Regular Article
Withdrawal: Serum DLAT Is a Potential Diagnostic Marker in AFP-Negative HCC
2024 Volume 47 Issue 12 Pages 2127-2137
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2024 Volume 47 Issue 12 Pages 2127-2137
The aim of this study was to analyze dihydrolipoyllysine-residue acetyltransferase (DLAT) expression and diagnostic ability in hepatocellular carcinoma (HCC), assess its role in HCC growth, and factors affecting it. We conducted bioinformatics analyses, examined DLAT expression and prognosis in pre-cancer, and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment studies while investigating its correlation with immunity. We also predicted regulatory factors, and detected DLAT in HCC cells using quantitative PCR (qPCR) and Western blotting, and in patient serum via enzyme-linked immunosorbent assay (ELISA). We found that DLAT was found to be upregulated in HCC and identified as an independent risk factor associated with immune cells. DLAT expression was regulated by hsa-miR-122-5p and the transcription factors zinc finger protein 148 (ZNF148), Myc-associated zinc-finger protein (MAZ), and Zinc finger and BTB domain-containing protein 12 (ZBTB12). Surprisingly, there was an increase in DLAT promoter methylation. The area under the receiver operating characteristic curves (AUCs) for serum DLAT in distinguishing HCC from healthy controls, high-risk individuals, and non-HCC cohorts were 0.905, 0.754, and 0.831, respectively. Combining DLAT with alpha-fetoprotein (AFP) improved diagnostic accuracy, with AUCs of 0.957, 0.819, 0.773, and 0.887 for the respective comparisons. Notably, serum DLAT was positive in 71.4% of AFP-negative HCC patients. And the transfection of hsa-miR-122-5p mimic could down regulate DLAT expression.
According to GLOBOCAN 2020 data,1) liver cancer is the sixth most prevalent cancer and the third leading cause of cancer-related mortality globally. In 2020, there were approximately 90000 new cases and 830000 deaths worldwide. Data from China’s National Cancer Center (NCC) shows that, as of 2016, the incidence of liver cancer in China was 388800 cases (288800 in males and 100000 in females), making it the fourth most common cancer. The mortality rate was 13.9%, with 336400 deaths, positioning liver cancer as the second leading cause of cancer-related death in China, after lung cancer.2) Despite significant research efforts, the five-year survival rate for hepatocellular carcinoma (HCC) patients remains low, at 18%, though it can exceed 70% with early diagnosis.3) Therefore, early detection is essential for timely and effective treatment. In that regard, there is a pressing need to identify new biomarkers and therapeutic targets for HCC.
Dihydrolipoyllysine-residue acetyltransferase (DLAT), as the E2 subunit of the pyruvate dehydrogenase complex (PDHC), was previously believed to facilitate the conversion of pyruvate into acetyl-CoA, a key substrate for the tricarboxylic acid cycle (TCA cycle), thus supporting TCA cycle metabolism.4) Consequently, one might expect DLAT to be downregulated in cancer under the Warburg effect hypothesis. However, research has demonstrated that DLAT is highly expressed in gastric, lung, and liver cancers and is significantly associated with overall survival.5–9) Moreover, DLAT plays a role in regulating the glycolytic and pentose phosphate pathways in lung cancer cells, as well as the Wnt/β-catenin and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathways in HCC cells, but does not appear to affect aerobic glycolysis.4,6,8) The mechanisms underlying DLAT upregulation in HCC and its potential as a diagnostic biomarker remain unclear.
In this study, we conducted a comprehensive analysis of DLAT in HCC, examining its expression, prognostic significance, regulatory mechanisms, pathway involvement, and diagnostic potential. We verified its expression levels in HCC through both cell lines and clinical serum samples, and assessed its diagnostic value.
