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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Hepatoma Res.</journal-id>
      <journal-id journal-id-type="publisher-id">HR</journal-id>
      <journal-title-group>
        <journal-title>Hepatoma Research</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2454-2520</issn>
      <publisher>
        <publisher-name>OAE Publishing Inc.</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.20517/2394-5079.2026.10</article-id>
      <article-categories>
        <subj-group>
          <subject>Review</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Gut microbiota as predictors of immunotherapy response in hepatocellular carcinoma</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Xia</surname>
            <given-names>Shufen</given-names>
          </name>
          <xref ref-type="aff" rid="I1">
            <sup>1</sup>
          </xref>
		  <xref ref-type="aff" rid="I2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yang</surname>
            <given-names>Qingxia</given-names>
          </name>
          <xref ref-type="aff" rid="I3">
            <sup>3</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Liu</surname>
            <given-names>Yuyao</given-names>
          </name>
          <xref ref-type="aff" rid="I3">
            <sup>3</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yan</surname>
            <given-names>Aohui</given-names>
          </name>
          <xref ref-type="aff" rid="I1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Li</surname>
            <given-names>Shangru</given-names>
          </name>
          <xref ref-type="aff" rid="I3">
            <sup>3</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Xu</surname>
            <given-names>Linfu</given-names>
          </name>
          <xref ref-type="aff" rid="I2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Guan</surname>
            <given-names>Huayu</given-names>
          </name>
          <xref ref-type="aff" rid="I1">
            <sup>1</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Xie</surname>
            <given-names>Yubin</given-names>
          </name>
          <xref ref-type="aff" rid="I2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Hu</surname>
            <given-names>Shixian</given-names>
          </name>
          <xref ref-type="aff" rid="I2">
            <sup>2</sup>
          </xref>
          <xref ref-type="corresp" rid="cor1" />
          <contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1190-0325</contrib-id>
        </contrib>
      </contrib-group>
	  <aff id="I1">
      <sup>1</sup>Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.</aff>
      <aff id="I2">
        <sup>2</sup>Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.</aff>
      <aff id="I3">
        <sup>3</sup>Department of Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China.</aff>
      <author-notes>
        <corresp id="cor1">Correspondence to: Prof. Shixian Hu, Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, Guangdong, China. E-mail: <email>hushx9@mail.sysu.edu.cn</email></corresp>
        <fn fn-type="other">
          <p>
            <bold>Received:</bold> 30 Jan 2026 | <bold>First Decision:</bold> 19 Mar 2026 | <bold>Revised:</bold> 4 Apr 2026 | <bold>Accepted:</bold> 22 May 2026 | <bold>Published:</bold> 18 Jun 2026</p>
        </fn>
        <fn fn-type="other">
          <p>
            <bold>Academic Editor:</bold> Terence Kin Wah Lee | <bold>Copy Editor:</bold> Ting-Ting Hu | <bold>Production Editor:</bold> Ting-Ting Hu</p>
        </fn>
      </author-notes>
      <pub-date pub-type="ppub">
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>18</day>
        <month>6</month>
        <year>2026</year>
      </pub-date>
      <volume>12</volume>
	  <elocation-id>30</elocation-id>
      <permissions>
        <copyright-statement>© The Author(s) 2026.</copyright-statement>
        <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <license-p>© The Author(s) 2026. <bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License (<uri xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.</license-p>
        </license>
      </permissions>
      <abstract>
        <p>Hepatocellular carcinoma (HCC) remains a major cause of global cancer-related mortality, with limited therapeutic options for advanced disease. The advent of immune checkpoint inhibitors (ICIs) has revolutionized HCC treatment; however, only a subset of patients achieve durable responses, underscoring the urgent need for reliable biomarkers to guide precision immunotherapy. Emerging evidence highlights the gut microbiota as a determinant of immunotherapy efficacy through the gut-liver axis. Community diversity, specific microbial taxa, and functional metabolites have been associated with responses to ICIs. Mechanistically, gut microbes influence antigen presentation, T cell activation, and immune tolerance within the hepatic microenvironment. Moreover, microbiota-targeted interventions hold promise for restoring responsiveness in non-responders. This review summarizes the regulatory roles of the gut microbiota in HCC immunotherapy and discusses the potential of microbiota-based strategies as predictive and therapeutic tools. Integrating multi-omics, strain-level analysis, and high-resolution microbial profiling is crucial for realizing microbiota-informed precision immunotherapy in HCC.</p>
      </abstract>
      <kwd-group>
        <kwd>Gut microbiota</kwd>
        <kwd>hepatocellular carcinoma</kwd>
        <kwd>immunotherapy response</kwd>
        <kwd>microbial regulation</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec1">
      <title>INTRODUCTION</title>
      <p>Hepatocellular carcinoma (HCC) accounts for 75%-85% of the approximately 0.9 million liver cancer cases diagnosed in 2022<sup>[<xref ref-type="bibr" rid="B1">1</xref>]</sup>. It ranks third among the global leading causes of cancer-related mortality due to its heterogeneity and coexistence with other underlying liver diseases<sup>[<xref ref-type="bibr" rid="B2">2</xref>]</sup>, with an estimated 5-year survival rate of 20%<sup>[<xref ref-type="bibr" rid="B1">1</xref>,<xref ref-type="bibr" rid="B3">3</xref>,<xref ref-type="bibr" rid="B4">4</xref>]</sup>. Despite surgical resection, liver transplantation, and locoregional ablation therapies being effective for patients with early-stage HCC, most patients are diagnosed at an advanced stage and hence are ineligible for curative treatment<sup>[<xref ref-type="bibr" rid="B5">5</xref>]</sup>. Recently, immune checkpoint inhibitor (ICI)-based combinations have revolutionized the first-line treatment landscape for unresectable HCC. Pivotal Phase III trials, including IMbrave150 (atezolizumab plus bevacizumab)<sup>[<xref ref-type="bibr" rid="B6">6</xref>]</sup>, HIMALAYA (durvalumab plus tremelimumab)<sup>[<xref ref-type="bibr" rid="B7">7</xref>]</sup>, and the recent CheckMate-9DW (nivolumab plus ipilimumab)<sup>[<xref ref-type="bibr" rid="B8">8</xref>]</sup>, have achieved objective response rates (ORRs) of approximately 20.1%-36%, compared with 5.1%-18.8% for multi-target kinase inhibitors, such as sorafenib and lenvatinib, in their respective pivotal trials. However, durable clinical benefit (DCB) is limited to a subset of patients, reflecting the lack of robust biomarkers for response prediction<sup>[<xref ref-type="bibr" rid="B9">9</xref>]</sup>. Current biomarkers - including programmed death-ligand 1 (PD-L1) expression, tumor mutational burden, and immune gene signatures - are of limited predictive value<sup>[<xref ref-type="bibr" rid="B4">4</xref>,<xref ref-type="bibr" rid="B10">10</xref>-<xref ref-type="bibr" rid="B14">14</xref>]</sup>, underscoring the urgent need for more effective tools to identify responders and guide individualized treatment strategies.</p>
