIB-4和NFS的“增益大小”、阈值可迁移性及对疗效监测的临床价值,以明确其进入常规临床路径的可行性。

3.3. ALD (酒精性肝病相关肝纤维化)

酒精性肝病(ALD)是导致严重肝病的重要病因之一,在部分地区(如欧美)负担尤为突出。对于其血清标志物,AST与ALT比值(AAR)大于1.5被认为是ALD的经典诊断标志物,然而单凭增高的转氨酶不能区分肝纤维化的严重程度。增强型肝纤维化评分(ELF)及FibroTest (FT)在诊断ALD晚期纤维化方面相较于FIB-4、APRI具有更优的性能[20] [21]

作为研究性标志物(Investigational),蛋白质组学研究表明IGFBP3 (类胰岛素生长因子结合蛋白3)、IGFALS (类胰岛素生长因子酸不稳定亚基)和B2M (β2-微球蛋白)三种蛋白含量在重度ALD患者和健康人群中具有显著差异,且作为诊断ALD纤维化的血清标志物,其诊断性能优于APRI、FIB-4及AAR [52]。而血清芳香烃受体(AhR)活性在ALD患者中显著高于对照组,并且这种活性的增加与较高的死亡率相关,这一定程度上反映血清AhR活性有可能作为ALD预后标志物[53]。未来需要围绕检测方法学标准化、阈值建立与多中心前瞻性验证,评估其对ELF/FT等临床工具的增益,并明确其在结局预测与治疗监测中的具体应用场景。

3.4. AIH (自身免疫性肝炎相关肝纤维化)

在AIH相关肝纤维化的临床管理中,纤维化程度评估通常仍需结合临床表现、常规实验室检查及必要时的组织学、影像学证据综合判断;相较于病毒性肝炎或MASLD,AIH缺乏被广泛确立的“特异性血清纤维化检测”进入常规路径的共识,提示其无创评估仍存在明显空白。

现阶段更多证据集中于研究性标志物(Investigational):血清GP73 (高尔基体蛋白73)水平随肝纤维化和炎症分期升高,对重度纤维化和中重度坏死性炎症具有较高诊断价值[35]。此外,血清HE4 (人附睾蛋白4)水平在AIH相关肝硬化患者中显著升高,与纤维化严重程度强相关[41]。但目前证据多来自回顾性或单中心研究,仍需多中心阈值验证与标准化检测流程,进一步明确其在AIH人群中的真实世界表现及其对临床路径的增益,方可推动进入常规临床应用。

总的来说,不同病因的肝纤维化在血清标志物的应用上存在共性,但也具有各自的特点和挑战。针对特定病因,结合病因学特点和疾病进展规律开发或优化诊断策略,是未来研究的重要方向。同时,需要大规模、多中心的研究来验证这些标志物在不同病因肝纤维化中的诊断效能,并制定相应的临床应用指南。随着多组学技术和人工智能在肝纤维化评估中的融合应用,未来针对特定病因开发个体化血清标志物组合,有望提升无创诊断的准确性与临床指导价值。

4. 肝纤维化血清标志物应用的展望

随着研究的不断深入,肝纤维化血清标志物的应用正逐步突破传统的单一诊断范畴,向动态评估、预后预测和治疗监测等全流程管理方向拓展。为进一步提升其临床价值,未来研究需聚焦于肝纤维化机制研究及精准医疗等关键领域。

4.1. 从诊断到预后预测与治疗反应评估

血清标志物目前主要应用于肝纤维化的诊断与分期,但其潜力远不止于此。随着对病理机制的不断深入理解,以及新型分子标志物的不断涌现,其应用正逐步拓展至疾病预后预测与治疗反应评估等更复杂环节。例如,反映细胞外基质(ECM)动态重塑的III型前胶原(PRO-C3)、IV型前胶原(PRO-C4)等标志物,已被证实能独立预测晚期慢性肝病(ACLD)患者的首次失代偿及肝脏相关事件[26]。此外,标志物谱系正从ECM扩展到全身性病理生理过程,如血清总胆汁酸水平的升高与疾病严重程度和生存率降低独立相关[54];而肝硬化预后指数(DGPRI)则能有效预测甲胎蛋白(AFP)阴性HCC患者术后的复发风险。这些发现共同凸显了血清标志物在构建多维预后模型中的巨大潜力[55]