We obtained serum samples from 72 patients at Zhongnan Hospital of Wuhan University between December 2021 and September 2022. The cohort comprised 26 HCC patients (24 males and 2 females, mean age 61.15 ± 8.38), 23 patients with hepatitis B and liver cirrhosis (14 males and 9 females, mean age 58.74 ± 10.52), and 23 healthy controls (11 males and 13 females, mean age 45.37 ± 15.01). Detailed patients’ characteristics is presented in Table 1. Inclusion criteria for HCC patients was a confirmed diagnosis of hepatocellular carcinoma with no history of preoperative chemotherapy or radiotherapy. The control group consisted of healthy individuals attending routine physical examinations at the hospital. The samples were centrifuged at 1000 × g and 4 °C for 10 min and stored at −80 °C for further analysis. We also collected clinical data, including gender, age, tobacco and alcohol use, diabetes, hypertension, and levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP). The Ethics Committee of Zhongnan Hospital of Wuhan University approved this study. Informed consent was obtained from all study participants.
| Characteristics | HCC (26) | High-risk (23) | Health (23) | p-Value |
|---|---|---|---|---|
| Sex (%) | 0.0001b) | |||
| Men | 24 (92.3%) | 14 (60.9%) | 11 (45.8%) | |
| Women | 2 (7.7%) | 9 (39.1%) | 13 (54.2%) | |
| Age | 61.15 ± 8.38 | 58.74 ± 10.52 | 45.37 ± 15.01 | 0.005b) |
| Smoke | 0.0001b) | |||
| Yes | 9 (34.6%) | 6 (26.1%) | ||
| No | 17 (65.4%) | 17 (73.9%) | ||
| Drink | 0.0001b) | |||
| Yes | 3 (11.5%) | 8 (34.8%) | ||
| No | 23 (88.5%) | 15 (65.2%) | ||
| Hypertension | 0.0001b) | |||
| Yes | 8 (30.8%) | 4 (17.4) | ||
| No | 18 (69.2%) | 19 (82.6%) | ||
| Diabetes | 0.0001a) | |||
| Yes | 5 (19.2%) | 5 (21.7%) | ||
| No | 21 (80.8%) | 18 (78.3%) | ||
| ALT (U/L) | 36 (22.75,89.25) | 31 (19,54) | 23 (15,28) | 0.0098d) |
| AST (U/L) | 61.5 (34.75,102.5) | 47 (27,77) | 23 (19.25,26) | 0.0001d) |
| ALB (U/L) | 34.18 (6.29) | 80 (29,154) | 27 (17,37) | 0.0001c) |
| GGT (U/L) | 151.5 (54.75,277.8) | 112.13 (141.686) | 32 (18.5,45) | 0.0001d) |
| ALP (U/L) | 121.5 (84,209.8) | 102 (67,142) | 73.5 (56.25,84.5) | 0.0002d) |
a) Chi-square test, b) Fisher’exact probability method, c) One-way ANOVA, d) Kruskal–Wallis test.
This study was approved by Medical Ethics Committee of Zhongnan Hospital, Wuhan University, Approval Number: 2021141.
Bioinformatics AnalysisFirst, we assessed and validated DLAT RNA expression levels in HCC using data from the TCGA (https://portal.gdc.cancer.gov/) and GEO (http://www.ncbi.nlm.nih.gov/geo/), and demonstrated DLAT protein expression levels using immunohistochemistry results from THPA (https://www.proteinatlas.org/) databases. Subsequently, we explored the association between DLAT expression and overall survival as well as pathological stage using GEPIA (http://gepia.cancer-pku.cn/). The relationship between DLAT expression and immune cell infiltration in HCC, along with immune checkpoint expression levels, was further investigated using TIMER (https://cistrome.shinyapps.io/timer/) and TCGA data, with additional immune checkpoint data sourced from TISIDB (http://cis.hku.hk/TISIDB/index.php). Differential gene expression between the top 25% and bottom 25% of HCC patients by DLAT levels was analyzed through the TCGA database (criteria: |logFC| > 2, p < 0.05), and upregulated genes in the top 25% were subjected to GO and KEGG enrichment analysis. We also examined DLAT promoter methylation using the DNMIVD (http://119.3.41.228/dnmivd/index/) database. MiRNAs targeting the 3′ UTR of DLAT mRNA were predicted using StarBase (filter: programs ≥3, single cancer type), and DLAT transcription factors were identified using UCSC (https://genome.ucsc.edu/) and JASPAR (https://jaspar.genereg.net/) (filter: Score ≥500). Finally, we validated the correlations between DLAT and the expression levels of the identified miRNAs and transcription factors.
