Abstract

Aim

   This study aims to explore the dynamic tumor microenvironment of
   hepatocellular carcinoma (HCC) through deep transcriptomic analysis and
   to identify key regulatory genes, among which MRE11 was further
   validated for its immunomodulatory and prognostic significance.

Methods

   We performed Summary-data-based Mendelian Randomization (SMR) analysis
   to identify genes causally associated with HCC and intersected these
   with DNA damage repair (DDR) genes, leading to the identification of
   MRE11. A comprehensive evaluation of MRE11 expression in HCC was
   conducted using transcriptomic data analysis. We collected data from 92
   HCC patient samples and validated MRE11 expression differences in HCC
   tissues through qPCR, immunohistochemistry, and Western blotting.
   Publicly available single-cell RNA sequencing (scRNA-seq) data and
   spatial transcriptomics were utilized to explore MRE11’s dynamic
   mechanisms in the tumor microenvironment (TME) of both primary and
   post-immunotherapy cases. We also screened for differentially expressed
   genes and constructed a robust HCC prognosis model using 101
   machine-learning algorithms.

Results

   Our results demonstrated that high MRE11 expression is strongly
   associated with poor prognosis in HCC. In the primary TME, MRE11
   regulates immune responses, facilitating immune evasion. Single-cell
   analysis revealed significant tumor heterogeneity in MRE11
   high-expression groups, particularly in macrophages and malignant
   cells, where MRE11 regulates immune evasion and tumor progression via
   the cGAS-STING pathway and HGF-MET axis. Under immunotherapy, high
   MRE11 expression facilitated epithelial-mesenchymal transition (EMT)
   and extensive remodeling of the TME. Furthermore, MRE11 dynamically
   enhanced macrophage regulation, exhibiting immunosuppressive and
   tumor-invasive features. Finally, our prognostic model exhibited strong
   predictive accuracy across multiple datasets.

Conclusion

   High MRE11 expression is crucial in regulating the immune
   microenvironment in HCC, fostering immune evasion and driving tumor
   progression. MRE11 emerges as a promising biomarker for HCC diagnosis
   and a potential target for personalized immunotherapy.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12935-025-03931-7.

   Keywords: MRE11, Hepatocellular Carcinoma, Deep Transcriptomic Atlas,
   Tumor Micro-environment, Machine Learning

Introduction

   The product of the MRE11 gene is a critical component of the
   MRE11-RAD50-NBS1 (MRN) complex, primarily responsible for recognizing
   and repairing DNA double-strand breaks [[40]1]. Recently, MRE11 has
   attracted significant attention in various cancers [[41]2], with
   studies indicating its role in genomic stability and tumor development
   in breast cancer [[42]3], prostate cancer [[43]4], and ovarian cancer
   [[44]5]. However, the specific role of MRE11 in HCC remains
   inadequately explored. Existing literature indicates that MRE11 is
   highly expressed in HCC tissues and may be associated with tumor
   progression and patient prognosis. However, the precise molecular
   mechanisms by which it regulates the TME in liver cancer are still
   unclear [[45]6]. Further investigation into the role of MRE11 in HCC
   may reveal its potential as a novel therapeutic target.

   Beyond its role in DNA damage repair (DDR), MRE11 also modulates the
   immune microenvironment in cancer [[46]7, [47]8], particularly in HCC,
   where it may influence tumor progression by regulating immune cell
   infiltration and activity [[48]9]. The relationship between MRE11 and
   the cGAS-STING pathway has garnered considerable interest. The
   cGAS-STING signaling axis consists of cyclic GMP–AMP synthase (cGAS),
   which generates the second messenger cyclic GMP–AMP (cGAMP), and the
   cyclic GMP–AMP receptor stimulator of interferon genes (STING)
   [[49]10]. Recent studies suggest that MRE11 can promote tumorigenesis
   by releasing the inhibitory sequestration of cGAS by nucleosomes,
   thereby activating the cGAS-STING signaling pathway and innate immune
   response. This regulatory mechanism underscores the pivotal role of
   MRE11 in linking tumor biology with innate immune surveillance [[50]10,
   [51]11]. In HCC, MRE11 may reshape the tumor immune microenvironment by
   modulating the cGAS-STING pathway, potentially impacting immunotherapy
   outcomes [[52]12, [53]13]. Consequently, MRE11 plays a crucial role not
   only in DNA repair and maintaining genomic stability but also in
   regulating immune evasion and immune responses in HCC.

   Given the limited research on MRE11 in HCC, this study combines
   multi-layered bioinformatics analyses (including scRNA-seq, Bulk RNA
   sequencing, Spatial transcriptome analysis, and Mendelian
   randomization) with experimental validation to comprehensively
   investigate MRE11’s function and its dynamic role within the HCC tumor
   microenvironment. Additionally, we developed an HCC prognosis
   prediction model based on large-scale HCC datasets, employing a
   combination of 101 machine-learning algorithms. This model successfully
   identified robust potential prognostic indicators for predicting
   patient outcomes. Through detailed exploration of molecular mechanisms
   and the development of clinical prediction tools, MRE11 is poised to
   become a potential target for HCC diagnosis and personalized treatment.

Materials and methods

Patient sample collection and ethical approval

   This study was approved by the Ethics Committee of the General Hospital
   of the People’s Liberation Army of China (Approval No. S2016-098-02).
   Tumor and adjacent non-tumor liver tissue samples were collected from
   92 HCC patients, with 45 cases from the Fifth Medical Center and 47
   cases from the First Medical Center, respectively. Patient demographics
   and clinical characteristics are detailed in Table [54]1.