在抗纤维化治疗时代,血清标志物为动态、无创地监测治疗反应提供了不可或缺的工具。其价值在于能够频繁、便捷地评估纤维化的“活动性”与“逆转”情况,从而实时指导临床决策。这一价值已在多项研究中得到印证:在ARMOR试验中,Ratziu等人(2024)发现Aramchol通过改善肝脏硬度和降低PRO-C3水平等指标,证实了其对代谢功能障碍相关脂肪性肝炎(MASH)患者纤维化的疗效[56]。Karsdal等人(2023)进一步证实III型前胶原(PRO-C3)、IV型前胶原(PRO-C4)能够预测晚期代谢功能障碍相关脂肪性肝病(MAFLD)患者对减重治疗的组织学应答[27]。更进一步,Chang等人(2024)引入的基于生物力学标志物的机器学习方法,为治疗评估开辟了全新的定量途径[46]

未来,血清标志物有望贯穿慢性肝病从早筛、动态监测到疗效评估的全过程,成为连接基础机制与临床策略的重要桥梁,助力实现真正意义上的个体化精准治疗。

4.2. 新型标志物的发现与精准医疗

新型生物标志物的发现正从依赖单一指标转向基于多组学整合的策略。高通量蛋白质组学、代谢组学及单细胞测序等技术能够从循环DNA、外泌体、微RNA、蛋白质或代谢产物中筛选出候选标志物[50]。例如,Kotsiliti (2023)通过系统性蛋白质组学分析,将组织蛋白酶S确定为人类纤维化肝脏中ECM重塑的核心节点,为其作为新型诊断标志物和治疗靶点提供了坚实依据[31]。与此同时,对肝纤维化分子机制的深入理解,正推动具有明确病理生理学功能的标志物与靶点的发现。例如,Surf4缺陷可减轻小鼠肝纤维化[57]、S100A4下调可改善病理性修复[58],以及IP3R抑制剂dmXeB可通过抑制HSC活化缓解纤维化进程等发现[59]。提示这些分子更可能首先作为机制通路节点或候选标志物来源。但现阶段其证据多来自动物或细胞实验,距离“可用于临床检测的血清标志物”仍需经历:可检测性验证、检测方法学标准化、阈值建立、前瞻性多中心验证、与现有Standard of Care的增益比较等关键步骤。

5. 总结

肝纤维化是慢性肝病进展中可逆且关键的病理阶段,其早期识别与动态评估对改善患者预后至关重要。血清标志物凭借其无创、便捷与可重复等优势,已成为肝纤维化评估的重要工具。随着分子生物学及多组学技术的迅速发展,血清标志物的研究正从传统间接指标迈向反映分子机制的直接标志物,并通过与影像学、人工智能及临床数据的深度融合,推动肝纤维化评估走向多维度、智能化与个体化。

尽管如此,现阶段血清标志物在病因特异性、检测标准化及大规模验证等方面仍存在局限。未来研究应聚焦于高性能标志物的筛选、检测体系的标准化建设以及智能化综合评估平台的构建。只有将基础研究与临床应用深度衔接,方能实现血清标志物从“检测指标”到“临床决策工具”的转化,最终推动肝纤维化的精准诊疗与全程管理。

