Integrated and clinical validation of helicase-like transcription factor as a biomarker for hepatocellular carcinoma

Abstract

Immune checkpoint inhibitors (ICIs) have reshaped the treatment landscape for advanced cancers; however, a substantial proportion of patients fail to benefit from ICI therapy, and reliable predictive biomarkers remain limited. This study aimed to investigate the immunological relevance of helicase-like transcription factor (HLTF) and its potential therapeutic implications in hepatocellular carcinoma (HCC).

HLTF expression was elevated across multiple cancer types, with particularly high levels observed in HCC. Increased HLTF expression was significantly associated with advanced tumor stage, higher histological grade, TP53 mutations, and lymph node metastasis. Moreover, high HLTF expression correlated with poorer overall survival, disease-specific survival, and progression-free interval in patients with HCC, indicating its potential value as a prognostic biomarker. Consistent with data from the Human Protein Atlas, HLTF protein expression was 74.51% higher in HCC tissues than in adjacent non-tumor tissues, and a similar pattern was observed at the transcriptional level. Immune infiltration analysis revealed a negative association between HLTF expression and CD8⁺ T cell infiltration, despite notable HLTF enrichment within CD8⁺ T cells themselves. Clear differences in pDC/CD8⁺ T cells were observed between HLTF-high and HLTF-low subgroups, further supporting an inverse relationship between HLTF and CD8⁺ T cells in HCC TMA cohorts. In addition, we observed that tumor tissues from nonresponders displayed greater levels of HLTF compared to those from responders in our cohort.

Taken together, our study points out that HLTF acts as a critical marker that dwindles the effectiveness of ICIs in HCC sufferers. More trials with large numbers of patients are imperative to validate its application as a beneficial biomarker for ICIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-026-15652-9.

Introduction

In recent years, immunotherapy has emerged as a transformative technique, significantly changing the treatment paradigm for advanced-stage human malignancies across many tumor types [1–5]. This strategy is designed to stimulate the patient’s immune system, harnessing its inherent ability to target and eliminate tumor cells. The most extensively employed form of immunotherapy involves the utilization of immune checkpoint inhibitors (ICIs) to interfere with a way anti-cancer immune cells and immunological checkpoints expressed on malignant cells interact [6, 7]. As an immunosuppressive checkpoint mostly located on tumor cells, Programmed death-ligand 1 (PD-L1) attaches to its receptor, programmed cell death protein 1 (PD-1), which is on the surface of anti-tumor immune cells [8–10]. In this way, it is instrumental in facilitating tumor immune evasion. Lymphocytes, particularly CD8⁺ T cells and NK cells, play a crucial role in antitumor immunity, according to a tumor immunology paradigm [11, 12]. Therefore, it would be anticipated that a positive response to treatment would be predicted by the elevated occurrence of these lymphocytes, either at baseline or after ICI treatment [13]. Several studies have described how the levels of PD-1 on CD8⁺ T cells alters after ICI treatment, and its unexpected predictive benefit in ICI treatment [14–16]. Nonetheless, immunotherapy may also prove advantageous for a considerable proportion of patients exhibiting negative PD-L1 expression upon testing. Consequently, there is an urgent need in clinical practice for additional and alternative biomarkers that can accurately forecast immunotherapeutic responses.

Systemic treatments, such as tyrosine kinase inhibitors (TKIs) like sorafenib or Lenvatinib, are used as first-line treatment for half of HCC patients for long time [17, 18]. Nevertheless, medication resistance to sorafenib is becoming more prevalent [19]. New drugs are imminent. In 2020, the FDA authorized atezolizumab (anti-PD-L1) plus bevacizumab (anti-VEGF) as a first-line therapy for advanced HCC. Targeting to advanced HCC, the FDA authorized two drugs as a preliminary therapy. Nowadays, atezolizumab plus bevacizumab are widely utilized in clinical practice. This combination showed preferable overall as well as progression-free survival results than sorafenib in patients with unresectable hepatocellular carcinoma [20]. In 2023, compared with active surveillance, adjuvant atezolizumab and bevacizumab substantially prolonged recurrence-free survival (RFS) in patients with a high probability of disease recurrence [21]. Therefore, ICIs are now recommended as first-line treatments in HCC by clinical guidelines.