Analysis of Serum DLATDouble-antibody sandwich enzyme-linked immunosorbent assay (ELISA) was used to detect DLAT in human serum. The ELISA kit was obtained from Wenzhou Kemiao Biotechnology Co., Ltd. (Wenzhou, China), and the experiment was performed strictly according to the instructions.
Cell CultureHCC cell lines (HepG2, Hep3B, and Huh-7), liver-derived cell line expressing the hepatitis B virus genome (HepAD38) were obtained from the American Type Culture Collection (Manassas, VA, U.S.A.). HepAD38 and Huh7 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin; HepG2 and Hep3B cells were cultured in MEM supplemented with 10% FBS and 1% penicillin–streptomycin, with Hep3B requiring additional 1% L-glutamine, 1% sodium pyruvate, and 1% non-essential amino acids. All cells were maintained at 37 °C in a 5% CO2 incubator.
Mimic TransfectionOne hour before transfection, the medium was changed to OptiMem (Thermo Fisher Scientific, Waltham, MA, U.S.A.) supplemented with 10% FBS. The small interfering RNA (siRNA) duplexes were diluted in DMEM to a concentration of 100 pmol, and lipofectamine RNAiMAX (Thermo Fisher Scientific, Waltham, MA, U.S.A.) was added according to the manufacturer’s instructions. Next, 48 h after mimic transfection cells were collected and processed for RNA/protein isolation for further RNA sequencing and Western blotting.
Western BlotTotal cellular proteins were extracted using radio immunoprecipitation assay (RIPA) lysis buffer (Cat: P0013; Beyotime, Shanghai, China) with a protease inhibitor phenylmethylsulfonyl fluoride (PMSF); (Cat: G2008; Thermo Fisher Scientific). Protein concentration was determined using the bicinchoninic acid (BCA) assay kit (Cat: Poo10; Beyotime, Shanghai, China). Equal amounts (15 µg) of protein were loaded onto an 8% sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE) gel, separated by electrophoresis, and transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Sigma, U.S.A.). The membranes were blocked with 5% skimmed milk, and then incubated overnight with anti-DLAT polyclonal antibody (Cat#: 13426-1-AP, SanYing Biotechnology Co., Ltd., Wuhan, China), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Cat#: ab263962, Abcam, U.K.; dilution: 1 : 5000). The blots were then incubated with HRP secondary antibodies (Cat: AS1106; dilution: 1: 6000, Aspen, China) for 2 h at room temperature and the bands visualized using enhanced chemiluminescence detection system and a Chemidoc XRS imager system (Bio-Rad Laboratories, U.S.A.).
RNA Isolation and Quantitative RT-PCRTotal RNA was extracted from the cultured cells using TRIzol (Invitrogen, Carlsbad, CA, U.S.A.) according to the manufacturer’s instructions. RNA concentration was determined using the Nano-drop 2000 spectrophotometer (Thermo Fisher Scientific), and then reverse transcribed into cDNA using HiScript Q RT SuperMix (Cat#: R123, Vazyme, Nanjing, China). All the cDNA samples were kept at −80 °C prior to qPCR analysis. Quantitative real-time PCR was then performed using ChamQ SYBR qPCR Master Mix (Cat#: Q311, Vazyme) according to the manufacturer’s instructions, on the Bio-Rad CFX PCR machine. A 10-µL total reaction volume was used. The cycling conditions were as follows: Initial denaturation at 95 °C for 10 min; 40 cycles of amplification at 95 °C for 10 s; annealing at 60 °C for 30 s, elongation at 72 °C for 30 s. The PCR reaction system is described in Table 2, and primers sequences are listed in Table 3. The target gene expression analysis was evaluated by the 2−ΔΔCt method.