      <p>The gut microbiota plays a crucial role in regulating host immunity and metabolism, and may also impact the efficacy of cancer immunotherapy. In non-small cell lung cancer (NSCLC) and renal cell carcinoma, patients with higher gut microbial diversity respond more favorably to anti-PD-1 therapy<sup>[<xref ref-type="bibr" rid="B15">15</xref>]</sup>. In melanoma, patients with prolonged progression-free survival (PFS ≥ 12 months) exhibit increased abundances of <italic>Bacteroides</italic> and <italic>Lactobacillus</italic> species in their gut microbiota<sup>[<xref ref-type="bibr" rid="B16">16</xref>]</sup>. Several clinical trials of fecal microbiota transplantation (FMT) from immune checkpoint blockade (ICB) responders or healthy donors to non-responders have demonstrated that this approach can restore sensitivity to anti-PD-1 treatment in melanoma<sup>[<xref ref-type="bibr" rid="B17">17</xref>,<xref ref-type="bibr" rid="B18">18</xref>]</sup>.</p>
      <p>Although the relationship between the gut microbiota and immunotherapy has been extensively studied in multiple cancer types, its role in HCC, a malignancy uniquely shaped by the gut-liver axis<sup>[<xref ref-type="bibr" rid="B19">19</xref>]</sup>, remains underexplored and lacks a systematic synthesis, which hinders clinical translation. Considering that the liver maintains a close bidirectional interaction with the gut microbiota and its metabolites, it is crucial to understand this interplay. In this review, we summarize the current knowledge on the relationship between gut microbiota and immunotherapy response in HCC, discuss the underlying mechanisms, and highlight the potential of microbiota-based strategies as predictive biomarkers and therapeutic targets in precision oncology.</p>
    </sec>
    <sec id="sec2">
      <title>GUT-LIVER AXIS AND MICROBIAL DYSBIOSIS IN HCC</title>
      <p>The gut and liver are anatomically and functionally linked through the portal venous system, which drains nutrient-rich blood, microbial products, and metabolites from the intestine directly to the liver. This portal circulation provides approximately 75% of the liver’s blood supply, positioning the liver as the first organ to encounter gut-derived signals under both physiological and pathological conditions<sup>[<xref ref-type="bibr" rid="B20">20</xref>,<xref ref-type="bibr" rid="B21">21</xref>]</sup>. The gut microbiota and its metabolites, such as short-chain fatty acids (SCFAs) and bile acids (BAs), profoundly influence hepatic immune homeostasis, bile acid metabolism, and energy homeostasis<sup>[<xref ref-type="bibr" rid="B22">22</xref>,<xref ref-type="bibr" rid="B23">23</xref>]</sup>, whereas liver-derived BAs and cytokines reciprocally shape the intestinal microbial composition and mucosal integrity<sup>[<xref ref-type="bibr" rid="B24">24</xref>,<xref ref-type="bibr" rid="B25">25</xref>]</sup>. When this delicate equilibrium is disrupted, microbial translocation and altered metabolite profiles can trigger chronic inflammation and tumorigenic signaling within the liver<sup>[<xref ref-type="bibr" rid="B26">26</xref>]</sup>. For instance, impairment of the intestinal barrier can facilitate the translocation of <italic>Klebsiella pneumoniae</italic> from the gut to distal hepatic tumors, thereby promoting local tumor proliferation<sup>[<xref ref-type="bibr" rid="B27">27</xref>]</sup>. In parallel, microbiota-derived indole metabolites, including indole-3-acetic acid (IAA) and indole-3-propionate, suppress inflammation and attenuate hepatic steatosis in nonalcoholic fatty liver disease (NAFLD) or nonalcoholic steatohepatitis<sup>[<xref ref-type="bibr" rid="B28">28</xref>,<xref ref-type="bibr" rid="B29">29</xref>]</sup>.</p>
      <p>HCC usually develops as the end stage of chronic liver diseases with various etiologies, including NAFLD, alcohol-associated liver disease (ALD), and liver cirrhosis<sup>[<xref ref-type="bibr" rid="B30">30</xref>-<xref ref-type="bibr" rid="B32">32</xref>]</sup>. In metabolic dysfunction-associated steatotic liver disease (MASLD), the gut microbiota is characterized by a decrease in the abundance of beneficial and SCFA-producing bacteria, such as <italic>Ruminococcaceae</italic> and <italic>Faecalibacterium</italic><sup>[<xref ref-type="bibr" rid="B33">33</xref>]</sup>. In ALD, dysbiosis is mainly marked by the overgrowth of pathogenic bacteria (<italic>Enterococcus</italic><sup>[<xref ref-type="bibr" rid="B34">34</xref>]</sup>, <italic>Klebsiella</italic><sup>[<xref ref-type="bibr" rid="B35">35</xref>]</sup>) and fungi (notably <italic>Candida albicans</italic>)<sup>[<xref ref-type="bibr" rid="B36">36</xref>]</sup>. Liver cirrhosis is associated with severe loss of microbial diversity, small intestinal bacterial overgrowth<sup>[<xref ref-type="bibr" rid="B37">37</xref>]</sup>, and enrichment of potentially pathogenic taxa, including <italic>Escherichia coli</italic> and <italic>Klebsiella pneumoniae</italic><sup>[<xref ref-type="bibr" rid="B38">38</xref>]</sup>. During progression to HCC, tumor-associated microbial signatures emerge, featuring the enrichment of <italic>Enterococcus</italic> and <italic>Klebsiella</italic>, along with the depletion of beneficial taxa such as <italic>Bifidobacterium</italic> and <italic>Akkermansia</italic><sup>[<xref ref-type="bibr" rid="B39">39</xref>,<xref ref-type="bibr" rid="B40">40</xref>]</sup>. Although the patterns of gut microbiota dysbiosis vary across different etiologies and stages of liver disease, their overall functional consequences are largely convergent. These variations contribute to intestinal barrier disruption, increased gut permeability, and translocation of microbial-associated molecular patterns and toxins in portal circulation<sup>[<xref ref-type="bibr" rid="B41">41</xref>]</sup>.</p>
    </sec>
    <sec id="sec3">
      <title>GUT MICROBIAL SIGNATURES PREDICTING HCC IMMUNOTHERAPY RESPONSE</title>
      <p>Specific gut microbial features are linked to immunotherapy efficacy in HCC, allowing the stratification of patients into responders and non-responders<sup>[<xref ref-type="bibr" rid="B42">42</xref>-<xref ref-type="bibr" rid="B44">44</xref>]</sup>. Stool-derived microbiome profiling is noninvasive and readily accessible, providing baseline microbial information<sup>[<xref ref-type="bibr" rid="B42">42</xref>,<xref ref-type="bibr" rid="B43">43</xref>]</sup>. The potential predictive features of the gut microbiome can be broadly classified into the following three categories: (i) community structure, capturing global characteristics, such as microbial diversity; (ii) taxonomic composition, referring to individual microbial taxa which can be specifically targeted or modulated; and (iii) microbial molecules, encompassing proteins and metabolites, which may serve as mechanistically relevant biomarkers.</p>
      <sec id="sec3-1">
        <title>Community structure</title>
        <p>A high-diversity baseline and community stability are recurrent hallmarks of clinical response in HCC immunotherapy. While alpha- and beta-diversity metrics serve as valuable population-level descriptors, their transition into individualized predictive biomarkers has been underwhelming. In a pilot study enrolling eight patients with HCC at Barcelona Clinic Liver Cancer (BCLC) Stage C receiving anti-PD-1 treatment after sorafenib failure, responders (complete or partial response, or stable disease lasting &gt; 6 months; <italic>N</italic> = 3) showed higher taxa richness than that in non-responders (progressed disease or stable disease lasting &lt; 6 months; <italic>N</italic> = 5)<sup>[<xref ref-type="bibr" rid="B43">43</xref>]</sup>. Consistently, in another cohort consisting of 35 patients with HCC, 16 were classified as responders and 19 as non-responders. During treatment, responders exhibited higher alpha diversity than that in non-responders, as indicated by both Shannon and inverse Simpson indices. Furthermore, beta diversity analyses demonstrated distinct clustering of gut microbial communities between the two groups<sup>[<xref ref-type="bibr" rid="B44">44</xref>]</sup>. Longitudinal analyses further indicated that the overall community structure of the gut microbiota remains largely stable during immunotherapy. For instance, in studies involving patients with NSCLC, repeated sampling throughout anti-PD-1 therapy revealed that alpha and beta diversity metrics did not fluctuate significantly over time, while responders and non-responders maintained distinct community profiles<sup>[<xref ref-type="bibr" rid="B45">45</xref>,<xref ref-type="bibr" rid="B46">46</xref>]</sup>. Although longitudinal data in HCC are limited, similar patterns in other cancers support the potential of the gut microbiome as a stable indicator for patient stratification<sup>[<xref ref-type="bibr" rid="B16">16</xref>]</sup>. Overall, these studies report statistical significance in diversity shifts but fail to provide robust performance metrics for patient stratification.</p>