Helicase Like Transcription Factor (HLTF) is a gene encoding certain protein, for example, a member of the SWI/SNF family. Its protein includes a structure of a RING finger DNA binding motif and this gene is believed to be capable of changing the chromatin structure of certain genes to regulate transcription [22]. HLTF is an E3 ubiquitin ligase and DNA helicase that plays an essential role in template switching DNA synthesis. DNA replication can continue even if there is DNA damage by using the newly synthesized intact chain as a template [23]. HLTF was previously reported to be associated with various diseases including Schimke Immunoosseous Dysplasia, Xeroderma Pigmentosum and Fanconi Anemia. Recently, increasing studies discovered the correlations of HLTF with gastric cancer [24], colon cancer [25], and colorectal cancer [26]. For HCC, some studies noted the involvement of HLTF methylation in hepatocarcinogenesis [27]. However, the exact roles and underlying mechanism are still unknown.

In the current study, we aimed to figure out the immunological correlations of HLTF and its underlying clinical significance for HCC. We firstly checked the prognostic implications of HLTF in Liver Cancer. Then we assessed the correlation between HLTF and immune cell infiltration in HCC by utilizing ssGSEA datasets. To figure out the biological significance of HLTF in the progression of HCC, we analyzed the differential expression genes (DEGs) in subgroups of HLTF high and low expression from the TCGA dataset. Simultaneously, we explored the clinical implications of HLTF by measuring HLTF degree of expression in our local HCC cohort including 102 self-matched HCC tissues. Furthermore, the prognostic efficacy of HLTF for reactions to immunotherapy was confirmed through two separate clinical cohorts.

Overall, we demonstrated HLTF’s important role in adjusting anti-tumor immunity and noted it to be an exclusive and prospective marker to anticipate immunotherapeutic responses in various malignancies.

Materials and methods

Clinical cohorts

Tissues from 102 HCC patients who underwent surgery at the Affiliated Hospital of Nantong University (Nantong, Jiangsu, China) between January 2016 and March 2018 were collected, along with self-matched adjacent liver tissues. The ethical committee of Nantong University’s associated hospital authorized this study (2022-L092) corresponding to the principles of the Helsinki Declaration.

Between January 2022 and June 2025, about 10 paraffin-embedded HCC samples were obtained from the Affiliated Wuxi People’s Hospital of Nanjing Medical University. The expression levels of these samples were assessed through immunohistochemistry staining of HLTF and CD8⁺ T cells. We evaluated the therapeutic responses of each patient according to the RECIST 1.1 criterion, categorizing them into responders (PR response) and non-responders (SD response and PD response). Approval for the ethical collection of samples was obtained from the Clinical Research Eth Committee at Nanjing Medical University.

Expression investigations based on bioinformatics

The Oncomine and Timer databases were utilized to measure the expression values of HLTF across various tumor tissues. This study employed the 10 GEO series (GSE22058, GSE25097, GSE36376, GSE14520, GSE10143, GSE46444, GSE54236, GSE63698, GSE64041, GSE76427), along with data from the TCGA and ICGC databases, to investigate the expression of HLTF in hepatocellular carcinoma (HCC) and normal tissues. The UALCAN database was utilized to assess HLTF expression across each subgroup of the TCGA HCC cohort, divided by clinicopathological characteristics such as disease stage, tumor grade, lymphatic metastasis, and TP53 mutant status.

Correlation between HLTF and Immune Cell Infiltration in HCC

We utilized ssGSEA (GSVA package built-in algorithm) with R software (version:3.6.3) and R package: GSVA package to perform immune infiltration algorithm. Spearman correlational analysis was applied for data statistics. We collected RNAseq data of level 3 HTSeq-FPKM format in TCGA-LIHC project. RNAseq data of FPKM (Fregments Per Kilobase per Million) format was converted to TPM (transcripts per million reads) format and then performed log2 conversion.