NOTES

*第一作者。

#通讯作者。

参考文献

[1] Zamani, M., Alizadeh-Tabari, S., Ajmera, V., Singh, S., Murad, M.H. and Loomba, R. (2025) Global Prevalence of Advanced Liver Fibrosis and Cirrhosis in the General Population: A Systematic Review and Meta-Analysis. Clinical Gastroenterology and Hepatology, 23, 1123-1134. [Google Scholar] [CrossRef] [PubMed]
[2] Akkız, H., Gieseler, R.K. and Canbay, A. (2024) Liver Fibrosis: From Basic Science Towards Clinical Progress, Focusing on the Central Role of Hepatic Stellate Cells. International Journal of Molecular Sciences, 25, Article No. 7873. [Google Scholar] [CrossRef] [PubMed]
[3] Loomba, R. and Adams, L.A. (2020) Advances in Non-Invasive Assessment of Hepatic Fibrosis. Gut, 69, 1343-1352. [Google Scholar] [CrossRef] [PubMed]
[4] 马嘉蹊, 赵鸿, 王艳, 王贵强. 肝纤维化无创诊断研究进展[J]. 传染病信息, 2022, 35(3): 271-275.
[5] 魏莎, 陈晰, 章臻翊, 邓秀娟, 张镓, 吴浪. 肝纤维化的早期血清标志物研究进展[J]. 实用预防医学, 2024, 31(2): 252-257.
[6] Tagliaferro, M., Marino, M., Basile, V., Pocino, K., Rapaccini, G.L., Ciasca, G., et al. (2024) New Biomarkers in Liver Fibrosis: A Pass through the Quicksand? Journal of Personalized Medicine, 14, Article No. 798. [Google Scholar] [CrossRef] [PubMed]
[7] Maroto-García, J., Moreno Álvarez, A., Sanz de Pedro, M.P., Buño-Soto, A. and González, Á. (2023) Serum Biomarkers for Liver Fibrosis Assessment. Advances in Laboratory Medicine, 5, 115-130. [Google Scholar] [CrossRef] [PubMed]
[8] Ling, S., Diao, H., Lu, G. and Shi, L. (2024) Associations between Serum Levels of Liver Function Biomarkers and All-Cause and Cause-Specific Mortality: A Prospective Cohort Study. BMC Public Health, 24, Article No. 3302. [Google Scholar] [CrossRef] [PubMed]
[9] Bera, C., Hamdan-Perez, N. and Patel, K. (2024) Non-Invasive Assessment of Liver Fibrosis in Hepatitis B Patients. Journal of Clinical Medicine, 13, Article No. 1046. [Google Scholar] [CrossRef] [PubMed]
[10] Castellana, M., Donghia, R., Guerra, V., Procino, F., Castellana, F., Zupo, R., et al. (2021) Fibrosis-4 Index vs Nonalcoholic Fatty Liver Disease Fibrosis Score in Identifying Advanced Fibrosis in Subjects with Nonalcoholic Fatty Liver Disease: A Meta-Analysis. American Journal of Gastroenterology, 116, 1833-1841. [Google Scholar] [CrossRef] [PubMed]
[11] Hashem, M.B., Alem, S.A., Elsharkawy, A., Fouad, R., Esmat, G. and Abdellatif, Z. (2023) Performance of Albumin-Bilirubin (ALBI) Score in Comparison to Other Non-Invasive Markers in the Staging of Liver Fibrosis in Chronic HCV Patients. Egyptian Liver Journal, 13, Article No. 40. [Google Scholar] [CrossRef
[12] Allam, A.S., Elkarmouty, K.Z., Kaisar, H.H., Arafah, M.M. and Gadallah, S.H. (2023) Impact of Direct Acting Antivirals Therapy on Novel Fibrosis Index for Assessment of Hepatic Fibrosis in Comparison with AST to Platelet Ratio and Fibrosis-4 (FIB-4) Indexes in Egyptian Patients with Chronic Hepatitis C Infection in Correlation with Fibroscan. The Egyptian Journal of Hospital Medicine, 90, 255-261. [Google Scholar] [CrossRef
[13] Wang, J., Sun, X., Wei, S., Chen, X., Zhu, H., Liantang, Y., et al. (2024) Noninvasive Models for the Prediction of Liver Fibrosis in Patients with Chronic Hepatitis B. BMC Gastroenterology, 24, Article No. 183. [Google Scholar] [CrossRef] [PubMed]