| Component | Volume |
|---|---|
| 2× ChamQ SYBR qPCR Master Mix | 5 µL |
| Forward primer (1 µM) | 0.5 µL |
| Reverse primer (1 µM) | 0.5 µL |
| cDNA | 1 µL (1 µg) |
| RNase-free water | Add to 10.0 µL |
| Gene | Sequence | |
|---|---|---|
| GAPDH | F | CAATGACCCCTTCATTGACC |
| R | TTGATTTTGGAGGGATCTCG | |
| DLAT | F | ACCAAAGCAAGAGAGGGTAAACT |
| R | AGACATCATGCTAGCCACATCAA |
Normally distributed data were summarized as mean ± standard deviation (S.D.) and skewed data as median and inter-quartile ranges. Student’s t test was used for continuous variables, while Pearson’s Chi square test was used for categorical variables. Multiple groups of variables were analyzed using one-way ANOVA. To evaluate the diagnostic value of DLAT, receiver operating characteristic (ROC) curves were made, and the area under the ROC curves determined. Image J software was used to quantitate the western blots. Statistical data analysis was conducted in SPSS 26.0 (SPSS, Chicago, IL, U.S.A.) and Graph Pad Prism 8.0.1 (GraphPad software, La Jolla, CA, U.S.A.). All statistical tests were two-sided and significance level set at p < 0.05.
From bioinformatics analysis, assessment of pan-cancer expression of DLAT revealed that it is significantly upregulated in cholangiocarcinoma (CHOL), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and stomach adenocarcinoma (STAD) tissues, and down-regulated in head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and thyroid cancer (THCA) tissues (Figs. 1A, B). Correlation analysis of its expression and patents’ survival showed that that high DLAT expression was positively correlated with poor survival and prognosis of patients with LIHC, and was negatively correlated with poor survival and prognosis of patients with KIRC (Figs. 2A–H), but had no correlation with others (Supplementary 1). This finding meant that DLAT significantly influences LIHC and KIRC stating (Figs. 2I, J). To further validate our findings, we also referred to one dataset from the GEO database (GSE36376). The mRNA expression level of DLAT in LIHC tissues was significantly higher than that in normal tissues (p < 0.05) (Fig. 2K). In order to confirm the higher expression of DLAT in LIHC tissue at protein level compared to normal liver tissue, we used immunohistochemical results from the HPA database and collected clinical samples for immunohistochemistry experiments. The results showed that the protein expression level in LIHC tissue was significantly higher than in normal liver tissue. Typical microphotographs of IHC are shown in Fig. 2L. Finally, univariate and multivariate Cox risk regression analyses confirmed DLAT as an independent risk factor for LIHC (hazard ratio: 1.367 (1.091–1.712)) (Table 4).
DLAT expression in pan-cancer (A, B).