   Table 1. Baseline characteristics of the study population
   Characteristics MRE11 Low MRE11 High Total(N=92) P value
   Number 46(50%) 46(50%)
   Gender (Female/Male) 6(6.52%)/40(43.48%) 5(5.43%)/41(44.57%)
   11(11.96%)/81(88.04%) >0.05
   Age (Mean±SD) 55.39±10.36 56.54±12.08 55.97±11.20
   BMI (Mean±SD) 24.55±3.05 25.13±5.77 24.84±4.60
   HBV (No/Yes) 4(4.35%)/42(45.65%) 4(4.35%)/42(45.65%)
   8(8.70%)/84(91.30%) >0.05
   Anti-Hepatitis Treatment (No/Yes) 21(22.83%)/25(27.17%)
   22(23.91%)/24(26.09%) 43(46.74%)/49(53.26%) >0.05
   Underlying Disease (No/Yes) 38(41.30%)/8(8.70%) 27(29.35%)/19(20.65%)
   65(70.65%)/27(29.35%) 0.020
   Platelet count (10^9/L) (Mean±SD) 164.09±61.69 184.41±58.05
   174.25±60.44
   Albumin (g/dL) (Mean±SD) 40.70±3.94 42.65±6.67 41.67±5.54
   Bilirubin (mg/dL) (Mean±SD) 14.26±7.46 14.02±7.47 14.14±7.43
   ALT (g/L) (Mean±SD) 61.70±148.75 40.72±77.00 51.21±118.26
   AFP (ng/ml) (<400/>=400) 34(36.96%)/12(13.04%) 36(39.13%)/10(10.87%)
   70(76.09%)/22(23.91%) >0.05
   TNM Stage 0.043
   IA/IB/II 1(1.09%)/13(14.13%)/19(20.65%) 1(1.09%)/18(19.57%)/21(22.83%)
   2(2.17%)/31(33.70%)/40(43.48%)
   IIIA/IIIB/IV 3(3.26%)/7(7.61%)/3(3.26%) 0(0%)/6(6.52%)/0(0%)
   3(3.26%)/13(14.13%)/3(3.26%)
   Ki67(+) 0.058
   I/II/ 20(21.98%)/6(6.59%) 16(17.58%)/14(15.38%) 36(39.56%)/20(21.98%)
   III/IV 15(16.48%)/5(5.49%) 14(15.38%)/1(1.10%) 29(31.87%)/6(6.59%)
   Grade 0.053
   G1/G2/G3 7(7.61%)/33(35.87%)/6(6.52%) 2(2.17%)/31(33.70%)/13(14.13%)
   9(9.78%)/64(69.57%)/19(20.65%)
   Adjuvant Therapy >0.05
   Interventional/Targeted/None Therapy 5(5.43%)/0(0%)/34(36.96%)
   5(5.43%)/2(2.17%)/27(29.35%) 10(10.87%)/2(2.17%)/61(66.30%)
   Interventional + Targeted Therapy 1(1.09%) 2(2.17%) 3(3.26%)
   Interventional + Targeted + Anti-PD-1 2(2.17%) 2(2.17%) 4(4.35%)
   Targeted Therapy + Anti-PD-1 Therapy 4(4.35%) 8(8.70%) 12(13.04%)
   [55]Open in a new tab

   Data for bulk RNA-seq analysis were obtained from The Cancer Genome
   Atlas (TCGA), Genotype-Tissue Expression (GTEx), International Cancer
   Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases.
   Machine learning data included the raw mRNA expression profiles from
   the GEO dataset [56]GSE14520 [[57]14], TCGA dataset, and ICGC LIRI-JP
   cohort. The TCGA-LIHC dataset (n = 371) was used as the training set
   for model construction, while [58]GSE14520 (n = 221) and ICGC-LIRI-JP
   (n = 232) were used as external validation cohorts. For single-cell
   analysis, we utilized three external scRNA-seq datasets: [59]GSE149614
   [[60]15], [61]GSE151530 [[62]16], and [63]GSE125449 [[64]17].
   Additionally, spatial RNA transcript data were obtained from a previous
   publication [[65]18, [66]19].

Mendelian randomization analysis

   We performed Summary-data-based Mendelian Randomization (SMR) analysis
   using the SMR R package and Two-sample MR analysis using the
   TwoSampleMR R package. The exposure factor was the MRE11 gene (ID:
   prot-a-1939), and the outcome factors included HCC (ID: bbj-a-158) and
   p53/DNA damage-related proteins (ID: prot-a-2238). All data were
   sourced from the Integrative Epidemiology Unit (IEU) database. To
   ensure analytical precision, we filtered exposure data with a
   p-value < 1 × 10^−5and excluded linkage disequilibrium between
   instrumental variables (IVs) using an r^2 < 0.001 threshold. We further
   assessed potential confounders or risk factors for HCC to ensure the
   robustness of our results.

Diagnostic and prognostic analysis of MRE11

   Genes with potential causal association with HCC were screened out by
   SMR analysis, and MRE11A (P_HEIDI > 0.05, P < 0.05) that passed the
   heterogeneity test was retained as a robust candidate gene for
   downstream analysis after cross-validation with the DDR gene set
   [[67]20]. To evaluate the diagnostic potential of MRE11, we performed
   paired T-tests on HCC samples from the TCGA database and processed
   spatial transcriptomic data using the stCancer package. MRE11
   expression levels in tumor and adjacent non-tumor tissues were
   visualized using the HCCDB database (HCCDB: Integrative Molecular
   Database of Hepatocellular Carcinoma (lifeome.net)). Differences in
   expression across clinical stages were assessed using unpaired
   Student’s T-tests, and ANOVA was applied for comparisons between
   multiple groups. A nomogram was constructed using the rms package to
   assess the prognostic value of MRE11 in 104 samples, and the Receiver
   Operating Characteristic (ROC) function of the pROC package was
   employed to predict 1-, 3-, and 5-year survival rates.