      </sec>
      <sec id="sec3-2">
        <title>Taxonomic composition</title>
        <p>At the taxonomic level, the definitive microbial signatures for ICI responders in HCC are characterized by significant inter-study heterogeneity. For example, a small-scale study involving eight patients with advanced HCC found that, at the genus level, responders more frequently exhibit a balanced <italic>Firmicutes</italic>/<italic>Bacteroidetes</italic> ratio (generally 0.5-1.5, 66.7% <italic>vs</italic>. 10%) and higher <italic>Prevotella/Bacteroides</italic> ratio (22.99 <italic>vs</italic>. 2.312) than those in non-responders<sup>[<xref ref-type="bibr" rid="B47">47</xref>]</sup>. In addition, responders exhibit the enrichment of <italic>Lactobacillus</italic> species, <italic>Lachnospiraceae bacterium</italic>, <italic>Alistipes</italic> sp., and <italic>Ruminococcaceae</italic> spp. Specifically, patients enriched for <italic>Lachnospiraceae bacterium</italic> achieve longer PFS [median PFS (mPFS): 7.9 months <italic>vs</italic>. 5.1 months] and overall survival [OS; median OS (mOS): not reached (NR) <italic>vs</italic>. 13.8 months]. Similar clinical benefits were observed in patients with a higher abundance of <italic>Alistipes</italic> sp. (mPFS: 9.0 months <italic>vs</italic>. 5.2 months; mOS: NR months <italic>vs</italic>. 13.8 months)<sup>[<xref ref-type="bibr" rid="B42">42</xref>,<xref ref-type="bibr" rid="B43">43</xref>]</sup>. However, one cohort study (<italic>N</italic> = 65) associated <italic>Veillonellaceae</italic> enrichment with worsening PFS (mPFS: 3.6 months <italic>vs</italic>. 10.8 months) and OS (mOS: 7.8 months <italic>vs</italic>. NR)<sup>[<xref ref-type="bibr" rid="B42">42</xref>]</sup>, which is consistent with the reported role of <italic>Veillonellaceae</italic> in liver cirrhosis progression, whereas another study (<italic>N</italic> = 41) reports its enrichment in responders<sup>[<xref ref-type="bibr" rid="B48">48</xref>]</sup>. Such discrepancies may stem from variations in prior sorafenib exposure or underlying viral etiologies [hepatitis B virus (HBV) <italic>vs</italic>. hepatitis C virus (HCV)], which profoundly shape the baseline microbiome. Despite these localized inconsistencies, the following robust consensus is emerging from pan-cancer meta-analyses: biomarkers such as <italic>Akkermansia muciniphila</italic> and <italic>Faecalibacterium prausnitzii</italic> are consistently enriched in responders, indicating their universal role in anti-tumor immunity<sup>[<xref ref-type="bibr" rid="B43">43</xref>,<xref ref-type="bibr" rid="B47">47</xref>]</sup>. While ICI-associated biomarkers are still being refined, these key species represent the most reliable mechanistic anchors for current microbiome-based stratification.</p>
      </sec>
      <sec id="sec3-3">
        <title>Microbial metabolites</title>
        <p>Beyond taxonomic inconsistencies, microbial functional molecules have emerged as more stable and biologically relevant predictors of ICI efficacy. While microbial compositions may vary across different HCC etiologies, their downstream metabolic outputs, particularly BAs and SCFAs, exhibit remarkable predictive consistency<sup>[<xref ref-type="bibr" rid="B42">42</xref>]</sup>.</p>
        <p>Primary BAs are produced from cholesterol in the liver, conjugated with glycine or taurine, and released into the intestine, where they are deconjugated and dehydroxylated by gut microbiota to secondary BAs<sup>[<xref ref-type="bibr" rid="B49">49</xref>]</sup>. Higher levels of ursodeoxycholic acid (UDCA), tauro-UDCA (TUDCA), ursocholic acid (UCA), and murideoxycholic acid (MDCA) have been observed in HCC immunotherapy responders (OR, <italic>N</italic> = 20) than those in non-responders [progressive disease (PD), <italic>N</italic> = 21], suggesting that BA profiles may serve as predictive metabolic signatures for treatment outcomes<sup>[<xref ref-type="bibr" rid="B48">48</xref>]</sup>.</p>
        <p>SCFAs contain fewer than six carbon atoms. In the proximal intestine, dietary fiber cannot be degraded by host digestive enzymes and thus passes into the cecum and colon, where it is fermented by the gut microbiota to generate SCFAs (primarily acetate, propionate, and butyrate)<sup>[<xref ref-type="bibr" rid="B50">50</xref>,<xref ref-type="bibr" rid="B51">51</xref>]</sup>. A prospective cohort study included 77 patients with virus-related HCC (V-HCC) and 25 patients with MASLD-associated HCC who initiated first-line combination immunotherapy. Pre-treatment fecal microbiota and metabolites, as well as serum cytokine and chemokines, were analyzed in relation to durable tumor response, OS, and PFS. Patients with MASLD-HCC showed lower baseline levels of SCFAs and UDCA than those in patients with V-HCC, whereas durable responders (DRs) exhibited higher levels of SCFAs (including acetate, propionate, butyrate, and isobutyrate) and UDCA. Similarly, DRs in the V-HCC cohort also displayed higher SCFA levels despite differing microbial backgrounds. In both MASLD-HCC and V-HCC cases, fecal acetate levels were a common predictor of DR, PFS, and OS. Patients with relatively higher acetate levels exhibited significantly longer OS (mOS: 25.2 months <italic>vs</italic>. 11.3 months) and PFS (mPFS: 15.3 months <italic>vs</italic>. 4.2 months)<sup>[<xref ref-type="bibr" rid="B52">52</xref>]</sup>.</p>
        <p>This functional convergence suggests that metabolic signatures transcend the inherent noise of taxonomic profiling, offering a more robust framework for patient stratification. Therefore, the metabolic profiling of fecal or serum samples represents a critical frontier in moving from associative observations to mechanistically grounded clinical predictions.</p>
      </sec>
    </sec>
    <sec id="sec4">
      <title>REGULATORY ROLES OF GUT MICROBIOTA IN HCC IMMUNOTHERAPY RESPONSE</title>
      <sec id="sec4-1">
        <title>Microbiota-associated immune microenvironment in HCC immunotherapy</title>
        <p>Cancer immunoediting suggests that tumor immune responses undergo the following three phases: elimination, equilibrium, and escape<sup>[<xref ref-type="bibr" rid="B53">53</xref>]</sup>. In HCC, the final escape phase is characterized by the establishment of an immunosuppressive tumor microenvironment (TME). To evade immune recognition, tumor cells downregulate antigen presentation and secrete inhibitory factors [e.g., arginase, transforming growth factor-beta (TGF-β)], while upregulating checkpoint molecules such as PD-L1<sup>[<xref ref-type="bibr" rid="B54">54</xref>]</sup>. ICIs are designed to disrupt these evasive tactics by releasing the “molecular brakes” on T-cell function. Within the HCC TME, PD-1 is overexpressed on exhausted T cells, with its binding to PD-L1 on tumor cells or suppressive populations (e.g., myeloid-derived suppressor cells and tumor-associated macrophages) restricting cytokine production and cytotoxicity. Anti-PD-1/PD-L1 antibodies (e.g., nivolumab, pembrolizumab) reinvigorate these effector cells by blocking this inhibitory axis. Similarly, cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) competes with CD28 for B7 molecules on antigen-presenting cells, thereby limiting T-cell priming. CTLA-4 inhibitors (e.g., ipilimumab, tremelimumab) enhance T-cell priming and reduce Treg-mediated immunosuppression, thereby promoting a more immune-active TME<sup>[<xref ref-type="bibr" rid="B55">55</xref>,<xref ref-type="bibr" rid="B56">56</xref>]</sup>.</p>
      </sec>
      <sec id="sec4-2">
        <title>Gut microbiota-immune cell crosstalk</title>