The relationship between DLAT and overall survival rate of patients with bile duct cancer (A), liver cancer (B), lung adenocarcinoma (C), lung squamous cell carcinoma (D), stomach cancer (E), head and neck cancer (F), renal clear cell carcinoma (G), thyroid cancer (H); the relationship between DLAT and stage of patients with liver cancer (I), renal clear cell carcinoma (J); validation of DLAT expression pattern in the GEO database dataset GSE36376 (K); representative IHC micrographs of DLAT in liver samples and LIHC from the HPA database (L). ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
| Characteristics | N | Single-factor | Multi-factor | ||
|---|---|---|---|---|---|
| HR (95% CI) | p-Value | HR (95% CI) | p-Value | ||
| DLAT | 373 | 1.497 (1.214–1.847) | < 0.001 | 1.367 (1.091–1.712) | 0.007 |
| Stage | |||||
| I&II | 259 | ||||
| III&IV | 90 | 2.504 (1.727–3.631) | < 0.001 | 2.331 (1.601–3.393) | < 0.001 |
| Sex | |||||
| Women | 121 | ||||
| Men | 252 | 0.793 (0.557–1.130) | 0.2 | ||
| AFP (ng/mL) | |||||
| ≦400 | 215 | ||||
| >400 | 64 | 1.075 (0.658–1.759) | 0.772 | ||
| Albumin (g/dL) | |||||
| <3.5 | 69 | ||||
| ≧3.5 | 230 | 0.897 (0.549–1.464) | 0.662 | ||
The expression level of DLAT in LIHC was positively correlated with immune infiltration by B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. The correlation was especially marked in macrophages, neutrophils and dendritic cells (r: 0.277, 0.304 and 0.382, respectively), all p < 0.05 (Figs. 3A–F). DLAT was also positively correlated with immune checkpoints BTLA, CD160, CD244, PD-L1(CD274), CD96, colony stimulating factor I receptor (CSF1R), hepatitis A virus cellular receptor 2 (HAVCR2), IL10, IL10RB, kinase domain-containing receptor (KDR), galectin-9 (LGALS9), programmed cell death 1 ligand 2 (PDCD1LG2), transforming growth factor beta 1 (TGFB1), transforming growth factor beta receptor 1 (TGFBR1), T cell immune receptor with immunoglobulin (Ig) and ITIM domains (TIGIT) and V-set domain containing T-cell activation inhibitor-1 (VTCN1), but it was negatively correlation with lymphocyte activation Gene-3 (LAG3) (Fig. 3G).
The correlation between DLAT and infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells is analyzed by TIMER platform (A–F); The heat map shows correlation between DLAT and immune checkpoints expression level analyzed by TCGA database (G).
In the high DLAT expression LIHC group, 1,692 genes were up-regulated, while 41 genes were down-regulated compared to the low DLAT expression group. GO analysis revealed that the up-regulated genes were predominantly associated with cadherin and GTPase binding (biological processes), cell-substrate junctions, focal adhesion (cellular components), histone modification, and regulation of protein complex assembly (molecular functions) (Fig. 4A). KEGG analysis indicated that these genes were enriched in pathways related to genetic information processing, signal transduction (including PI3K/Akt, TGF-β, and HIF-1 pathways), cellular communities, transport and catabolism, and the immune system (Fig. 4B), but showed no significant enrichment in glucose metabolism pathways (Supplementary 2, p > 0.05).
The methylation level of the DLAT DNA promoter in LIHC tissues [0.0525 (0.0361, 0.1105)] was significantly higher compared to normal tissues [0.0494 (0.037, 0.066)] (Fig. 5A, p = 0.017). Using Starbase, we identified 12 miRNAs with potential binding sites at the 3′ UTR of DLAT mRNA (Table 1 of Supplementary 2). Given the regulatory role of miRNAs, their expression is expected to be inversely correlated with DLAT mRNA levels. Further analysis revealed that hsa-miR-122-5p expression was reduced in LIHC [14.39 (13.7, 14.96) vs. 15.28 (14.96, 15.44), p < 0.001] and was positively associated with patient prognosis [HR = 0.63 (0.44, 0.91), p = 0.014] (Figs. 5B, C). Additionally, four transcription factors—MAZ, ZBTB12, ZNF148, and ZNF460—predicted to bind the DLAT promoter were positively correlated with DLAT expression (Figs. 5D–G). Among these, MAZ, ZBTB12, and ZNF148 were significantly upregulated in LIHC (Figs. 5H–J, p < 0.05), whereas ZNF460 was not (Fig. 5K).