[14] Lin, Z.H., Xin, Y.N., Dong, Q.J., Wang, Q., Jiang, X., Zhan, S., et al. (2011) Performance of the Aspartate Aminotransferase-to-Platelet Ratio Index for the Staging of Hepatitis C-Related Fibrosis: An Updated Meta-Analysis. Hepatology, 53, 726-736. [Google Scholar] [CrossRef] [PubMed]
[15] Kouvari, M., Valenzuela-Vallejo, L., Guatibonza-Garcia, V., Polyzos, S.A., Deng, Y., Kokkorakis, M., et al. (2023) Liver Biopsy-Based Validation, Confirmation and Comparison of the Diagnostic Performance of Established and Novel Non-Invasive Steatotic Liver Disease Indexes: Results from a Large Multi-Center Study. Metabolism, 147, Article ID: 155666. [Google Scholar] [CrossRef] [PubMed]
[16] Li, Y., Chen, Y. and Zhao, Y. (2014) The Diagnostic Value of the FIB-4 Index for Staging Hepatitis B-Related Fibrosis: A Meta-Analysis. PLOS ONE, 9, e105728. [Google Scholar] [CrossRef] [PubMed]
[17] Liguori, A., Zoncapè, M., Casazza, G., Easterbrook, P. and Tsochatzis, E.A. (2025) Staging Liver Fibrosis and Cirrhosis Using Non-Invasive Tests in People with Chronic Hepatitis B to Inform WHO 2024 Guidelines: A Systematic Review and Meta-Analysis. The Lancet Gastroenterology & Hepatology, 10, 332-349. [Google Scholar] [CrossRef] [PubMed]
[18] Park, H., Kim, M., Kim, H., Cho, S., Yoon, E.L. and Jun, D.W. (2024) Diagnostic Performances of Fibrosis‐4 Index and Nonalcoholic Fatty Liver Disease Fibrosis Score in Metabolic Dysfunction‐Associated Steatotic Liver Disease in Asian Primary Care Clinics. Hepatology Research, 54, 1027-1034. [Google Scholar] [CrossRef] [PubMed]
[19] Castera, L., Rinella, M.E. and Tsochatzis, E.A. (2025) Noninvasive Assessment of Liver Fibrosis. New England Journal of Medicine, 393, 1715-1729. [Google Scholar] [CrossRef
[20] Connoley, D., Patel, P.J., Hogan, B., Tanwar, S., Rhodes, F., Parkes, J., et al. (2021) The Enhanced Liver Fibrosis Test Maintains Its Diagnostic and Prognostic Performance in Alcohol-Related Liver Disease: A Cohort Study. BMC Gastroenterology, 21, Article No. 268. [Google Scholar] [CrossRef] [PubMed]
[21] Moreno, C., Mueller, S. and Szabo, G. (2019) Non-Invasive Diagnosis and Biomarkers in Alcohol-Related Liver Disease. Journal of Hepatology, 70, 273-283. [Google Scholar] [CrossRef] [PubMed]
[22] Kang, Y.W., Baek, Y.H. and Moon, S.Y. (2024) Sequential Diagnostic Approach Using FIB-4 and ELF for Predicting Advanced Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease. Diagnostics, 14, Article No. 2517. [Google Scholar] [CrossRef] [PubMed]
[23] Kim, B.K., Kim, S.U., Kim, H.S., Park, J.Y., Ahn, S.H., Chon, C.Y., et al. (2012) Prospective Validation of Fibrotest in Comparison with Liver Stiffness for Predicting Liver Fibrosis in Asian Subjects with Chronic Hepatitis B. PLOS ONE, 7, e35825. [Google Scholar] [CrossRef] [PubMed]
[24] Huang, Y., Adams, L.A., Joseph, J., Bulsara, M.K. and Jeffrey, G.P. (2016) The Ability of Hepascore to Predict Liver Fibrosis in Chronic Liver Disease: A Meta‐Analysis. Liver International, 37, 121-131. [Google Scholar] [CrossRef] [PubMed]