        <p>Accumulating evidence highlights specific gut microbial signatures and their metabolites as pivotal orchestrators of the hepatic immune microenvironment, directly modulating both innate and adaptive immunity [<xref ref-type="fig" rid="fig1">Figure 1A</xref>-<xref ref-type="fig" rid="fig1">D</xref>].</p>
        <fig id="fig1" position="float">
          <label>Figure 1</label>
          <caption>
            <p>Roles of the gut microbiota in HCC immunotherapy response. (A) Microbiota-derived SCFAs exert context-dependent effects on hepatic immunity, regulating M1 macrophage polarization, ILC3 function, and Treg differentiation; (B) Microbiota-derived BAs induce immunosuppressive reprogramming of macrophages and Kupffer cells, suppressing antitumor immunity; (C) Microbial reprogramming of IAA production impairs CD8<sup>+</sup> T-cell function, contributing to immunotherapy resistance; (D) Co-stimulation of microbiota-derived riboflavin metabolites via MR1 and CpG (TLR9 agonist) activates MAIT cells, thus enhancing antitumor immune responses; (E) Microbial components and intrahepatic bacteria promote HCC cell invasion and proliferation, thereby impairing antitumor immune responses. Created in BioRender. Hu, S. (2026) <uri xlink:href="https://BioRender.com/rh59ehn">https://BioRender.com/rh59ehn</uri>. HCC: Hepatocellular carcinoma; SCFA: short-chain fatty acid; ILC3: group 3 innate lymphoid cell; Treg: regulatory T cell; BA: bile acid; TCA: taurocholic acid; isoallo-LCA: isoallolithocholic acid; MP2: Marco<sup>+</sup> IL-10<sup>+</sup> Kupffer cells; IAA: indole-3-acetic acid; 5-OP-RU: 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil; MAIT: mucosal-associated invariant T cell; IFN-γ: interferon-γ; GZMB: granzyme B; LPS: microbial-derived lipopolysaccharide; TLR: Toll-like receptor.</p>
          </caption>
          <graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hr12010.fig.1.jpg" />
        </fig>
        <p>SCFAs, particularly acetate and butyrate, potentially exert divergent, context-dependent effects on hepatic immunity. Acetate potentially enhances antitumor surveillance. For instance, <italic>Bacteroides thetaiotaomicron</italic>-derived acetate may promote M1 macrophage polarization via histone acetylation at the <italic>ACC1</italic> promoter, subsequently augmenting CD8<sup>+</sup> T-cell effector functions<sup>[<xref ref-type="bibr" rid="B57">57</xref>]</sup>. Correspondingly, acetate depletion, often associated with <italic>Lactobacillus reuteri</italic> reduction, increases interleukin (IL)-17A production by hepatic ILC3s via <italic>Sox13</italic>-mediated transcriptional reprogramming. Restoring acetate levels, particularly when combined with PD-1/PD-L1 blockade, alleviates this pro-tumorigenic inflammation and significantly enhances immunotherapy efficacy<sup>[<xref ref-type="bibr" rid="B58">58</xref>]</sup>. In contrast, butyrate enrichment in MASLD-HCC is associated with an immunosuppressive microenvironment. High butyrate concentrations correlate negatively with cytotoxic CD8<sup>+</sup> T-cell infiltration<sup>[<xref ref-type="bibr" rid="B59">59</xref>]</sup>. Mechanistically, SCFAs such as propionate and butyrate may function as histone deacetylase inhibitors, thereby enhancing <italic>Foxp3</italic> expression and facilitating Treg-mediated immune suppression<sup>[<xref ref-type="bibr" rid="B22">22</xref>,<xref ref-type="bibr" rid="B60">60</xref>-<xref ref-type="bibr" rid="B63">63</xref>]</sup>.</p>
        <p>BA derivatives shaped by specific gut taxa serve as signaling molecules that reprogram hepatic macrophages toward an immunosuppressive phenotype. Primary BAs such as taurocholic acid, whose metabolism is influenced by gut microbes, including <italic>Clostridium</italic> spp. and <italic>Akkermansia muciniphila</italic>, may favor M2-like macrophage polarization via FXR/TGR5 signaling<sup>[<xref ref-type="bibr" rid="B64">64</xref>-<xref ref-type="bibr" rid="B66">66</xref>]</sup>. M2-like macrophages secrete immunosuppressive cytokines (IL-10, TGF-β), which inhibit CD8<sup>+</sup> T cell cytotoxicity, thereby counteracting T cell-mediated tumor cytotoxicity and facilitating immune evasion<sup>[<xref ref-type="bibr" rid="B67">67</xref>,<xref ref-type="bibr" rid="B68">68</xref>]</sup>. Similarly, isoallo-LCA, a secondary BA produced by Odoribacteraceae, induces a specialized subset of Marco<sup>+</sup> IL-10<sup>+</sup> Kupffer cells (MP2) through activation of the nuclear hormone receptor, NR4A1<sup>[<xref ref-type="bibr" rid="B69">69</xref>,<xref ref-type="bibr" rid="B70">70</xref>]</sup>. The NR4A1-mediated induction of MP2 cells may foster an immunosuppressive microenvironment characterized by increased IL-10 production, thereby potentially dampening antitumor immunity<sup>[<xref ref-type="bibr" rid="B71">71</xref>]</sup>.</p>
        <p>The reprogramming of tryptophan metabolism by gut commensals represents another pivotal mechanism underlying immunotherapy resistance. Higher <italic>Phocaeicola vulgatus</italic> abundance is associated with shorter PFS and OS<sup>[<xref ref-type="bibr" rid="B72">72</xref>]</sup>. Mechanistic validation in HCC mouse models suggests that <italic>P. vulgatus</italic> colonization markedly reduced the efficacy of PD-1 blockade. This resistance potentially correlates with a metabolic rerouting wherein, unlike commensals, which convert tryptophan to IAA via the indolepyruvate decarboxylase enzyme<sup>[<xref ref-type="bibr" rid="B73">73</xref>]</sup>, <italic>P. vulgatus</italic> utilizes an indolepyruvate oxidoreductase-mediated pathway that diverts metabolic flux away from IAA production. Considering that IAA may enhance CD8<sup>+</sup> T-cell effector function<sup>[<xref ref-type="bibr" rid="B74">74</xref>,<xref ref-type="bibr" rid="B75">75</xref>]</sup>, its depletion by <italic>P. vulgatus</italic> may compromise the potency of ICB and serve as a mechanistic basis for immunotherapy resistance in HCC<sup>[<xref ref-type="bibr" rid="B72">72</xref>]</sup>.</p>
        <p>Microbiota-derived riboflavin (vitamin B<sub>2</sub>) metabolites represent a unique class of antigens which specifically modulate mucosal-associated invariant T (MAIT) cells, an innate-like T-cell population highly abundant in the human liver<sup>[<xref ref-type="bibr" rid="B76">76</xref>-<xref ref-type="bibr" rid="B78">78</xref>]</sup>. While tumor-infiltrating MAIT cells in HCC frequently exhibit an exhausted phenotype characterized by elevated PD-1/CTLA-4 expression and diminished effector function, leveraging microbial-derived signals offers a potential strategy to reverse this dysfunction<sup>[<xref ref-type="bibr" rid="B76">76</xref>,<xref ref-type="bibr" rid="B79">79</xref>]</sup>. Commensal bacteria from the Bacteroidetes, Proteobacteria, and Fusobacteria phyla provide the requisite metabolic precursor, 5-OP-RU. Upon MR1-mediated presentation and co-stimulation with the Toll-like receptor 9 (TLR9) agonist CpG, this microbial antigen robustly expands MAIT cells and restores their interferon-γ and granzyme B production. These findings suggest that the riboflavin biosynthetic capacity of gut microbiota may serve as a critical determinant of innate-like T-cell-mediated antitumor immunity in HCC<sup>[<xref ref-type="bibr" rid="B80">80</xref>]</sup>.</p>
      </sec>
      <sec id="sec4-3">
        <title>Gut microbiota-HCC cell crosstalk</title>
        <p>Gut microbes and their metabolites not only modulate immune cells but also directly affect HCC cells and the surrounding TME [<xref ref-type="fig" rid="fig1">Figure 1E</xref>].</p>
        <p>Microbiota-derived lipopolysaccharide (LPS) is a potent facilitator of tumor cell reprogramming following gut barrier dysfunction<sup>[<xref ref-type="bibr" rid="B81">81</xref>,<xref ref-type="bibr" rid="B82">82</xref>]</sup>. In HCC cells characterized by high TLR4 expression, translocated LPS potentially activates the nuclear factor kappa B-Snail signaling axis, thereby inducing epithelial-mesenchymal transition and enhancing metastatic potential<sup>[<xref ref-type="bibr" rid="B83">83</xref>]</sup>.</p>