The single-cell RNA sequence (scRNA-seq) analysis

The tumor immune single-cell hub (TISCH) serves as a single-cell RNA-seq data repository focused on the tumor microenvironment (TME). It provides single-cell annotations of various cell types, facilitating TME analysis across numerous cancer types. TISCH was implemented to analyse scRNA-seq data from the datasets GSE125449, GSE140228, GSE146115, GSE146409, GSE166635, GSE179795, and GSE98638.

qRT-PCR

Total RNA was isolated using TRIzol reagent (Thermo, USA). cDNA synthesis was performed via the ReverTra Ace qPCR RT Kit (Toyobo, Japan). The Fast SYBR Green Master Mix (Applied Biosystems Inc., MA, United States) was utilized for RT-qPCR based on the manufacturer’s guidelines. The settings for PCR were as follows: Polymerase activation occurs for 30 s at 95 °C, followed by 40 cycles consisting of 5 s at 95 °C and 30 s at 60 °C. GAPDH served as an indicator. The corresponding expression level of the target gene was determined using the 2-ΔΔCT method. All of the primer sequences are mentioned below. HLTF, F, GTTCCTTTTGGTGCAAACAATG; R, AACTGATCTGAAACCGCTTTTC. GAPDH, F, AGGTCGGTGTGAACGGATTTG; R, TGTAGACCATGTAGTTGAGGTCA.

Immunohistochemistry

Dewaxed and dehydrated formaldehyde-fixed and paraffin-embedded tissues were carried out using xylene and serially diluted ethanol, separately. Following treatment with hydrogen peroxide and blocking with BSA, the slides were incubated with HLTF primary antibodies (1:200, Santa Cruz) for 12 h at 4 °C. The slices were washed with TBST, followed by treatment with streptavidin-biotin complex, and finally stained with 3,3′-diaminobenzidine/hematoxylin.

Multiplex fluorescence immunohistochemistry

The TMAs and tissue slides described above were stained. This study employed primary antibodies anti-HLTF (1:200, Santa Cruz) and anti-CD8⁺ (Ready-to-use, Abcarta). Antibody staining was observed with DAPI (CST) and fixed in buffered glycerol. Images were viewed utilizing fluorescent signals from several lasers and collected using an optical and epifluorescence microscope. Two pathologists individually assessed all stained sections.

Statistical analysis

The experimental data was visualized via using GraphPad Prism 10.0. To compare various groups, an analysis of variance (ANOVA) with one-way analysis of variance was implemented. The student’s t-test was employed to evaluate distinctions between two subgroups. Survival analysis adopted the Kaplan-Meier method accompanied by a log-rank test. A P-value of less than 0.05 was deemed to be statistically noteworthy.

Results

Expression levels of HLTF in HCC

First of all, by means of Oncomine database, we compared HLTF mRNA expression across multiple cancer types with their corresponding normal tissues. As shown in Fig. 1A, HLTF was highly expressed in most of cancer types, including bladder cancer, brain cancer, cervical cancer, colorectal cancer, esophageal cancer, gastric cancer, head and neck, lymphoma, myeloma, sarcoma, liver cancer, and lung cancer. In contrast, reduced HLTF mRNA expression was observed in leukemia. Then we further analyzed its expression features in TIMER database. Similarly, HLTF expression was significantly augmented in various malignancies, of which HCC tissues showed remarkably higher expression of HLTF than those normal tissues (Fig. 1B). Subsequently, we focused on the expression characteristics of HLTF in multiple HCC-related datasets. In line with the above findings, HLTF expression was significantly elevated in HCC tissues relative to normal controls (Fig. 1C). Moreover, HLTF expression gradually increased during hepatocarcinogenesis, progressing from healthy liver to cirrhotic tissue and finally to HCC.

Fig. 1.

Verification of HLTF as a prognostic marker for HCC. (A) HLTF expression levels across multiple tumor tissue types within the Oncomine database. (B) The expression of HLTF throughout divers tumor tissues and para-carcinoma ones from the TCGA within the TIMER database. (C) HLTF expression in GEO, TCGA and ICGC databases. Chart and plot showing the expression features of HLTF in pre-cancerous tissues and HCC tissues. *, P < 0.05, **, P < 0.01, ***, P < 0.001

Correlation of HLTF expression with clinical parameters of HCC patients

We next evaluated HLTF expression across clinical subgroups of the TCGA HCC cohort by UALCAN. HLTF expression was significantly upregulated in HCC patients with advanced tumor stage or higher histological grade (Fig. 2A and B). In addition, HCC cases harboring TP53 mutations or exhibiting lymphatic metastasis showed higher HLTF expression levels (Fig. 2C and D). As summarized in Supplementary Table 1, elevated HLTF expression was also significantly correlated with Histological type (P=0.028). We further detected HLTF expression in a local HCC cohort consisting of 102 self-matched HCC tissues. The expression levels were similar to the shown ones in the Human Protein Atlas (http://www.proteinatlas.org/). Then we found that 76 of tissues were relatively positive. It displayed higher expression levels of HLTF in cancerous tissues than normal cases with a positive ratio of 74.51% (Fig. 2E). Moreover, increased HLTF transcript levels in HCC tissues were confirmed by qRT-PCR analysis (Fig. 2F). Taken together, these findings suggest that elevated HLTF expression is closely associated with HCC progression and may serve as a potential indicator of disease advancement.