[25] Cardoso-Lezama, I., Ramos-Tovar, E., Arellanes-Robledo, J., Vargas-Pozada, E.E., Vásquez-Garzón, V.R., Villa-Treviño, S., et al. (2023) Serum Α-Sma Is a Potential Noninvasive Biomarker of Liver Fibrosis. Toxicology Mechanisms and Methods, 34, 13-19. [Google Scholar] [CrossRef] [PubMed]
[26] Simbrunner, B., Villesen, I.F., Semmler, G., Balcar, L., Kramer, G., Almeida Calvão, J., et al. (2025) Biomarkers of Extracellular Matrix Remodelling Are Linked to Severity and Outcome of Advanced Chronic Liver Disease. Alimentary Pharmacology & Therapeutics. [Google Scholar] [CrossRef
[27] Karsdal, M.A., Hallsworth, K., Scragg, J., Leeming, D.J., Villesen, I.F., Avery, L., et al. (2023) Serum Levels of Fibrogenesis Biomarkers Reveal Distinct Endotypes Predictive of Response to Weight Loss in Advanced Nonalcoholic Fatty Liver Disease. Hepatology Communications, 7, e0254. [Google Scholar] [CrossRef] [PubMed]
[28] Tang, L., Sun, D., Song, S.J., Yip, T.C., Wong, G.L., Zhu, P., et al. (2024) Serum PRO‐C3 Is Useful for Risk Prediction and Fibrosis Assessment in MAFLD with Chronic Kidney Disease in an Asian Cohort. Liver International, 44, 1129-1141. [Google Scholar] [CrossRef] [PubMed]
[29] Abdelmonem, M., Abdelmageed, M., Wasim, H., Eldaly, R. and Saleh Abdelfattah, M. (2023) The Diagnostic Performance of Serum Hyaluronic Acid and Collagen III in Differentiating Liver Fibrosis in HCV-Induced Patients. American Journal of Clinical Pathology, 160, S17-S18. [Google Scholar] [CrossRef
[30] Chen, Q., Mei, L., Zhong, R., Han, P., Wen, J., Han, X., et al. (2023) Serum Liver Fibrosis Markers Predict Hepatic Decompensation in Compensated Cirrhosis. BMC Gastroenterology, 23, Article No. 317. [Google Scholar] [CrossRef] [PubMed]
[31] Kotsiliti, E. (2023) Cathepsin S in Liver Fibrogenesis. Nature Reviews Gastroenterology & Hepatology, 20, 631-631. [Google Scholar] [CrossRef] [PubMed]
[32] 黄丽玲, 吴春城, 梁惠卿, 邱洋佳, 唐金模. 血清标志物壳多糖酶3样蛋白1评价肝纤维化的研究[J]. 医学信息, 2021, 34(10): 32-35.
[33] Zhang, F., Han, Y., Zheng, L., Liu, J., Wu, Y., Bao, Z., et al. (2023) Association of Non-Invasive Markers with Significant Fibrosis in Patients with Nonalcoholic Fatty Liver Disease: A Cross-Sectional Study. Diabetes, Metabolic Syndrome and Obesity, 16, 2255-2268. [Google Scholar] [CrossRef] [PubMed]
[34] Cao, Z., Li, Z., Wang, Y., Liu, Y., Mo, R., Ren, P., et al. (2017) Assessment of Serum Golgi Protein 73 as a Biomarker for the Diagnosis of Significant Fibrosis in Patients with Chronic HBV Infection. Journal of Viral Hepatitis, 24, 57-65. [Google Scholar] [CrossRef] [PubMed]
[35] Zhang, Y., Xu, A., Jin, Y., Gao, J. and He, J. (2025) Diagnostic Value of Serum Golgi Protein 73 in Liver Fibrosis and Inflammation in Patients with Autoimmune Hepatitis. Medicine, 104, e43064. [Google Scholar] [CrossRef] [PubMed]
[36] Kim, D., Sung, M., Park, M., Sun, E., Yoon, S., Yoo, K.H., et al. (2024) Galectin 3‐Binding Protein (LGALS3BP) Depletion Attenuates Hepatic Fibrosis by Reducing Transforming Growth Factor‐β1 (TGF-β1) Availability and Inhibits Hepatocarcinogenesis. Cancer Communications, 44, 1106-1129. [Google Scholar] [CrossRef] [PubMed]