        <p>Intrahepatic colonization by specific bacterial taxa provides a more direct mechanistic link between gut dysbiosis and hepatocarcinogenesis. For instance, <italic>Klebsiella pneumoniae</italic> and <italic>Catenibacterium mitsuokai</italic> can breach the compromised mucosal barrier to colonize the liver<sup>[<xref ref-type="bibr" rid="B27">27</xref>,<xref ref-type="bibr" rid="B84">84</xref>]</sup>. Once established within the liver, these microbes utilize distinct surface proteins to activate tumor-intrinsic signaling pathways. The <italic>K. pneumoniae</italic> protein, PBP1B, potentially activates TLR4, whereas <italic>C. mitsuokai</italic> employs Gtr1/RagA to interact with γ-catenin on HCC cells. Both mechanisms may promote tumor cell proliferation and survival<sup>[<xref ref-type="bibr" rid="B27">27</xref>,<xref ref-type="bibr" rid="B84">84</xref>]</sup>. Notably, <italic>C. mitsuokai</italic>, which is consistently enriched in both the feces and tumors of patients with HCC, further amplifies oncogenic signaling by secreting quinolinic acid, an oncometabolite which triggers the TIE2-PI3K/AKT axis<sup>[<xref ref-type="bibr" rid="B84">84</xref>]</sup>.</p>
        <p>In summary, the gut microbiota-HCC crosstalk operates through both tumor-intrinsic and immune-mediated mechanisms, shaping disease progression and immunotherapy responsiveness. This complex interplay highlights gut microbes as potential predictive markers and therapeutic targets for improving treatment response.</p>
      </sec>
    </sec>
    <sec id="sec5">
      <title>GUT MICROBIOTA-TARGETED INTERVENTIONS IN HCC IMMUNOTHERAPY</title>
      <p>Targeting the gut microbiota has emerged as a promising strategy to enhance the efficacy of immunotherapy. This section reviews recent studies on the impact of targeted gut microbiota modulation on HCC immunotherapy outcomes, focusing on FMT, diet, antibiotics, and probiotics [<xref ref-type="fig" rid="fig2">Figure 2</xref>].</p>
      <fig id="fig2" position="float">
        <label>Figure 2</label>
        <caption>
          <p>Gut microbiota-targeted interventions to potentiate HCC immunotherapy. Created in BioRender. Hu, S. (2026) <uri xlink:href="https://BioRender.com/mqw7shd">https://BioRender.com/mqw7shd</uri>. HCC: Hepatocellular carcinoma; FMT: fecal microbiota transplantation.</p>
        </caption>
        <graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="hr12010.fig.2.jpg" />
      </fig>
      <sec id="sec5-1">
        <title>FMT</title>
        <p>Considering the distinct gut microbial signatures associated with responses to ICIs, FMT, which involves the transfer of stool from healthy individuals or ICI responders, typically via oral lyophilized/frozen capsules or endoscopic delivery, has emerged as a potential approach to enhance immunotherapy efficacy<sup>[<xref ref-type="bibr" rid="B85">85</xref>]</sup>. Accumulating clinical evidence from melanoma demonstrates that FMT can restore or potentiate responsiveness to anti-PD-1 therapy<sup>[<xref ref-type="bibr" rid="B17">17</xref>,<xref ref-type="bibr" rid="B86">86</xref>]</sup>. For instance, a phase I clinical trial (NCT03772899) evaluated the combination of FMT with anti-PD-1 treatment in advanced melanoma and reported an encouraging ORR of 65%, underscoring the therapeutic promise and clinical feasibility of microbiome modulation<sup>[<xref ref-type="bibr" rid="B18">18</xref>]</sup>.</p>
        <p>However, clinical data on FMT in HCC remain limited. A prospective clinical study evaluated the therapeutic impact of FMT combined with anti-PD-1 therapy in patients with unresectable or metastatic solid tumors. Six donors (four with HCC) were selected based on achieving DCB (complete response or partial response ≥ 6 months) from prior anti-PD-1 monotherapy. Thirteen recipients (four with HCC) had confirmed progressive disease despite ongoing PD-1-based treatment. All recipients received a 5-day course of amoxicillin-clavulanate before the first FMT to deplete the native microbiota, followed by colonoscopy-based FMT and continuation of standard-dose nivolumab until toxicity or further progression. Additional FMTs from the same or different donors were permitted when early clinical benefit was not observed. Among the 13 recipients, one patient achieved a partial response and five exhibited stable disease, resulting in an ORR of 7.7% and a disease control rate of 46.2%. Notably, the only partial responder was a patient with metastatic HCC who experienced substantial tumor regression after receiving FMT alongside continued nivolumab therapy<sup>[<xref ref-type="bibr" rid="B87">87</xref>]</sup>. Considering these preliminary findings, large-scale clinical trials are warranted to further evaluate the feasibility of FMT for enhancing ICI efficacy in patients with HCC and to elucidate the underlying mechanisms.</p>
      </sec>
      <sec id="sec5-2">
        <title>Diet</title>
        <p>Dietary patterns can shape the gut microbiome and, in turn, affect hepatocarcinogenesis and therapeutic responses<sup>[<xref ref-type="bibr" rid="B88">88</xref>,<xref ref-type="bibr" rid="B89">89</xref>]</sup>. Fermentable fiber-enriched refined diets, such as inulin, induce cholestatic HCC resembling human icteric HCC in Toll-like receptor 5-deficient mice, which are prone to gut microbiota dysbiosis. This tumorigenesis is associated with gut dysbiosis and elevated secondary BA levels, while SCFAs, particularly butyrate, may further promote HCC under conditions of inflammation and cholestasis<sup>[<xref ref-type="bibr" rid="B90">90</xref>]</sup>. In contrast, the ketogenic diet, characterized by high fat and very low carbohydrate and protein intake, can alleviate tumor-induced immunosuppression<sup>[<xref ref-type="bibr" rid="B91">91</xref>]</sup>. Mice fed the ketogenic diet exhibit markedly reduced subcutaneous HCC tumor growth than that in mice fed a normal diet<sup>[<xref ref-type="bibr" rid="B92">92</xref>]</sup>. Moreover, a study in murine HCC models revealed that short-term starvation (STS) can potentiate responsiveness to ICIs, with mice undergoing two treatment cycles, each consisting of three repeated fasting periods. Under this regimen, the combination of STS with anti-PD-L1 produces greater tumor suppression than that with anti-PD-1 or other monotherapies, and survival analyses further support the superior efficacy of this combination<sup>[<xref ref-type="bibr" rid="B93">93</xref>]</sup>.</p>
      </sec>
      <sec id="sec5-3">
        <title>Antibiotics</title>
        <p>Antibiotics can selectively eliminate gut bacteria, thereby potentially influencing immunotherapy outcomes. In 2021, two studies reported seemingly conflicting findings regarding the impact of antibiotics on the efficacy of ICIs in HCC. One retrospective cohort study from Hong Kong, including 395 patients with advanced HCC, found that the concurrent use of antibiotics during ICI therapy is associated with higher cancer-related and all-cause mortality<sup>[<xref ref-type="bibr" rid="B94">94</xref>]</sup>. In contrast, an international cohort study including 450 patients with HCC from Europe, North America, and Asia evaluated patients receiving either monotherapy or combination ICI. Early antibiotic exposure was defined as antibiotic use within 30 day before or after ICI initiation. This study found that early antibiotic exposure did not compromise OS or treatment efficacy and was even associated with prolonged PFS<sup>[<xref ref-type="bibr" rid="B95">95</xref>]</sup>. However, more recent studies published in 2023, including two large retrospective cohorts comprising 4,098 patients with unresectable HCC receiving ICI therapy<sup>[<xref ref-type="bibr" rid="B96">96</xref>]</sup> and 105 patients treated with atezolizumab plus bevacizumab<sup>[<xref ref-type="bibr" rid="B97">97</xref>]</sup>, consistently demonstrated that early antibiotic exposure is associated with reduced treatment efficacy, including shorter PFS and OS. The causal relationship between antibiotic use and outcomes in HCC patients is yet to be definitively established.</p>
      </sec>
      <sec id="sec5-4">
        <title>Probiotics</title>