Mutation, epigenetic modification, and regulatory network construction of
MRE11

   MRE11 mutations were analyzed using the Maftools package, and
   chi-square tests were employed to evaluate mutation frequency
   differences. The results were visualized via waterfall plots. We
   further analyzed the correlation between MRE11 and RNA modification
   genes, including m1A, m5C, and m6A genes. Copy number variation (CNV)
   data were obtained from the GDC database for TCGA samples, and
   integrated with gene expression data. Statistical significance was
   assessed using Wilcoxon rank-sum tests. DNA methylation analysis was
   conducted using the UALCAN database [[68]21]. Potential miRNA targets
   of MRE11 were predicted using four online databases (DIANA-microT
   [[69]22], miRcode [[70]23], miRWalk [[71]24], and miRDB [[72]25]), and
   miRNA-lncRNA interactions were analyzed on the StarBase 2.0 platform.
   Finally, the miRNA-lncRNA-MRE11 regulatory network was visualized using
   Cytoscape (v2.22.1) and networkD3 (v0.4.1) packages as a Sankey
   diagram.

Pathway enrichment, immune correlation, and drug sensitivity analysis

   Fifty MRE11-interacting proteins were extracted from the STRING
   database, and 100 MRE11-associated genes were obtained using the GEPIA2
   platform for KEGG and GO enrichment analyses to assess their
   association with the cGAS-STING pathway. Immune infiltration analysis
   was conducted using the CIBERSORT package, and checkpoint differences
   among MRE11 expression subgroups were analyzed using the limma package.
   Drug sensitivity was evaluated using data from TCGA, GDSC (Genomics of
   Drug Sensitivity in Cancer), and CTRP (The Cancer Therapeutics Response
   Portal) databases, and the relationship between MRE11 and drugs was
   visualized via Cytoscape. IC50 (half maximal inhibitory concentration)
   values were calculated using the pRRophetic package to quantitatively
   assess drug sensitivity differences between MRE11 expression groups.

Preprocessing and analysis of scRNA-seq data

   The scRNA-seq datasets [73]GSE149614, [74]GSE125449, and [75]GSE151530
   were analyzed using Seurat v4. Cells were filtered based on criteria of
   300 to 7,000 expressed genes, 1,000 to 100,000 unique molecular
   identifiers (UMIs), and less than 10% mitochondrial genes. The
   [76]GSE149614 cohort was normalized using the NormalizeData, ScaleData,
   and FindVariableFeatures functions, selecting the top 3,000 highly
   variable genes (HVG) to stabilize UMI count variance. Data were
   integrated using the IntegrateData function, with 2,000 cells selected
   as anchors. For [77]GSE125449 and [78]GSE151530, normalization and
   integration were performed using the SCTransform (v0.4.1) function and
   the Harmony (v1.21) algorithm.

Clustering and cell type annotation

   Principal component analysis (PCA) was performed, and the shared
   nearest neighbor (SNN) graph and uniform manifold approximation and
   projection (UMAP) were constructed based on the top 30 principal
   components using the Louvain algorithm. Differentially expressed genes
   in the [79]GSE149614 dataset were identified using the FindAllMarkers
   function, and cell types were annotated using established markers
   [[80]26–[81]30], such as ALB, EPCAM, SERPINA1, and HNF4A for
   Hepatocytes; CD79A, CD79B, CD37, and MS4A1 for B cells (BCs); CD4,
   CD3D, CD3E, and IL7R for CD4 + T cells; GZMK, CD8A, and CD8B for
   CD8 + T cells; GNLY, NKG7, NCR1, and KLRC1 for natural killer cells
   (NKs); C1orf54, LGALS2, CD1C, and XCR1 for dendritic cells (DCs); CD14,
   CD163, APOE, C1QA, C1QB, and C1QC for macrophages (Mac); PECAM1, VWF,
   FLT1, and CLDN5 for endothelial cells (ECs); and COL1A1, DCN, COL1A2,
   and COL3A1 for fibroblasts (Fibs). For the [82]GSE151530 and
   [83]GSE125449 datasets, cell types and subpopulations were annotated
   using SingleR (v2.4.1) and canonical marker-based scoring. The
   [84]GSE151530 and [85]GSE125449 datasets were annotated using SingleR
   and marker-based scoring, Additionally, in the [86]GSE151530 dataset,
   macrophages were further classified into immune-suppressive
   subpopulations (marked by ‘MRC1’, ‘CD163’, ‘TGFB1’, ‘SPP1’) and
   immune-activating subpopulations (marked by ‘CD68’, ‘CD86’, ‘TNF’,
   ‘CD40’) [[87]31–[88]33].

InferCNV (Inference of copy number Variations) analysis

   CNV was inferred using the InferCNV (v1.18.1) algorithm. Gene location
   information was processed, and expression matrices were extracted from
   the scRNA-seq data, with reference cell groups defined. CNV states in
   the MRE11 high-expression group were inferred by comparing gene
   expression levels to the reference cell group, and CNV frequencies were
   quantitatively analyzed across cell clusters.

Functional enrichment and trajectory analysis of differential genes

   Differentially expressed genes between MRE11 high- and low-expression
   groups were identified using the FindMarkers function, and KEGG and GO
   pathway enrichment analyses were performed using Gene set enrichment
   analysis (GSEA) and msigdbr (v1.16.0) analysis. The results were
   visualized with the ComplexHeatmap package. GSEA was conducted using
   the clusterProfiler package, and Monocle (v2.30.1) v2 and v3 were used
   for trajectory inference to evaluate the transition of cells from
   malignant states to fibroblasts. Dimensionality reduction,
   classification, and trajectory construction were carried out with
   default parameters, and significant genes were identified via
   unsupervised analysis (Benjamini–Hochberg corrected P value < 0.01).