[37] Zhang, C., Liu, Y., Wang, L., Liu, X., Chen, C., Zhang, J., et al. (2024) Dose-Response Relationship between Serum N-Glycan Markers and Liver Fibrosis in Chronic Hepatitis B. Hepatology International, 18, 1434-1447. [Google Scholar] [CrossRef] [PubMed]
[38] Su, R., Yan, L., Jiang, B., Li, J., Li, P., Liu, Y., et al. (2024) A Novel Model Based on Serum N‐glycan Markers for Evaluating Stage of Liver Necroinflammation in Treatment‐Naïve Chronic Hepatitis B Patients. Journal of Medical Virology, 96, e29863. [Google Scholar] [CrossRef] [PubMed]
[39] Krasner, Y.A., Romanov, V.V., Fazullina, O.N., Osipenko, M.F. and Klimontov, V.V. (2024) Serum Biomarkers Associated with Liver Fibrosis in Patients with Type 2 Diabetes. Diabetes Mellitus, 27, 25-32. [Google Scholar] [CrossRef
[40] Dallio, M., Romeo, M., Di Nardo, F., Vaia, P., Napolitano, C., Ventriglia, L., et al. (2025) FLAME: Training and Validating a Newly Conceived Model Incorporating Alpha-Glutathione-S-Transferase Serum Levels for Predicting Advanced Hepatic Fibrosis and Acute Cardiovascular Events in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). International Journal of Molecular Sciences, 26, Article No. 761. [Google Scholar] [CrossRef] [PubMed]
[41] Yu, Z., Nian, C., Sun, W., Liu, X. and Nian, X. (2024) Elevated Serum HE4 Levels as a Novel Biomarker of Disease Severity and Hepatic Fibrosis in Autoimmune Hepatitis. Clinica Chimica Acta, 559, Article ID: 119682. [Google Scholar] [CrossRef] [PubMed]
[42] Diehl, D.L., Sangwan, V., Khurana, S., Khara, H.S., Zhang, J. and Confer, B.D. (2025) Reproducibility of EUS-Guided Shear Wave Elastography for Assessment of Hepatic Fibrosis: A Prospective Pilot Cohort Study. Gastrointestinal Endoscopy, 101, 659-662. [Google Scholar] [CrossRef] [PubMed]
[43] 杨兆辉, 陈珑斌, 马龙. Fibrotouch联合血清标志物评估慢性乙型肝炎肝纤维化的价值[J]. 中国卫生标准管理, 2024, 15(14): 14-18.
[44] Mesropyan, N., Huaroc Moquillaza, E., Chang, J., Lutz, P., Katemann, C., Weiss, K., et al. (2025) Dixon-Based Water T1 Mapping for Fat-Corrected Assessment of Hepatic Fibrosis in Chronic Liver Disease. Investigative Radiology, 61, 84-91. [Google Scholar] [CrossRef] [PubMed]
[45] Zhang, C., Shu, Z., Chen, S., Peng, J., Zhao, Y., Dai, X., et al. (2024) A Machine Learning-Based Model Analysis for Serum Markers of Liver Fibrosis in Chronic Hepatitis B Patients. Scientific Reports, 14, Article No. 12081. [Google Scholar] [CrossRef] [PubMed]
[46] Chang, Z., Peng, C., Chen, K. and Xu, G. (2024) Enhancing Liver Fibrosis Diagnosis and Treatment Assessment: A Novel Biomechanical Markers-Based Machine Learning Approach. Physics in Medicine & Biology, 69, Article ID: 115046. [Google Scholar] [CrossRef] [PubMed]
[47] Huang, X., Huang, S., Dong, C., Chen, N., Wang, Y., Wang, C., et al. (2025) Study on Ultrasound-Assisted Diagnosis of CHB Complicated with NAFLD Hepatic Fibrosis Based on Deep Learning. Journal of Imaging Informatics in Medicine, 38, 4349-4357. [Google Scholar] [CrossRef] [PubMed]
[48] 陈琼, 陈蓉, 曾艺馨, 王悦, 周馥叶. 乙型肝炎病毒感染患者HBV-DNA载量与血清及肝纤维化标志物的关系[J]. 中华医院感染学杂志, 2017, 27(15): 3487-3489+3509.