        <p>Probiotics are defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host”<sup>[<xref ref-type="bibr" rid="B98">98</xref>]</sup>. Unlike FMT, transplantation of specific gut bacteria, alone or in combination, offers a more targeted and safer approach to modulate the gut microbiota in patients with HCC. In mouse models of MASLD-HCC, the administration of <italic>Lactobacillus acidophilus</italic><sup>[<xref ref-type="bibr" rid="B99">99</xref>]</sup> and <italic>Akkermansia muciniphila</italic><sup>[<xref ref-type="bibr" rid="B100">100</xref>]</sup> effectively suppresses hepatocarcinogenesis and substantially reduces tumor burden, respectively. Notably, the combination of <italic>Akkermansia muciniphila</italic> with anti-PD-1 therapy elicits a more robust antitumor response across multiple MASLD-HCC models<sup>[<xref ref-type="bibr" rid="B100">100</xref>]</sup>. In addition to antitumor efficacy, probiotics can potentially improve postoperative outcomes. In a clinical trial involving patients with HCC, the oral administration of a probiotic bacteria cocktail containing <italic>Bifidobacterium longum</italic> reduced the rates of delayed recovery, shortened hospital stays, and improved overall 1-year survival<sup>[<xref ref-type="bibr" rid="B101">101</xref>]</sup>. Furthermore, although PD-1 inhibitors exhibit antitumor activity, their use is often limited by immune-related adverse events (irAEs) such as colitis and hepatitis<sup>[<xref ref-type="bibr" rid="B102">102</xref>]</sup>. <italic>Lactobacillus rhamnosus</italic> modulates inflammatory signaling in both the gut and liver in HCC murine models<sup>[<xref ref-type="bibr" rid="B103">103</xref>]</sup>, suggesting its potential as a complementary approach to mitigate irAEs while enhancing immunotherapeutic efficacy.</p>
      </sec>
    </sec>
    <sec id="sec6">
      <title>CHALLENGES AND FUTURE PERSPECTIVES</title>
      <sec id="sec6-1">
        <title>Limitations of current microbial predictors of ICI response in HCC</title>
        <p>As summarized in <xref ref-type="table" rid="t1">Table 1</xref>, current microbial studies in HCC are skewed toward East Asian populations and primarily focus on taxonomic abundance and simple diversity metrics, providing a foundational but fragmented and geographically biased understanding regarding the microbial predictors of ICI response.</p>
        <table-wrap id="t1">
          <label>Table 1</label>
          <caption>
            <p>Summary of predictive gut microbial signatures for ICI efficacy in HCC cohorts</p>
          </caption>
          <table frame="hsides" rules="groups">
            <tbody>
              <tr>
                <td>
                  <bold>Patient characteristics</bold>
                </td>
                <td>
                  <bold>Location</bold>
                </td>
                <td>
                  <bold>Cohort size</bold>
                </td>
                <td>
                  <bold>Evaluation criteria</bold>
                </td>
                <td>
                  <bold>Sequencing</bold>
                </td>
                <td>
                  <bold>Predictive signatures</bold>
                </td>
                <td>
                  <bold>Remarks</bold>
                </td>
              </tr>
              <tr>
                <td>Unresectable HCC or advanced BTC (after failure of gemcitabine + cisplatin)<sup>[<xref ref-type="bibr" rid="B42">42</xref>]</sup></td>
                <td>Beijing, China</td>
                <td>65</td>
                <td>CBR: CR, PR, or SD ≥ 6 months (<italic>N</italic> = 17 HCC, <italic>N</italic> = 15 BTC); NCB: SD &lt; 6 months or PD (<italic>N</italic> = 13 HCC, <italic>N</italic> = 20 BTC)</td>
                <td>Metagenomics</td>
                <td>
                  <italic>Lachnospiraceae bacterium-GAM79, Alistipes</italic> sp <italic>Marseille-P5997, Ruminococcus calidus</italic> and <italic>Erysipelotichaceae bacterium-GAM147</italic> enriched in CBR; <italic>Veillonellaceae</italic> family enriched in NCB</td>
                <td>Higher diversity and <italic>Firmicutes</italic> abundance identified as potential protective factors against irAEs (e.g., diarrhea)</td>
              </tr>
              <tr>
                <td rowspan="2">HCC (BCLC Stage C; Sorafenib failure)<sup>[<xref ref-type="bibr" rid="B43">43</xref>]</sup></td>
                <td rowspan="2">Zhejiang, China</td>
                <td rowspan="2">8</td>
                <td rowspan="2">R: CR, PR, or SD &gt; 6 months (<italic>N</italic> = 3); NR: PD or SD &lt; 6 months (<italic>N</italic> = 5)</td>
                <td rowspan="2">Metagenomics</td>
                <td>Higher taxa richness in R</td>
                <td rowspan="2">Small sample size</td>
              </tr>
              <tr>
                <td>
                  <italic>Lactobacillus</italic> spp., <italic>Akkermansia muciniphila</italic> and <italic>Lachnospiraceae</italic> enriched in R; <italic>Escherichia coli</italic> enriched in NR</td>
              </tr>
              <tr>
                <td rowspan="2">HCC<sup>[<xref ref-type="bibr" rid="B44">44</xref>]</sup></td>
                <td rowspan="2">Guangdong, China</td>
                <td rowspan="2">35</td>
                <td rowspan="2">R: CR, PR, or SD ≥ 6 months (<italic>N</italic> = 16); NR: PD or SD &lt; 6 months (<italic>N</italic> = 19)</td>
                <td rowspan="2">Fecal 16S rRNA; Serum metabolomics</td>
                <td>Higher alpha diversity in R; distinct beta diversity clustering between R and NR</td>
                <td rowspan="2">AUC of 0.827-0.837 for predicting the R status</td>
              </tr>
              <tr>
                <td>
                  <italic>Faecalibacterium</italic>, <italic>Blautia</italic>, <italic>Lachnospiraceae incertae sedis</italic>, <italic>Megamonas</italic> and <italic>Ruminococcus</italic> enriched in R</td>
              </tr>
              <tr>
                <td rowspan="2">HCC (Sorafenib failure)<sup>[<xref ref-type="bibr" rid="B47">47</xref>]</sup></td>
                <td rowspan="2">South Korea</td>
                <td rowspan="2">8</td>
                <td rowspan="2">R: CR, PR, or SD &gt; 6 months (<italic>N</italic> = 5); NR: PD or SD &lt; 6 months (<italic>N</italic> = 3)</td>
                <td rowspan="2">Fecal 16S rRNA</td>
                <td>Higher taxa richness in Rs</td>
                <td rowspan="2" />
              </tr>
              <tr>
                <td>Proper <italic>Firmicutes</italic>/<italic>Bacteroidetes</italic> ratio (0.5-1.5) and higher <italic>Prevotella</italic>/<italic>Bacteroides</italic> ratio in R. <italic>Akkermansia</italic> species enriched in R</td>
              </tr>
              <tr>
                <td rowspan="2">HCC<sup>[<xref ref-type="bibr" rid="B48">48</xref>]</sup></td>
                <td rowspan="2">Taiwan, China</td>
                <td rowspan="2">41</td>
                <td rowspan="2">OR: CR or PR (<italic>N</italic> = 20); PD: <italic>N</italic> = 21</td>
                <td rowspan="2">Fecal 16S rRNA; Fecal BAs and SCFAs</td>
                <td>
                  <italic>Veillonella</italic>, <italic>Lachnospiraceae</italic>, and <italic>Lachnoclostridium</italic> enriched in OR; <italic>Prevotella 9</italic> in PD</td>
                <td rowspan="2">Co-existence of <italic>Lachnoclostridium</italic> enrichment and <italic>Prevotella 9</italic> depletion predicted better OS</td>
              </tr>
              <tr>
                <td>UDCA, TUDCA, UCA and MDCA enriched in OR</td>
              </tr>
              <tr>
                <td rowspan="2">V-HCC，MASLD-HCC<sup>[<xref ref-type="bibr" rid="B52">52</xref>]</sup></td>
                <td rowspan="2">Taiwan, China</td>
                <td rowspan="2">102</td>
                <td rowspan="2">DR: PR or SD ≥ 8 months (<italic>N</italic> = 32, V-HCC; <italic>N</italic> = 2, MASLD-HCC)</td>
                <td rowspan="2">Fecal 16S rRNA; Fecal BAs and SCFAs; Serum cytokines</td>
                <td>
                  <italic>Mediterraneibacter gnavus</italic> ATCC 29149 enriched in DR (MASLD-HCC); <italic>Bifidobacterium</italic> enriched in DR (v-HCC)</td>
                <td rowspan="2">Baseline metabolic levels significantly differ between V-HCC and MASLD-HCC etiologies</td>
              </tr>
              <tr>
                <td>Acetate enriched in DR</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn>