Cell-Cell communication

   MRE11-mediated cell-cell communication within the TME of HCC,
   ligand-receptor interactions were analyzed using the CellChat (v1.5.0)
   and iTALK (Coolgenome/iTALK (github.com)) packages. Overexpressed genes
   and ligand-receptor pairs were identified using functions such as
   identifyOverExpressedGenes, and communication probabilities were
   calculated. Cell communication networks were constructed using the
   FindLR function in the iTALK package. Furthermore, the NicheNet
   (v2.2.0) method was employed to predict ligand-receptor interactions
   between different cell types in patients receiving immunotherapy,
   providing further insight into MRE11’s role in regulating the TME.

Spatial transcriptome analysis of MRE11 and cells

   In this study, we focused on analyzing the HCC-2T sample [[89]19].
   First, we performed co-localization analysis and cell degree analysis
   of MRE11A + malignant cells and MRE11A + Cancer Associated Fibroblasts
   (CAFs). Next, we used the CellChat R package to analyze intercellular
   interactions between the MRE11A + malignant cell subpopulations and CAF
   subpopulations, specifically focusing on the Hepatocyte Growth Factor
   (HGF)-Mesenchymal-Epithelial Transition factor (MET) signaling pathway.
   Finally, the commot package was used to visualize the signaling pathway
   diagrams of the HGF-MET pathway.

Predictive model construction combining Bulk-seq data and machine learning

   To construct an accurate predictive model, we selected 95 candidate
   genes derived from differential expression in MRE11 high- and
   low-expression groups in [90]GSE149614, scRNA-seq tumor and adjacent
   normal tissues, and TCGA-Liver Hepatocellular Carcinoma (LIHC) data.
   Ten models from a pool of 101 machine learning algorithms, including
   random survival forest (RSF) (randomForest, v4.7-1.2), Lasso (glmnet,
   v4.1-8 for Lasso-Cox regression), and Ridge (v3.3), were
   cross-validated using leave-one-out cross-validation (LOOCV). The model
   was trained on TCGA-LIHC data and validated using the [91]GSE14520 and
   ICGC LIRI-JP datasets. The best model was selected based on the Harrell
   concordance index (C-index), and predictive accuracy was evaluated
   using time-dependent ROC curves, calibration curves, and decision curve
   analysis (DCA). Kaplan-Meier (KM) survival analysis was used to assess
   the model’s prognostic power.

RT-qPCR

   Total RNA was extracted from samples using RNAzolt (Huaxingbio,
   HX16010), followed by reverse transcription into cDNA. Real-time
   quantitative PCR (RT-qPCR) was performed using the PowerUp SYBR Green
   Master Mix (Applied Biosystems, 2607157). MRE11 expression was
   normalized to GAPDH and evaluated using the ΔΔCT method. All
   experiments were conducted on the ABI 7500 Real-Time PCR System
   (Applied Biosystems, USA). The primer sequences for MRE11 were:
   Forward, 5′-GGG TCT CAA AGA GGA AGA GAC AC-3′; Reverse, 5′-GAC ATT TCG
   GGA AGG CTG CT-3′. GAPDH primers were: Forward, 5′-GGT GGT CTT CTC TGA
   CTT CAA CA-3′; Reverse, 5′-GTT GCT GTA GCC AAA TTC GTT GT-3′.

Immunohistochemistry (IHC)

   HCC tissue samples were fixed with 4% paraformaldehyde and sectioned.
   Sections were blocked with 5% bovine serum albumin (BSA) at room
   temperature for 2 h and incubated overnight at 4 °C with an anti-MRE11
   recombinant antibody (Huaxingbio, HX16004, 1:150, RRID: AB_3675435).
   The next day, sections were washed with phosphate buffered solution
   (PBST) and incubated with goat anti-rabbit secondary antibody (Abcam,
   ab288151, 1:500, RRID: AB_3675437) at room temperature for 1 h. After
   washing, the sections were developed using diaminobenzidine (DAB)
   solution, counterstained with hematoxylin, and observed under a
   microscope.

Western blotting (WB)

   Total protein was extracted from tissue samples using RIPA buffer
   (Huaxingbio, HX1862-1). Ten micrograms of protein were separated via
   10% SDS-PAGE and transferred onto a polyvinylidene fluoride membrane.
   The membrane was blocked with a 5% BSA blocking buffer (Huaxingbio,
   HX3303). The blot was incubated overnight with primary antibodies,
   including rabbit anti-MRE11 (Huaxingbio, HX16004, 1:500, RRID:
   AB_3675435) and β-tubulin (Huaxingbio, HX1829, 1:5000, RRID:
   AB_3662663). After three washes with Tris-Borate-Sodium Tween-20
   (TBST), the blot was incubated with an anti-rabbit secondary antibody
   (Huaxingbio, HX2031, 1:10,000, RRID: AB_3572247) for 2 h. Bands were
   visualized using an Enhanced Chemiluminescence (ECL) detection system
   (LAS-3000, JAPAN, NO.7612350).

Statistical analysis

   Statistical analyses were performed using R software (version 4.3.1)
   and Python software (version 3.9.11). Parametric tests (Student’s
   t-test or ANOVA) were applied for normally distributed variables.
   Non-parametric tests (Wilcoxon rank-sum test or Kruskal-Wallis test)
   were used to compare continuous variables between two or more groups
   when the data were non-normally distributed. Pearson’s correlation or
   Spearman’s rank-order correlation was used to assess relationships
   between continuous variables. P value < 0.05 was considered
   statistically significant.