[49] Chen, L., Jing, X., Chen, X., Xie, Y., Chen, Y. and Cai, X. (2024) Non-Invasive Serum Markers of Non-Alcoholic Fatty Liver Disease Fibrosis: Potential Tools for Detecting Patients with Cardiovascular Disease. Reviews in Cardiovascular Medicine, 25, Article No. 344. [Google Scholar] [CrossRef] [PubMed]
[50] Armandi, A., Michel, M., Gjini, K., Emrich, T., Bugianesi, E. and Schattenberg, J.M. (2023) Emerging Concepts in the Detection of Liver Fibrosis in Non-Alcoholic Fatty Liver Disease. Expert Review of Molecular Diagnostics, 23, 771-782. [Google Scholar] [CrossRef] [PubMed]
[51] Jiang, Y., Zou, J., Fan, F., Yang, P., Ma, L., Gan, T., et al. (2023) Application of Multi-Echo Dixon and MRS in Quantifying Hepatic Fat Content and Staging Liver Fibrosis. Scientific Reports, 13, Article No. 12555. [Google Scholar] [CrossRef] [PubMed]
[52] Huang, J., Yu, J., Wang, J., Liu, J., Xie, W., Li, R., et al. (2022) Novel Potential Biomarkers for Severe Alcoholic Liver Disease. Frontiers in Immunology, 13, Article ID: 1051353. [Google Scholar] [CrossRef] [PubMed]
[53] Yamazaki, T., Kouno, T., Hsu, C.L., Hartmann, P., Mayo, S., Zhang, X., et al. (2024) Serum Aryl Hydrocarbon Receptor Activity Is Associated with Survival in Patients with Alcohol-Associated Hepatitis. Hepatology, 80, 403-417. [Google Scholar] [CrossRef] [PubMed]
[54] Eguchi, A., Iwasa, M., Miyazaki, T., Tempaku, M., Ichishi, M., Tamai, Y., et al. (2025) Conjugated Bile Acids in Serum Reflect Disease Severity and Predict Survival in Chronic Liver Disease of Humans and Rats. Scientific Reports, 15, Article No. 32911. [Google Scholar] [CrossRef
[55] Zhang, B., Xue, J., Xu, B., Chang, J., Li, X., Huang, Z., et al. (2024) DGPRI, a New Liver Fibrosis Assessment Index, Predicts Recurrence of AFP-Negative Hepatocellular Carcinoma after Hepatic Resection: A Single-Center Retrospective Study. Scientific Reports, 14, Article No. 10726. [Google Scholar] [CrossRef] [PubMed]
[56] Ratziu, V., Yilmaz, Y., Lazas, D., Friedman, S.L., Lackner, C., Behling, C., et al. (2024) Aramchol Improves Hepatic Fibrosis in Metabolic Dysfunction-Associated Steatohepatitis: Results of Multimodality Assessment Using both Conventional and Digital Pathology. Hepatology, 81, 932-946. [Google Scholar] [CrossRef] [PubMed]
[57] Wang, B., Li, H., Gill, G., Zhang, X., Tao, G., Liu, B., et al. (2024) Hepatic Surf4 Deficiency Impairs Serum Amyloid A1 Secretion and Attenuates Liver Fibrosis in Mice. Research, 7, Article No. 0435. [Google Scholar] [CrossRef] [PubMed]
[58] Zhang, C., Bai, K. and Li, D. (2025) Downregulation of S100 Calcium-Binding A4 (S100A4) Ameliorates Hepatic Fibrosis via Regulating Wnt/β-Catenin Signaling Pathway. European Journal of Histochemistry, 69, Article No. 4186. [Google Scholar] [CrossRef] [PubMed]
[59] Smith-Cortinez, N., Heegsma, J., Podunavac, M., Zakarian, A., Cardenas, J.C. and Faber, K.N. (2024) Novel Inositol 1,4,5-Trisphosphate Receptor Inhibitor Antagonizes Hepatic Stellate Cell Activation: A Potential Drug to Treat Liver Fibrosis. Cells, 13, Article No. 765. [Google Scholar] [CrossRef] [PubMed]