Fig. 2.

HLTF was upregulated in human HCC tissues. The stratified based on disease stages, histologic grade, lymph node involvement, and TP53 alteration status in TCGA. The expression levels of HLTF based on (A) disease stages, (B) histologic grade, (C) lymph node involvement, (D) TP53 alteration status in the sub-groups were analyzed by UALCAN. (E) Typical immunohistochemistry analysis of HLTF in a local cohort of 102 self-matching hepatocellular carcinoma and adjacent non-cancerous tissues. (F) Expression level of HLTF in HCC tissues. *, P < 0.05, **, P < 0.01, ***, P < 0.001

Prognostic implications of HLTF in liver cancer

To evaluate the predictive performance of HLTF at different time periods, we conducted time-dependent analyses of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). HLTF showed favorable predictive accuracy for one- and three-year OS, DSS, and PFI (Fig. 3A). When compared with two commonly used biomarkers, alpha-fetoprotein (AFP) and glypican-3 (GPC3), HLTF demonstrated a larger area under the ROC curve (AUC), indicating superior predictive performance (Fig. 3B). Furthermore, patients with high HLTF expression had significantly worse OS, DSS, and PFI than those with low HLTF expression (Fig. 3C). Consistently, both univariate and multivariate Cox analyses identified HLTF as a strong predictor of survival outcomes in HCC (Supplementary Tables 2–4). Notably, HLTF served as an independent predictive indicator for progression-free interval in HCC patients. Thus, HLTF might be a potential predictor for OS, DSS and PFI of HCC patients.

Fig. 3.

HLTF is a prognostic marker of OS DSS and PFI in hepatocellular carcinoma(A) Time-dependent ROC analysis was performed to compare the predictive accuracy and risk score for overall survival (OS), disease specific survival (DSS), and progress free interval (PFI) of HCC patients. Time-dependent ROC analysis was conducted to evaluate the prediction veracity and risk score for overall survival, disease-specific survival, and progression-free interval in patients with HCC. (B) Time-dependent ROC analysis of overall survival, disease-specific survival, and progression-free interval to compare the distinctions among HLTF, AFP, along with GPC3. (C) Kaplan-Meier survival analytics of overall survival, disease-specific survival, and progression-free interval in HCC patients distinguished by upregulated and downregulated groups.

Enrichment of the GO and KEGG pathways

To figure out the biological significances of HLTF in progression of HCC, we compared the differential expression genes (DEGs) in HLTF high expression sub-group and HLTF low expression sub-group of TCGA dataset. And we then found that up-regulation genes accounted for 87.4% of total 231 DEGs (Fig. 4A and 4B). KEGG pathway analyses showed the DEGs were enriched in nearly 40 pathways, including cell cycle, Ierpenoid-quinone biosynthesis lyrosine metabolism, metabolism of xenobiotics by cytochrome P450, Drug metabolism-cytochrome P450 and Cholesterol metabolism. GO analysis indicated that HLTF were involved in DNA replication, sister chromatid segregation, organelle fission, nuclear division, mitotic nuclear division, mitosis chromosome segregation, acute-phase response and acute inflammatory response (Fig. 4C). It suggested that HLTF may contribute to HCC progression by regulating cell cycle–related processes, metabolic pathways, and inflammatory responses.

Fig. 4.

Possible regulatory pathways of HLTF(A)Volcano plots were used to reveal the distinctions in gene expression (DEGs) between patients with high and low HLTF levels. (B) Hierarchical clustering analysis of elevated expression of HLTF subgroup and decreased expression of HLTF subgroup. (C) GO and KEGG analyses were conducted to identify the possible regulatory pathways and processes.