Results

   This study analyzed two single-cell RNA-seq datasets and bulk RNA-seq
   data to explore the role of MRE11 in HCC. Using immune infiltration
   analysis, pathway enrichment, pseudotime trajectory analysis, spatial
   transcriptome analysis, and cell-cell communication models, we
   demonstrated how MRE11 overexpression modulates the immune
   microenvironment and reshapes the TME. By employing 101 machine
   learning algorithms, we built a prognostic model based on
   MRE11-associated genes and validated its robustness as a potential
   clinical therapeutic target (Fig. [92]1A).

Fig. 1.

   [93]Fig. 1
   [94]Open in a new tab

   Differential expression analysis of MRE11 in HCC tumor and adjacent
   non-tumor tissues. A. Workflow outlining the experimental design and
   analysis strategy B. Venn diagram showing the intersection of 490
   DDR-related genes with 79 potential causal genes implicated in HCC
   causality. C. Mendelian randomization analysis, depicted as a scatter
   plot, reveals the potential causal relationship between MRE11 and HCC.
   D. Comparison of MRE11 mRNA expression levels between HCC tissues and
   paired normal tissues from the TCGA database, showing significant
   upregulation of MRE11 in tumors. E. UMAP dimensional reduction
   visualization of MRE11 expression at the single-cell level (top) and
   distribution across cell types (bottom), illustrating MRE11 expression
   patterns across different cell populations. F. Spatial transcriptomics
   of MRE11 in HCC tissues visualized using 10x Genomics technology, based
   on the HRA000437 dataset, depicting the spatial distribution of MRE11
   expression. G. qPCR analysis of MRE11 expression in tumor and adjacent
   non-tumor tissues from 92 HCC patients, displayed as bar plots. H. Bar
   plots comparing differential MRE11 expression between tumor and
   adjacent tissues from 10 HCC patients. I-J. Immunohistochemical
   validation of MRE11 expression in tumor (I) and adjacent tissues (J)
   from one HCC patient, demonstrating differences at the protein level.
   K. Western blot analysis of MRE11 protein expression in paired tumor
   and adjacent normal tissues from six HCC patients shows a significant
   upregulation of MRE11 in tumor tissues compared to normal tissues
   (P = 0.03), with β-tubulin as the loading control. Grayscale
   quantification confirms the elevated MRE11 levels in tumor tissues.
   Data are presented as mean ± SD, with statistical significance
   determined by paired t-test. Statistical significance is denoted as
   *P < 0.05, **P < 0.01, ***P < 0.001

MRE11 as a potential prognostic target for HCC

   Through SMR analysis, we identified 79 genes with potential causal
   relationships with HCC (Supplementary Table 1). By intersecting these
   genes with the DDR gene set (Fig. [95]1B), we identified two candidate
   genes: MRE11A (P = 0.027) and UVSSA (P = 0.015). Due to the serious
   heterogeneity observed in UVSSA (P_HEIDI < 0.05), MRE11A
   (P_HEIDI > 0.05) emerged as the only DDR gene with a causal link to HCC
   (Fig. [96]1C). Therefore, we focused further investigations on the
   MRE11A gene.

   MRE11 was found to be significantly upregulated in HCC through paired
   sample T-tests (P = 1.1e-10) (Fig. [97]1 D). This finding was further
   supported by single-cell and spatial transcriptomic analyses, which
   demonstrated elevated MRE11 expression in HCC tissues (Fig. [98]1E-F).
   In a cohort of 92 patients from two major medical centers, qPCR
   confirmed the differential expression of MRE11 at the RNA level between
   tumor and adjacent non-tumor tissues (P < 0.0001) (Fig. [99]1G). IHC
   staining from 10 patients validated these results at the protein level
   (P < 0.0001) (Fig. [100]1H-J). Western blot analysis of tumor and
   normal tissues from six additional patients corroborated the
   differential expression of MRE11 (P = 0.03) (Fig. [101]1K).
   Stratification of the 92 patients based on MRE11 expression levels
   (median = 1.139) revealed significant correlations between MRE11
   expression and various clinical parameters (Table [102]1).

   Prognostic analyses demonstrated a strong association between MRE11
   expression and overall survival (OS, P = 9.8e-3) as well as
   relapse-free survival (RFS, P = 2.7e-3) (Supplementary Fig. [103]1
   A-B). Kaplan-Meier (K-M) survival analysis further indicated that high
   MRE11 expression was linked to poorer outcomes in OS (P = 0.0036), RFS
   (P = 0.015), progression-free survival (PFS, P = 0.0019), and
   disease-specific survival (DSS, P = 0.0013) (Supplementary Fig. [104]1
   C-F). Correlation analysis revealed significant associations between
   MRE11 expression and clinical parameters, including TNM stage
   (P < 0.01), T stage (P < 0.01), tumor grade (P < 0.0001), and patient
   age (P < 0.001) (Fig. [105]2A-F, Supplementary Fig. [106]1 G-J). Based
   on these findings, we developed a predictive model with a C-index of
   0.75 (95% CI: 0.69–0.82). Time-dependent ROC analysis demonstrated area
   under the curve (AUC) values of 0.77, 0.84, and 0.84 for 1-, 3-, and
   5-year survival predictions, respectively, with calibration curves
   showing high predictive accuracy (Fig. [107]2G-I, Supplementary Fig.
   [108]1 K).

Fig. 2.