Methylation level of DLAT DNA promoter (A); the expression level of hsa-miR-122-5p (B); survival curves of LIHC patients with different expression levels of hsa-miR-122-5p (C); correlation between MAZ and DLAT expression (D); correlation between ZBTB12 and DLAT expression (E); correlation between ZNF148 and DLAT expression(F); correlation between ZNF460 and DLAT expression (G); MAZ expression level in LIHC (H); ZBTB12 expression level in LIHC (I); expression level of ZNF148 in LIHC (J); expression level of ZNF460 in LIHC (K). ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001.
qPCR and Western blotting analysis revealed that DLAT mRNA were high expression in HepAD38 and Huh7 cells (Figs. 6A, B). ELISA analysis of clinical serum samples showed that DLAT protein levels were significantly increased in the HCC cohort (517.4 ± 46.61 ng/mL) compared to the healthy (117.6 ± 33.47 ng/mL) and high-risk cohorts (247.4 ± 56.26 ng/mL) (Fig. 6C, p = 0.0001, 0.0003, respectively). However, there was no significant difference between the healthy and high-risk cohorts (p = 0.13).
DLAT mRNA(A) and protein (B) expression of four distinct HCC cell lines; the serum levels of DLAT protein of different cohort (C); diagnostic value of serum DLAT protein (D); The mRNA level of DLAT in Huh7 cells transfected with hsa-miR-122-5p mimics was determined by real-time PCR(E); The protein level of DLAT was detected by Western blot (F). ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001.
The area under the receiver operating characteristic curve (AUC) for serum DLAT was 0.731 (95% CI: 0.5830–0.8789, p = 0.0067) when comparing the healthy and high-risk cohorts, 0.905 (95% CI: 0.8227–0.9882, p < 0.0001) between the healthy and HCC cohorts, 0.754 (95% CI: 0.6142–0.8941, p = 0.0023) between the high-risk and HCC cohorts, and 0.831 (95% CI: 0.7316–0.9312, p < 0.0001) comparing the non-HCC (healthy and high-risk) and HCC cohorts (Fig. 6D). As shown in Table 5, combining DLAT with AFP improved diagnostic performance with AUCs of 0.773 (95% CI: 0.636–0.909), 0.957 (95% CI: 0.898–1), 0.819 (95% CI: 0.693–0.946), and 0.887 (95% CI: 0.802–0.972) for the respective cohort comparisons, outperforming DLAT alone. Additionally, as outlined in Table 6, serum DLAT positivity in AFP-negative HCC patients was 71.4% (5/7), regardless of the cutoff values of 121.3 ng/mL or 137.5 ng/mL.
| Cut-off | AUC (95% CI) | Sen. | Sep. | p-Value | |
|---|---|---|---|---|---|
| Health vs. high-risk | |||||
| DLAT | > 69.16 ng/mL | 0.731 (0.583–0.879) | 0.739 | 0.750 | 0.0067 |
| DLAT + AFP | 0.773 (0.636–0.909) | 0.783 | 0.750 | 0.0001 | |
| Health vs. HCC | |||||
| DLAT | > 121.3 ng/mL | 0.905 (0.823–0.988) | 0.846 | 0.875 | 0.0001 |
| DLAT + AFP | 0.957 (0.898–1.000) | 1.000 | 0.885 | 0.0001 | |
| High-risk vs. HCC | |||||
| DLAT | > 137.5 ng/mL | 0.754 (0.614–0.894) | 0.846 | 0.696 | 0.0023 |
| DLAT + AFP | 0.819 (0.693–0.946) | 0.846 | 0.783 | 0.0001 | |
| Non-HCC vs. HCC | |||||
| DLAT | > 137.5 ng/mL | 0.831 (0.732–0.931) | 0.846 | 0.787 | 0.0001 |
| DLAT + AFP | 0.887 (0.802–0.972) | 0.885 | 0.872 | 0.0001 | |
| DLAT | AFP | ||
|---|---|---|---|
| + | − | Total | |
| + | 17 | 5 | 22 |
| − | 2 | 2 | 4 |
| Total | 19 | 7 | 26 |
Subsequently, in order to further verify the upstream regulatory factor hsa-miR-122-5p of DLAT, we transfected Huh7 with hsa-miR-122-5p mimics. The results showed that the mRNA expression levels of DLAT was decreased by nearly 60%and protein expression levels by 40% after transfection of miR-122-5p mimics (Figs. 6E, F). The results in this section demonstrated that miR-122-5p bound to DLAT mRNA 3‘UTR and downregulated its expression level.