The link between HLTF abundance and immune cell infiltration within TCGA-LIHC dataset

Besides, we paid attention to the connection between HLTF abundance and immune cell infiltration within the TCGA-LIHC dataset. ssGSEA was implemented to examine the link among HLTF expression levels and 24 distinct immune cell types. The expression of HLTF showed a notable positive correlation with Th2 cells, T helper cells, Tcm, and Eosinophils, whereas a negative correlation was observed with CD8⁺ T cells, pDC, Neutrophils, DC, and Cytotoxic cells (Fig. 5A and B). We isolated specific cell types (|R|>0.2 and P < 0.05) and conducted a comparative analysis of the cell enrichment scores across subgroups exhibiting increased and decreased expression levels of HLTF. According to Mann-Whitney U test, the distinctions among the HLTF-elevated and HLTF-reduced conditions were clearly noticed in pDC/CD8⁺ T cells (Fig. 5C). Furthermore, we investigated the expression features of HLTF within the TME via the analysis of HCC single-cell RNA sequencing data from the datasets GSE125449, GSE140228, GSE146115, GSE146409, GSE166635, GSE179795, and GSE98638. As shown in Fig. 5D, HLTF was highly enriched in immune cells, including T prolif cells, CD8⁺ T cells and Malignant cells. The outcomes indicate that HLTF could play a role in the immune infiltration associated with HCC.

Fig. 5.

The association between HLTF and immune cell infiltration (A) ssGSEA analysis revealed the correlations between infiltration levels of 24 immune cell types and HLTF expression patterns in a lollipop figure. (B) The cells with high relevance were demonstrated by the scatter plot. (C) Cell enrichment scores were evaluated in subgroups with over expressive and under expressive HLTF (|R|>0.2 and P<0.05). (D) TISCH database was utilized to illustrate the single-cell cluster map and dispersion of HLTF expression across disparate cell subtypes. *, P<0.05; **, P<0.01; ***, P<0.001.

Prognostic efficacy of HLTF for immunotherapy responses in-house cohorts

We further verified the prognostic efficacy of HLTF for immunotherapy responses in-house cohorts. Similarly, a microassay cohort of HCC tissues was designed to validate the association among CD8⁺ T cells and HLTF. Two independent cohorts of HCC immunotherapy were included (Fig. 6A and 6B). We investigated each patient’s treatment response in accordance with the RECIST 1.1 criterion and classified them as either responders (PR response) or nonresponders (SD response and PD response). In cohort 1, CD8⁺ T cells was strikingly expressed in tumor tissues from responders. Nonetheless, a scattering of HLTF’s expression was observed from responders, indicating a negative correlation with CD8⁺ T cells (Fig. 6A and 6C). In cohort 2, elevated HTLF expression was noted in nonresponders and downregulated CD8⁺ T cells was detected at the same time (Fig. 6B and 6D). The findings indicated that elevated levels of HLTF were found in tumor specimens from nonresponders compared to responders.

Fig. 6.

Inverse connection among HLTF and CD8+ T cells(A) (C)The expression of HLTF and CD8+ T cells in the group of responders (PR response). (B)(D) The expression of HLTF and CD8+ T cells in the group of noresponders(SD response and PD response). ***, P<0.001; ****, P<0.0001

Discussion

Immune checkpoint inhibitors have emerged as a potential supplementary strategy for advanced cancer treatment, with certain indicators utilized for treatment prediction now serving as new therapeutic targets. Previous investigations have demonstrated that tumor advancement in both sexes correlates with a dominant immunological pattern of CD8⁺ > CD4⁺, Th1 > Th17 > Th2 and M1 > M2 within the liver, implying that immunological pattern recognition elucidates the immunobiology of HCC and directs immune modulatory strategies for its therapy [28]. Clinical experience and genetic evidence indicate that immune checkpoint inhibitors may be effective in HCC [29]. As we know, HCC has been widely acknowledged as a polygenic and multi-process disease. As is known, various genes or proteins are evaluated as candidate biomarkers for the prognosis of HCC. HLTF are identified as a tumor suppressor in the thyroid and digestive carcinoma [24, 30–32], while serves as an oncogene in kidneys, hypopharynx and cervical cancer [33–35]. Nevertheless, it largely remains unknown for the roles of HLTF in HCC.