   [109]Fig. 2
   [110]Open in a new tab

   Role of MRE11 in HCC Prognosis and Construction of a Predictive
   Model. A-F. Violin plots based on the TCGA dataset display MRE11
   expression distribution across different clinical characteristics,
   including T classification (A), N classification (B), M classification
   (C), stage (D), histologic grade (E), and gender (F), revealing
   significant correlations between MRE11 and clinical parameters. G.
   Mulberry diagrams of the prediction model showing the prognostic
   predictive ability of MRE11 under different clinical characteristics.
   H. A nomogram is used to demonstrate the application of MRE11 as a
   prognostic indicator in HCC patients; Time variable is expressed in
   days.I. Time-dependent ROC curves evaluate the model’s predictive
   performance for 1-year, 3-year, and 5-year survival rates; Time
   variable is expressed in days. *P < 0.05, **P < 0.01, ***P < 0.001,
   ****P < 0.001, P = 0.05, P > 0.05

MRE11 mutation analysis and lncRNA-miRNA-MRE11 regulatory network

   Mutation analysis revealed that MRE11 is co-mutated most frequently
   with TP53 (P = 0.02) (Supplementary Fig. [111]2 A). Mendelian
   randomization confirmed a positive causal relationship between MRE11
   and TP53 (IVW: P = 0.005, OR = 1.13) (Supplementary Fig. [112]2 B-E).
   Numerous studies have highlighted the pivotal role of mRNA
   modifications in cancer progression and incidence [[113]34]. In HCC,
   MRE11 expression significantly correlated with most mRNA modification
   genes (P < 0.05) (Supplementary Fig. [114]2 F), and different
   modification types—such as m1A, m5C, and m6A—reinforced the association
   between MRE11 and these genes. These findings suggest that MRE11 may
   contribute to genomic instability, potentially driving HCC initiation
   and progression. Copy number variation and DNA methylation analyses
   suggest that MRE11 expression may be regulated through a complex
   interplay of epigenetic modifications and other regulatory mechanisms
   (P < 0.001). The exact mechanisms underlying this regulation remain to
   be further elucidated in future studies (Supplementary Fig. [115]2
   G-I).

   Target miRNAs for MRE11 in HCC were identified using miRDB,
   DIANA-micro, miRWalk, and miRcode databases, yielding 126, 308, 2108,
   and 33 miRNAs, respectively. The miRNAs that intersected across two or
   three databases were identified and individually analyzed using
   Starbase 2.0, resulting in the identification of eight miRNAs
   (hsa-miR-30b-5p, hsa-miR-148a-3p, hsa-miR-152-3p, hsa-miR-194-5p,
   hsa-miR-488-3p, hsa-miR-552-5p, hsa-miR-664b-3p, and hsa-miR-3163), all
   of which were negatively correlated with MRE11 expression
   (Supplementary Fig. [116]3 B-I). These miRNAs were further analyzed for
   their targeting of lncRNAs, leading to the construction of a
   comprehensive lncRNA-miRNA-MRE11 regulatory network (Supplementary Fig.
   [117]3 A, J, K, Supplementary Table 2). Typically, mRNA expression
   exhibits a negative correlation with miRNA expression [[118]35], and
   MRE11 expression was negatively correlated with its target miRNAs.
   Consistent with the ceRNA hypothesis, lncRNAs likely act as competing
   endogenous RNAs (ceRNAs), sponging these miRNAs to regulate MRE11
   expression [[119]36]. This highlights a dual regulatory mechanism where
   lncRNAs and miRNAs intricately control MRE11 expression, providing
   potential insights into post-transcriptional regulation in HCC.

Fig. 3.

   [120]Fig. 3
   [121]Open in a new tab

   Pathway enrichment, immune correlation, and drug sensitivity analysis
   of MRE11. (A) Bubble plot showing KEGG and GO pathway enrichment
   analysis based on 150 MRE11-related genes, highlighting a significant
   association with DNA damage repair pathways; Bonferroni-corrected P
   values. (B) Scatter plot illustrating the correlation between MRE11 and
   the cGAS-STING pathway. (C) Heatmap demonstrating the correlation
   analysis between MRE11 and cGAS-STING pathway-related genes.*P < 0.05,
   **P < 0.01, ***P < 0.001, ****P < 0.001. (D) Box plot representing
   immune cell infiltration analysis, showing differences in infiltration
   levels across various immune cell types between MRE11 high- and
   low-expression groups. (E) Analysis of MRE11-associated
   chemotherapeutic drug sensitivity from the CTRP database, displaying
   the top 30 drugs with significant positive or negative correlations.
   Light blue bubbles represent drugs that are positively correlated with
   MRE11 expression, indicating that higher MRE11 expression levels may
   contribute to drug resistance. In contrast, pink bubbles represent
   drugs that are negatively correlated with MRE11 expression, suggesting
   that higher MRE11 expression levels may enhance drug sensitivity. (F)
   Analysis of MRE11-associated chemotherapeutic drug sensitivity from the
   GDSC database, displaying the top 30 drugs with significant positive or
   negative correlations. Light blue bubbles represent drugs that are
   positively correlated with MRE11 expression, indicating that higher
   MRE11 expression levels may contribute to drug resistance. Similarly,
   pink bubbles represent drugs that are negatively correlated with MRE11
   expression, suggesting that higher MRE11 expression levels may enhance
   drug sensitivity. (G) Immunotherapy checkpoint analysis depicting
   differential expression of PD-1, PD-L1, and other checkpoints between
   MRE11 high- and low-expression groups