              <p>ATCC: American Type Culture Collection; AUC: area under the curve; BAs: bile acids; BCLC: Barcelona Clinic Liver Cancer; BTC: biliary tract cancer; CBR: clinical benefit response; CR: complete response; DR: durable response; HCC: hepatocellular carcinoma; ICI: immune checkpoint inhibitor; irAEs: immune-related adverse events; MASLD-HCC: metabolic dysfunction-associated steatotic liver disease-related hepatocellular carcinoma; MDCA: murideoxycholic acid; NCB: non-clinical benefit; NR: non-responder; OR: objective response; OS: overall survival; PD: progressive disease; PR: partial response; R: responder; SCFAs: short-chain fatty acids; SD: stable disease; TUDCA: tauroursodeoxycholic acid; UCA: ursocholic acid; UDCA: ursodeoxycholic acid; V-HCC: virus-related hepatocellular carcinoma.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>First, the restricted scale and observational nature of existing research remain primary hurdles<sup>[<xref ref-type="bibr" rid="B43">43</xref>,<xref ref-type="bibr" rid="B47">47</xref>]</sup>. With only one study incorporating an external validation cohort<sup>[<xref ref-type="bibr" rid="B48">48</xref>]</sup>, these inherent limitations increase the risk of overfitting and restrict the statistical power required to define robust biomarkers. Furthermore, the majority of the identified microbial-metabolite correlations lack mechanistic confirmation; therefore, further <italic>in vivo</italic> and <italic>in vitro</italic> validation experiments are needed.</p>
        <p>Second, the clinical landscape of HCC introduces etiological and therapeutic heterogeneity. For instance, the cohort of 35 patients involves mixed treatment backgrounds (e.g., combinations of ICI, TACE, and targeted therapies)<sup>[<xref ref-type="bibr" rid="B44">44</xref>]</sup>. Such therapeutic confounding, alongside the baseline divergence between V-HCC and MASLD-HCC etiologies<sup>[<xref ref-type="bibr" rid="B52">52</xref>]</sup>, suggests that many “responder” signatures are highly context-dependent. This is further exacerbated by the ability of concurrent agents, such as anti-CTLA-4, to modify gut barrier integrity and microbial compositions<sup>[<xref ref-type="bibr" rid="B104">104</xref>]</sup>.</p>
        <p>Third, technical discrepancies in protocols and sequencing methodologies hinder cross-cohort consistency. The reliance on 16S rRNA sequencing in several cohorts<sup>[<xref ref-type="bibr" rid="B44">44</xref>,<xref ref-type="bibr" rid="B47">47</xref>,<xref ref-type="bibr" rid="B48">48</xref>,<xref ref-type="bibr" rid="B52">52</xref>]</sup> introduces a “resolution gap” that fails to distinguish between closely related species or functional strains. The genus <italic>Veillonella</italic> epitomizes this fragility, serving as a positive predictor in some reports<sup>[<xref ref-type="bibr" rid="B47">47</xref>]</sup>, whereas other reports suggesting its enrichment in non-responders<sup>[<xref ref-type="bibr" rid="B42">42</xref>,<xref ref-type="bibr" rid="B43">43</xref>]</sup>. As highlighted by large-scale meta-analyses in melanoma, inherent population and microbiome heterogeneity make the identification of universal biomarkers exceptionally difficult, even with unified analytical pipelines<sup>[<xref ref-type="bibr" rid="B105">105</xref>]</sup>.</p>
        <p>Finally, robust predictive modeling for microbe-based ICI efficacy remains scarce. Here, only one study attempted to construct a predictive framework using a Random Forest model<sup>[<xref ref-type="bibr" rid="B44">44</xref>]</sup>. The development of such models faces significant technical hurdles, particularly the risk of overfitting due to the relatively small sample sizes in current HCC cohorts. Furthermore, the lack of cross-cohort generalizability, mainly driven by geographical and etiological heterogeneity, remains a critical barrier to clinical implementation. Transitioning toward multi-dimensional frameworks to capture the functional output of the tumor-immune-microbe axis remains a challenge.</p>
      </sec>
      <sec id="sec6-2">
        <title>Emerging microbial predictive signatures</title>
        <p>Accumulating research in other malignancy contexts has identified more sophisticated microbial signatures that hold predictive value for HCC immunotherapy.</p>
        <p>Microbial structural variations (SVs), comprising intra-species genomic deletions or insertions, may serve as a novel predictive dimension for immunotherapy efficacy. Using data from the gut microbiome of 996 patients from seven datasets, researchers have demonstrated that specific SVs are significantly associated with OR, PFS, and OS<sup>[<xref ref-type="bibr" rid="B106">106</xref>]</sup>. Crucially, these microbial SV associations are independent of taxonomic abundance. This shift toward genomic granularity is further supported by evidence that strain-resolved microbial abundances significantly improve machine learning-based predictions of ICB response and landmark PFS compared to species-rank quantifications or comprehensive clinical factors. Furthermore, meta-analyses across diverse cohorts have confirmed the cross-cancer and cross-country validity of these strain-response signatures, provided that the training and test cohorts utilize concordant ICB regimens<sup>[<xref ref-type="bibr" rid="B107">107</xref>]</sup>.</p>
        <p>Beyond microbial genomic markers, the characterization of immunotherapy sensitivity is increasingly focused on the ecological network. Enterotype classification provides a framework to capture individual microbiome heterogeneity, linking compositional patterns to immune phenotypes and therapeutic outcomes. The identification of favorable enterotypes with heightened microbial diversity signifies a potential biomarker for predicting treatment response in pan-cancer cohorts, since FMT from favorable-type donors enhances anti-PD-1 sensitivity and immune activation<sup>[<xref ref-type="bibr" rid="B108">108</xref>,<xref ref-type="bibr" rid="B109">109</xref>]</sup>. Consequently, emerging ecosystem-based strategies, such as TOPOSCORE, evaluate the stability and connectivity of microbial co-abundance networks. By identifying “guilds” or functional groups of cooperative bacteria, this approach reveals a balance between antagonistic sub-communities, defining a state of detrimental dysbiosis associated with immunotherapy resistance<sup>[<xref ref-type="bibr" rid="B110">110</xref>]</sup>. This topology-based scoring system has demonstrated predictive power for patient survival across multiple malignancies, moving the field from identifying “who is there” to characterizing “how they interact” within the tumor-immune-microbe axis.</p>
        <p>Moreover, the transition from associative descriptions to predictive systems is accelerated by integrative multi-omics modeling. For instance, in HBV-related HCC, integrating gut microbial signatures with associated BA profiles and host tumor transcripts provides mechanistic insights into how the gut microbiota contributes to tumor burden<sup>[<xref ref-type="bibr" rid="B111">111</xref>]</sup>. Such multi-dimensional predictive models, which incorporate strain-level variability, ecological topology, and host-microbe metabolic crosstalk, remain the frontier for establishing robust, cross-cohort microbial predictors in HCC.</p>
      </sec>
      <sec id="sec6-3">
        <title>Technical and methodological innovation</title>
        <p>While numerous studies have established correlations between certain taxa or microbial patterns and immunotherapy responsiveness, it remains unclear whether these signatures are causal drivers of anti-tumor immunity or simply predictive markers of host physiology. Emerging technologies are now bridging this mechanistic gap.</p>
        <p>Click chemistry enables selective labeling and enrichment of low-abundance microbial metabolites, allowing discrimination between microbiota-derived molecules and host-derived analogs which are otherwise indistinguishable by conventional metabolomics. This approach has facilitated the discovery of novel microbial BA derivatives, such as 3-succinylated cholic acid, and enabled functional characterization linking microbial metabolism to host immune regulation<sup>[<xref ref-type="bibr" rid="B66">66</xref>]</sup>.</p>