This study demonstrates that DLAT is upregulated across various cancers, including liver, lung, and gastric cancers, and significantly impacts HCC clinical staging. DLAT also serves as an independent prognostic factor for HCC, aligning with findings from previous research.7,10) Additionally, its expression is linked to lymph node metastasis, vascular infiltration, and recurrence. Recently report has shown that DLAT in tumor tissues of hepatitis B virus (HBV)-HCC patients is upregulated, and the expression level of DLAT in Huh7 significantly increases after being infected with HBV, which also verifies our result that DLAT is highly expressed in HBV replicating cell-HepAD38.11,12) However, there are no mechanism studies on how HBV regulates DLAT expression. Additionally, we found that there is other DLAT band around 100 kD in HepAD38 cells. Currently, the known modifications of DLAT include acetylation,13) phosphorylation14) and lipoylation,15) meanwhile, the bands of disulfide bond-dependent DLAT oligomers are around 200 kD.15) We hypothesized that HBV infection leads to increased expression of DLAT and modification of the protein, but what type of modification is still under investigation. DLAT knockdown suppresses tumor cell proliferation and is associated with reduced tumor progression.5–8) These findings underscore the critical role that DLAT plays in the onset, progression, and prognosis of HCC. Additionally, we found that in CHOL, DLAT is upregulated but higher expression seems to be associated with better outcomes in Figs. 1 and 2. Cholangitis is a risk factor for cholangiocarcinoma.16) Existing research has shown that in patients with primary biliary cirrhosis, the distribution of DLAT on the bile duct cell membrane surface is abnormally enhanced, resulting in an increase in the level of serum anti-DLAT antibodies.17) By the same token, we speculate that the same is true for DLAT in the bile duct cancer cells of some CHOL patients, and triggering an anti-tumor cell immune response mediated by anti-DLAT-related immunity, which may be related to the good prognosis of CHOL patients with high-expression DLAT.
Tumor immunity is crucial for tumor development and prognosis. Our findings indicate that DLAT expression is positively associated with neutrophils, macrophages, dendritic cells, and various immune checkpoints, including PD-L1. Tumor-associated macrophages (TAMs) are a predominant type of tumor-infiltrating immune cells, classified into M1 and M2 subtypes with contrasting functions: M1 macrophages typically exhibit anti-tumor activities, such as mediating cytotoxicity and antibody-dependent cell-mediated cytotoxicity (ADCC) to eliminate tumor cells, whereas M2 macrophages promote tumor growth, metastasis, inhibit T cell-mediated immune responses, foster angiogenesis, and contribute to tumor progression.18) Neutrophils, a significant component of the immune infiltrate in HCC, facilitate tumor progression by suppressing anticancer immunity, enhancing tumor cell survival, invasiveness, metastasis, stimulating angiogenesis, and degrading the extracellular matrix.19) In contrast, dendritic cells are potent antigen-presenting cells that phagocytose and cross-present tumor-associated antigens to CD8+ T cells and NK cells, recruiting them into the tumor microenvironment (TME).20) Research on the molecular mechanisms of DLAT in tumors is limited. Functional analysis indicates that DLAT is involved in histone modification but not in glucose metabolism pathways. As the E2 subunit of the pyruvate dehydrogenase complex (PDHC), DLAT is traditionally known for its role in glucose metabolism, suggesting that GO and KEGG analyses do not capture its full range of functions. Therefore, further studies on DLAT should focus on metabolomics or proteomics. Histone acetylation facilitates gene transcription,21) and acetylation is crucial for metabolic regulation, as seen with the acetylation of the mTOR1 complex.22) Evidence shows that DLAT participates in acetylation of glucose 6-phosphate dehydrogenase (6PGD) and histone acetylation,4,23,24) thus DLAT may plays an important role in gene transcription and metabolic regulation. Prior studies have shown that DLAT enhances glycolysis and pentose phosphate pathways in lung cancer cells, supporting cancer cell proliferation and tumor growth.4,6) Interestingly, HCC with high DLAT expression is predicted to be more responsive to sorafenib, a first-line treatment for patients with advanced Human HCC.10,25) Furthermore, cuproptosis inhibits autophagy through the DLAT/mammalian target of rapamycin (mTOR) pathway in prostate cancer, enhancing chemotherapy sensitivity to docetaxel26) These insights suggest that targeting DLAT may offer a novel therapeutic approach.