Pathway enrichment, immune correlation, and drug sensitivity analysis

   KEGG and GO enrichment analyses revealed that MRE11-related genes are
   closely associated with DDR pathways (Fig. [122]3A, Supplementary Fig.
   [123]4 A-B). Notably, MSH2 emerged as the sole overlapping gene between
   the 50 MRE11-associated interactome and 100 strongly related genes of
   MRE11. As a core DNA mismatch repair component, its co-occurrence with
   MRE11 suggests potential epistatic interactions or convergent pathways
   driving genomic instability in MRE11-high HCC patients (Supplementary
   Fig. [124]4 A-B). In addition, given the close relationship between the
   cGAS-STING pathway and DDR [[125]6], we analyzed the correlation
   between MRE11 and the cGAS-STING pathway (P = 1.9e-21, R = 0.47), as
   well as its associated genes (P < 0.01), revealing a significant
   association (Fig. [126]3B-C, Supplementary Fig. [127]4 C-J). Since
   cGAS-STING pathway activation mediates immune responses, we conducted
   an immune infiltration analysis. High MRE11 expression correlated with
   the activation of various immune cell types, including macrophages
   (P < 0.05), T cells (P < 0.05), dendritic cells (P < 0.01), and B cells
   (P < 0.001) (Fig. [128]3D, Supplementary Figs. [129]4 K-L).

Fig. 4.

   [130]Fig. 4
   [131]Open in a new tab

   Expression Patterns of MRE11 in scRNA-seq Analysis. (A) A TSNE plot
   visualizes the clustering of nine identified cell types from
   pre-processed scRNA-seq data. TSNE of malignant and non-malignant cells
   (total cells = 58,471) is colored in terms of clusters (left) or major
   cell types (right). Specific clusters corresponding to each cell type
   are as follows: CD4 + T cells: Clusters 0, 2, 3, 24, and 25; CD8 + T
   cells: Cluster 1; B cells: Clusters 6, 17, 20, 30, and 32; Natural
   killer cells (NKs): Clusters 4 and 19; Dendritic cells (DCs): Clusters
   29 and 33; Macrophages (Mac): Clusters 8, 9, 11, 13, 18, and 31;
   Endothelial cells (ECs): Clusters 10, 22, and 23; Fibroblasts (Fibs):
   Clusters 21 and 27; Malignant Hepatocytes: Clusters 5, 7, 12, 14, 15,
   16, 26, and 28. (B) A bubble chart displays the expression of canonical
   marker genes across different cell types. Key markers and their
   corresponding cell types are as follows: ALB, EPCAM, SERPINA1, and
   HNF4A for Hepatocytes; CD79A, CD79B, CD37, and MS4A1 for BCs; CD4,
   CD3D, CD3E, and IL7R for CD4 + T cells; GZMK, CD8A, and CD8B for
   CD8 + T cells; GNLY, NKG7, NCR1, and KLRC1 for NKs; C1orf54, LGALS2,
   CD1C, and XCR1 for DCs; CD14, CD163, APOE, C1QA, C1QB, and C1QC for
   Mac; PECAM1, VWF, FLT1, and CLDN5 for ECs; and COL1A1, DCN, COL1A2, and
   COL3A1 for Fibs; The redder the bubble, the higher the expression; the
   larger the bubble, the greater the number of cells in the cluster where
   the corresponding gene is expressed. (C) Bar and pie charts illustrate
   the proportions and relative numbers of nine cell types among 58,471
   cells. (D) Bar plots show the relative percentages of nine cell types
   captured across the patients with 8 benign livers tissues and 10 HCC
   tissues. (E) Comparison of cell type proportions between high and low
   MRE11 expression groups (using the median value as the cutoff),
   revealing heterogeneity within the tumor microenvironment. (F)
   Expression levels and distribution of MRE11 across different cell
   types. (G) A scatter plot of differentially expressed genes between
   high and low MRE11 expression groups (LogFC = 0.25, P < 0.05). (H) A
   UMAP plot shows the expression and distribution of differentially
   expressed genes across cell types. (I) A bar chart of GO enrichment
   analysis highlights the major functions of differentially expressed
   genes. (J) A UMAP plot shows AUC-based enrichment of the cGAS-STING
   pathway in various cell types. K. A heatmap of Gene Set Variation
   Analysis (GSVA) pathway enrichment analysis visualizes pathway activity
   across different cell types

   Further analysis revealed significant positive correlations between
   high MRE11 expression and immune checkpoints, including PD-1
   (P < 0.001), PD-L1 (P < 0.01), CTLA4 (P < 0.001), and HAVCR2
   (P < 0.001) (Fig. [132]3G). Subsequently, we performed a drug
   sensitivity analysis of the MRE11 gene using the GDSC and CTRP
   databases (Supplementary Table 3), presenting the top 30 drugs with
   positive and negative correlations (Fig. [133]3E-F). We also quantified
   IC50 values for the high and low expression groups of MRE11
   (Supplementary Table 3), visualizing results for 20 drugs
   (Supplementary Figs. [134]5 A-T).

Fig. 5.

   [135]Fig. 5
   [136]Open in a new tab

   Mechanistic Insights into MRE11 at the scRNA-seq level. A-B. GSEA
   enrichment analysis demonstrates two key pathways—Immune Response
   Regulating Signaling Pathway and Liver Cancer Krt19 Dn Pathway—showing
   a correlation between high MRE11 expression and immune response
   inhibition and tumor progression; Bonferroni-corrected P values. C.
   Pseudo-time analysis illustrates the transition from malignant cells to
   fibroblast-like states in the MRE11 high-expression group, with cells
   colored by pseudo-time (right) and cell type (left), tracking changes
   during processes such as differentiation. D. Pseudo-time trajectory
   plots show changes in differential gene expression during the
   transition from malignant cells to fibroblast-like states. E. The
   expression level of MRE11 regarding the pseudotime and cell state
   changes; different colors represent different cell types as follows: B
   cells (BCs) (light olive), Macrophages (Mac) (light pink), CD4 + T
   cells (teal), Endothelial cells (ECs) (blue), Natural Killer (NK) cells
   (lavender), Fibroblasts (Fibs) (salmon), Dendritic cells (DCs) (peach),
   Hepatocytes (coral), and CD8 + T cells (pale green). F-G. CellChat
   analysis comparing the differences in cell-cell interaction weights
   between MRE11 high-expression (F) and low-expression (G) groups across
   various cell types H-I. CellChat analysis reveals the interaction
   network between fibroblasts and other cell types in the MRE11
   high-expression group (H) and highlights the interaction strength of
   the HGF-MET ligand-receptor pair across different cell types (I)