The current research examines the expression of HLTF and its prognostic implications across various cancer types via database analysis. Differences in the expression of HLTF were noted when comparing cancerous tissues to normal tissues across multiple kinds of malignancies. Our findings demonstrate that abundance levels of HLTF were significantly elevated in the majority of cancer types. Concurrently, mRNA expression levels of HLTF were found to be reduced in various cancers, including kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, as well as thyroid carcinoma. It convinced that HLTF probably has organ-specificity in current expression and potential functions.

Few reports and relevant studies investigated the roles of HLTF in HCC, while related members of its family had been discovered connections with HCC. RNF6, a member of Ring finger proteins family, was reported to have influence over HCC. Z-D Jin et al. hypothesized the predictive significance of RNF6 in nonalcoholic fatty liver disease and its progression to hepatocellular carcinoma [36]. HLTF, a member of the Ring finger protein family, may contribute to the development of hepatocellular carcinoma. Here, HLTF showed high levels of expression in hepatocellular carcinoma tissues in TCGA, ICGC, and several GEO datasets. Patients with abnormally high expression of HLTF may have worse predictive performance in OS, DSS, and PFI comparing with AFP and GPC3 groups. In light of these results, we reckoned HLTF as a potential biomarker for HCC prognosis. Furthermore, HCC is recognized as a progressive disease. Most liver cancers progress from cirrhosis. Interestingly, our study found that HLTF seemed to show a dynamic increase in the liver cancer stage, which can be used as a prospective biomarker and a possible target for liver cancer progression. Then we confirmed this bioinformatics analysis by immunohistochemistry and qRT-PCR in a local cohort. These experiments implicated that HLTF was highly expressed in liver cancer tissues and liver cancer cells. However, the exact roles and underlying mechanism on HLTF in HCC are still vague. GO and KEGG analysis were conducted to uncover potential regulatory pathways and processes. It suggested that HLTF might participate in HCC progression by modulating the activity of acute inflammatory response. Given that association, we focused on pathways that were related to this biological progress.

As we know, cancer is marked by inflammation, which contributes to tumor initiation and progression. In established cancers, local immune responses and systemic inflammation are increasingly associated with tumor progression and patient survival [37–39]. In HCC, the immune system is integral to the growth and proliferation of tumor cells. Liver cancer can attain immune escape via the tumor microenvironment, which plays a crucial role in the progression of the disease [40]. Acute inflammation is a highly organized defense system that the body starts when it is hurt from the outside (like from an infection or trauma). Its main job is to bring immune cells (i.e., immune infiltration) from the blood into damaged tissues to get rid of harmful substances and start the healing process [41]. Therefore, we inquired whether HLTF has any connection with inflammation and immune response, and accordingly reviewed the relevant literature. Prior studies investigated the association between HLTF and tumor immunity, indicating that HLTF expression is linked to diminished immunotherapy efficacy and poor prognoses in various cancer types [42, 43]. So, we assessed the association between HLTF abundance level and immune cell infiltration. We found that pDC, Neutrophils, CD8⁺ T cells, DC and Cytotoxic cells had negative correlation with HLTF expression. As we know, DC cells, which are considered as the most potent specialized antigen-presentation cells, are able to absorb in disposing and delivering antigens in order. It also has the ability to induce the generation of specific cytotoxic T lymphocytes, promote excitation of CD8⁺ T cells, and enhance anti-cancer immunity. Hence, HLTF may impair tumor immunity, support tumor cells from escaping, and eventually promote occurrence and process of carcinogenesis.

To further verify the prognostic efficacy of HLTF for immunotherapy responses, we conducted two in-house cohorts. In our cohorts, we observed higher expression of HLTF and lower expression of CD8⁺ T cells in tumor tissues from nonresponders rather than responders, as well as the reverse correlation between CD8⁺ T cells and HLTF. Meanwhile, the HCC TMA cohort revealed an inverse relationship among HLTF and the CD8⁺ T cells. As we know, CD8⁺ T cells plays a crucial role in antitumor immunity and would be anticipated to be a positive response to treatment. In other words, patients with worse-therapeutic responses were observed with remarkable increase of the expression of HLTF, whereas patients with favorable therapeutic outcomes had decreased HLTF expression. Herein, HLTF could be likely to take a toll on the clinical efficacy of conventional immunotherapy and make it difficult for patients to benefit from immunotherapy. Therefore, HLTF stands a good chance to be a biomarker for immunotherapy with unfavorable prognosis.