This study is the first time to measure DLAT protein levels in human HCC serum. According to Fig. 6C, it appears that the DLAT levels could be split into two groups within each cohort: high and low expressers, with HCC patients showing a higher proportion of high expressers. We divided HCC group into two groups according to the expression of DLAT, the low DLAT–HCC group and the high DLAT–HCC group, and analyzed the baseline data of these two groups. As shown in Table 2 of Supplementary 2. There were no significant differences in patient information (AFP, Sex, Age, Smoke, Drink, HBV, Diabetes, Hypertension, ALB, ALP, ALT, AST, GGT) between these two groups. There may be several reasons for these results: 1. operational error; 2. insufficient sample size.
While AFP is the still the main HCC biomarker in clinical use today, it has a low sensitivity with only 10–20% of early HCC patients exhibiting elevated AFP levels, while 12–25% of patients with chronic liver disease also show increased AFP, leading to misdiagnosis easily.27,28) Our study shows that DLAT is superior to AFP in the diagnosis of HCC. The combination of DLAT and AFP improve the diagnostic efficiency. Serum level of DLAT was elevated in up to 71.4% of AFP-negative HCC patients, suggesting that DLAT has a certain application value as a liquid biopsy marker in AFP-negative HCC patients.
Hsa-miR-122-5p and transcription factors ZNF148, MAZ, and ZBTB12 are predicted to regulate DLAT expression. We have confirmed that hsa-miR-122-5p is an upstream regulator of DLAT. Studies have shown that hsa-miR-122-5p overexpresses in cancer cells, inhibits epithelial-mesenchymal transition and, enhances the diagnostic accuracy of HCC when combined with other miRNAs.29,30) ZNF148,31) MAZ32,33) and ZBTB1234) are highly expressed and play critical roles in HCC, with ZBTB12 notably promoting anaerobic glycolysis in HCC, potentially linked to DLAT transcription.34)
In summary, this study provides a comprehensive analysis of DLAT’s expression, prognostic significance, regulatory mechanisms, and role in HCC. Bioinformatics and sample validation revealed that DLAT is significantly overexpressed in HCC tissues and serum, indicating its potential as both a diagnostic and prognostic marker, as well as a possible therapeutic target for HCC. However, the study is limited by its small sample size and the lack of large experimental validation. Moreover, research on DLAT’s role in cancer is sparse, and the specific molecular mechanisms through which DLAT promotes cancer remain poorly understood.
We would like to thank Mr. Erick Thokerunga for his kind help in grammar correction.
This research was supported by Natural Science Foundation of Guangxi under Grant No. 2022GXNSFBA035515 and Self-funded research project of The Health Committee of Guangxi Zhuang Autonomous Region under Grant No. Z-A20220100.
Fangfang Huang mainly completed conceptualization, investigation, resources, writing—original draft, writing—original review & editing. Jindong Bai was responsible for resources, writing—original draft, writing—original review & editing. Limei Hu was responsible for writing—original draft, writing—original review & editing. Leping Ning was responsible for supervision, writing—original review & editing. Changliang Luo was responsible for writing—original review & editing, funding acquisition.
The authors declare no conflict of interest.
This article contains supplementary materials.