        <p>Serum antibodies can recognize both pathogens and commensal gut microbiota, yet the antigenic specificities of these responses remain incompletely characterized<sup>[<xref ref-type="bibr" rid="B112">112</xref>]</sup>. Phage Immunoprecipitation Sequencing is a high-throughput platform for profiling host antibody responses against microbial antigens at the epitope level<sup>[<xref ref-type="bibr" rid="B113">113</xref>]</sup>. These “immunological fingerprints” are longitudinally more stable than taxonomic compositions and enable the identification of systemic biomarkers and immunologically relevant microbial targets<sup>[<xref ref-type="bibr" rid="B114">114</xref>]</sup>.</p>
        <p>Moreover, the single-cell Bacteria Polygenic Score framework integrates microbial genome-wide association study with multi-organ human scRNA-seq data to map microbe-relevant genetic signals in the cellular context, yielding previously unperceived biological insights regarding involved cell types and biological pathways<sup>[<xref ref-type="bibr" rid="B115">115</xref>]</sup>. Complementing these novel technical and methodological improvements, large-scale multi-cohort integration is essential for validating microbial signatures across broader populations. For instance, performing <italic>de novo</italic> assembly across approximately 4,000 metagenomic samples from 38 studies spanning 15 diseases enables the recovery of high-quality metagenome-assembled genomes to define a stable “core microbiome signature”<sup>[<xref ref-type="bibr" rid="B116">116</xref>]</sup>. This interaction-focused analytical approach remains resilient to confounding factors without the need for conventional mathematical adjustments, providing a potent predictive backbone for immunotherapy outcomes across diverse global cohorts.</p>
      </sec>
      <sec id="sec6-4">
        <title>Clinical translation</title>
        <p>Despite growing evidence linking gut microbiota to immunotherapy outcomes, the clinical translation of microbiota research in HCC is still in its early stages, and HCC cohorts remain small and largely retrospective. FMT has been proposed as a potential strategy to overcome resistance to ICIs. However, adverse events related to FMT have been reported, with an incidence of 19% and serious adverse events accounting for approximately 1.4% of all cases<sup>[<xref ref-type="bibr" rid="B117">117</xref>]</sup>. Moreover, portal hypertension and gastrointestinal bleeding in advanced HCC also influence the gut-liver axis. A recent systematic review revealed that all FMT-related serious adverse events occur in patients with damage to the mucosal barrier<sup>[<xref ref-type="bibr" rid="B117">117</xref>]</sup>, a common feature in cirrhotic patients with HCC, which increases the risk of bacterial translocation and systemic infection. Furthermore, FMT acts as a “double-edged sword”; it can eradicate unfavorable microbial signatures but may inadvertently transmit procarcinogenic bacteria or harmful genetic elements that elude current donor screening protocols<sup>[<xref ref-type="bibr" rid="B118">118</xref>]</sup>. These risks necessitate more rigorous donor screening and longitudinal monitoring of intestinal barrier integrity.</p>
        <p>The success of FMT is influenced by a combination of donor- and recipient-related variables, including microbial diversity, immunomodulatory taxa, baseline dysbiosis, host immune tone, and genetic background. Procedural parameters, such as dosing, administration schedule, delivery route, and adjunctive antibiotic use, also play important roles<sup>[<xref ref-type="bibr" rid="B85">85</xref>]</sup>. As evidenced by the TACITO and PERFORM trials, recruitment and logistical challenges often lead to unintentional treatment delays. While relying on a single “super-donor” ensures consistency, it exacerbates supply shortages<sup>[<xref ref-type="bibr" rid="B119">119</xref>,<xref ref-type="bibr" rid="B120">120</xref>]</sup>. Moreover, recent Phase II trials such as FMT-LUMINate highlight the challenge of microbial engraftment, where the potential loss of transplanted bacteria from the intestinal tract remains a critical concern for functional stability<sup>[<xref ref-type="bibr" rid="B121">121</xref>]</sup>. These constraints underscore the necessity of transitioning toward standardized Live Biotherapeutic Products. Such next-generation interventions should be designed to offer a more predictable and scalable alternative to conventional FMT.</p>
        <p>The clinical impact of antibiotic exposure remains a contentious issue, which further complicates the translation of FMT. As discussed, the role of antibiotics as a “preconditioning” tool for FMT introduces a paradox. Systemic antibiotics (e.g., vancomycin and neomycin) can facilitate donor strain engraftment by reducing recipient microbial diversity to create a “niche,” whereas agents with minimal impact on diversity, such as rifaximin, often fail<sup>[<xref ref-type="bibr" rid="B122">122</xref>,<xref ref-type="bibr" rid="B123">123</xref>]</sup>. In HCC, identifying the optimal antibiotic class, dosage, and “window of vulnerability” is critical to balance the detrimental effects of dysbiosis against the necessity of microbial clearance for therapeutic colonization.</p>
      </sec>
    </sec>
    <sec id="sec7">
      <title>CONCLUSION</title>
      <p>The gut microbiota shapes the immune landscape of HCC and influences responses to ICIs. Community structures, specific taxa, and microbial molecules not only correlate with treatment outcomes but also offer potential targets for therapeutic modulation through FMT, diet, antibiotics, and probiotics. While challenges remain, advances in large-scale, multi-omics profiling, strain-level analysis, and innovative techniques hold promise for precision microbiota-informed immunotherapy. Harnessing the gut microbiota as both a biomarker and therapeutic adjunct may ultimately optimize the efficacy of immunotherapy, mitigate adverse events, and advance personalized treatment strategies in HCC.</p>
    </sec>
  </body>
  <back>
    <sec>
      <title>DECLARATIONS</title>
      <sec>
        <title>Acknowledgments</title>
        <p>The Graphical Abstract was created in BioRender. Hu, S. (2026) <uri xlink:href="https://BioRender.com/423wmn3">https://BioRender.com/423wmn3</uri>.</p>
      </sec>
      <sec>
        <title>Authors’ contributions</title>
        <p>Conceptualized the idea and planned the manuscript: Xia S, Yang Q, Liu Y, Yan A, Li S, Xu L, Guan H</p>
        <p>Wrote the initial draft: Xia S</p>
        <p>Revised, edited, and approved the final version: Xia S, Hu S, Xie Y, Liu Y</p>
      </sec>
      <sec>
        <title>Availability of data and materials</title>
        <p>Not applicable.</p>
      </sec>
      <sec>
        <title>AI and AI-assisted tools statement</title>
        <p>During the preparation of this manuscript, the AI tool ChatGPT (GPT-5.5) was used solely for language editing. The tool did not influence the study design, data collection, analysis, interpretation, or the scientific content of the work. All authors take full responsibility for the accuracy, integrity, and final content of the manuscript.</p>
      </sec>
      <sec>
        <title>Financial support and sponsorship</title>
        <p>This study was supported by the following grants: Guangdong Basic and Applied Basic Research Foundation (No. 2025B1515020059) and National Natural Science Foundation of China (No. 82570639; No. 82300623).</p>
      </sec>
      <sec>
        <title>Conflicts of interest</title>
        <p>All authors declared that there are no conflicts of interest.</p>
      </sec>
      <sec>
        <title>Ethical approval and consent to participate</title>
        <p>Not applicable.</p>
      </sec>
      <sec>
        <title>Consent for publication</title>
        <p>Not applicable.</p>
      </sec>
      <sec>
        <title>Copyright</title>
        <p>© The Author(s) 2026.</p>
      </sec>
    </sec>
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