Previous investigation has established that HLTF modulates immunity via the AMPK-HLTF-CD137L signaling axis in melanoma [43]; but it remains uncertain if this mechanism diminishes the effectiveness of immunotherapy in other malignancies, such as HCC. According to this research, CD137L expression patterns can act as prognostic indications for patient responses to anti-PD-1 therapy. Nonetheless, it has to be confirmed if HLTF suppresses tumor immunity through this signaling mechanism. Consequently, a deeper investigation may be conducted in the future. We need more experiments to ascertain if HLTF similarly modulates the expression of CD137L or other costimulatory/inhibitory molecules is the fundamental mechanism in HCC models, examine whether HLTF modulates immune-related gene networks through the alteration of histone modification or DNA methylation. Besides AMPK agonists, alternative strategies focusing on HLTF or its upstream signals (such as metabolic pathways and DNA damage response pathways) are also need to be investigated to improve the effectiveness of HCC immunotherapy.

Nevertheless, the “negative correlation” in ssGSEA reflects the suppressed overall functional activity or cytotoxic state of CD8⁺ T cells, whereas the “high enrichment” in scRNA-seq merely indicates a high relative abundance or proportion of CD8⁺ T cells within the tumor immune infiltrate. These findings are not contradictory but rather point to a more profound biological phenomenon: in HCC with high HLTF expression, there may exist “dysfunctional infiltration of CD8⁺ T cells.” That is, despite the presence of a substantial number of CD8⁺ T cells, they are likely in an exhausted, anergic, or functionally suppressed state, rendering them ineffective in killing tumor cells [44].

In conclusion, based on the multitude analyses, HLTF might serve as a crucial marker that dwindles the effectiveness of ICIs in patients with HCC.

Our study only verified that HLTF regulates immune infiltration, but the precise mechanism of HLTF in immune regulation still needs to be further explored, which is one of the limitations of this study. In addition, after reviewing the recent literature, it was found that HLTF was indeed related to tumor immunity, but whether this effect was related to PD-1 and whether it could cooperate with PD-1 immunotherapy was not clear. Considering that our current study was based on a small-size cohort and preliminary function validations, the underlying mechanism should be investigated by further assays, experiments and study of large-scale patients.

Abbreviation

HLTF

helicase-like transcription factor

HCC

hepatocellular carcinoma

VEGF

vascular endothelial growth factor

DEGs

differential expression genes

TCGA-LIHC

The Cancer Genome Atlas Liver Hepatocellular Carcinoma

ICGC

International Cancer Genome Consortium

FPKM

Fregments Per Kilobase per Million

TPM

transcripts per million reads

GSEA

Gene set enrichment analysis

scRNA-seq

single-cell RNA sequence

TISCH

The tumor immune single-cell hub

TME

tumor microenvironment

DAB

diaminobenzidine

ANOVA

analysis of variance

GO

gene ontology

OS

overall survival

DSS

disease specific survival

PFI

progress free interval

RNF6

Ring Finger Protein6

NAFLD-HCC

nonalcoholic fatty liver disease

ICIs

Immune checkpoint inhibitors

PD-L1

Programmed death-ligand 1

TKIs

tyrosine kinase inhibitors

Authors’ contributions

Conceptualization: ZYN; Data Curation: ZYN, JPG; Investigation: ZYN, QYQ; Methodology: ZYN, YL; Visualization: ZYN; Validation: JPG; Formal analysis: ZYN; Supervision: ZT, GPL; Project administration: JM, JYY, LS; Software: JM, LS; Resources: JM, LS; Funding acquisition: JYY; Writing – original draft: ZYN; Writing – review and editing: all authors;

Funding

The research received funding via Top Talent Support Program for young and middle-aged people of Wuxi Health Committee (BJ2023011), Scientific Research Project of Jiangsu Health Commission (Z2023075) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (JX13514525).

Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information.

Declarations

Ethics approval and consent to participate

The ethical review was approved, and all participants were informed of consent obtained. This study involving human participants was conducted in accordance with the Declaration of Helsinki and approved by the ethics committee of the Nantong University (2022-L092) and the Clinical Research Ethics Committee at Nanjing Medical University (2023-KY23035).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.