MRE11-Driven heterogeneity in the tumor microenvironment

   After quality control and filtering, we obtained 58,471 high-quality
   single-cell transcriptomes from the [137]GSE149614 dataset, comprising
   27,443 non-tumor and 31,028 HCC cells. Dimensionality reduction and
   clustering revealed key heterogeneity across different tissue types,
   hepatitis types, and pathological stages. Major cell populations
   identified include Hepatocytes, BCs, CD4 + T, CD8 + T, NKs, DCs, Mac,
   ECs, and Fibs (Fig. [138]4A-C, Supplementary Fig. [139]6 A-F). We
   analyzed cellular composition at the individual sample level, noting
   distinct differences in cell origins between normal and cancerous
   tissues (Fig. [140]4D, Supplementary Fig. [141]6 B, D). In HCC tissues,
   malignant hepatocytes predominated, while immune cell infiltration was
   more prominent in normal tissues. Tumor progression correlated closely
   with cancer transformation, showing increased infiltration of immune
   cells with advancing tumor stages (Supplementary Fig. [142]6 C-D).
   Additionally, hepatocytes were more abundant in HCV patients than in
   HBV and non-hepatitis patients, whereas B and T cell infiltration was
   lower (Supplementary Fig. [143]6 E-F).

Fig. 6.

   [144]Fig. 6
   [145]Open in a new tab

   MRE11 Enhances Malignant Cell-CAF Communication via HGF-MET pathway A.
   UMAP clustering of [146]GSE125449 dataset cell populations identifies
   major types. Malignant cells are highlighted in red, showing clear
   clustering differences from other groups; Abbreviation: Tumor
   Endothelial Cell, TEC; Cancer-associated Fibroblasts, CAF;
   Tumor-associated Macrophages, TAM; Hepatocyte Progenitor Cell-like
   Hepatocytes, HPC-like; Uniform Manifold Approximation and Projection,
   UMAP B. t-SNE visualization shows distinct clustering of malignant and
   HPC-like cells, indicating divergent differentiation and transformation
   trajectories. C. t-SNE density plot illustrates MRE11A gene expression,
   with high levels in malignant cells and specific regions of peak
   density, supporting its role in tumor progression. D. Pseudotime
   analysis reveals a bifurcating trajectory of HPC-like cells, with one
   branch differentiating towards normal hepatocytes and the other towards
   malignant cells, emphasizing MRE11’s role in driving malignancy; The
   square area is the starting section, gray points are cells that were
   not analyzed, and red, yellow, and blue points are cells for which
   trajectory analysis was performed; arrows show the direction of the
   trajectory. E. CellChat heatmap shows increased interaction between
   MRE11 + malignant cells and CAFs, with higher interaction scores
   reflecting MRE11’s key role in tumor-stroma crosstalk. F. Spatial
   transcriptomic analysis reveals elevated MRE11A expression in tumor
   regions, particularly in the tumor core and CAF-enriched areas. G. Cell
   degree analysis of MRE11 + malignant cells shows stronger interactions
   at the malignant cell-CAF interface, indicating active tumor-stroma
   signaling. H. Spatial mapping of HGF (sender) and MET (receptor)
   reveals the signaling intensity of the interplay between HGF as sender
   and MET as receiver between the malignant cell type and the CAF cell
   type, supporting MRE11’s regulation of this signaling axis; Blue
   represents the spatial localization of the ligand HGF in cell
   communication; red represents the spatial localization of the receptor
   MET in cell communication. I. Spatial co-enrichment analysis highlights
   significant MRE11 + malignant cell-CAF interactions, with yellow
   regions indicating strong spatial co-localization and interaction
   intensity. J. Vector field plot of the HGF-MET signaling pathway shows
   signal direction and intensity between MRE11 + malignant cells and
   CAFs, confirming MRE11-driven HGF-MET activity. K. Spatial distribution
   of MRE11 + CAFs shows enrichment near malignant cell populations,
   suggesting spatial coherence and functional interaction. L.
   Co-localization of MRE11 + CAFs and malignant cells illustrates a
   closely connected signaling network, further validating MRE11’s central
   role in promoting tumor progression through the HGF-MET axis

   To further investigate the heterogeneity of the tumor microenvironment
   related to MRE11, we compared the cellular compartments between high
   and low expression subgroups of MRE11 (Fig. [147]4E). The MRE11-high
   group exhibited higher hepatocyte content and lower levels of immune
   cells. The Infer CNV analysis of malignant cells (n = 12,001) revealed
   increased copy number variations (CNVs) and a higher degree of
   malignancy in the MRE11-high group, with a similar level of
   heterogeneity observed among clusters (Supplementary Fig. [148]6 G-H).

MRE11-Driven ecosystem changes in the HCC tumor microenvironment

   Single-cell resolution analysis revealed significant variation in MRE11
   expression across different cell types (Fig. [149]4F). Interestingly,
   MRE11 expression was higher in T cells, NK cells, hepatocytes, and
   macrophages. Differential expression analysis between MRE11 high and
   low groups identified 371 differentially expressed genes
   (Fig. [150]4G). KEGG and GO enrichment analyses linked these genes to
   the cGAS-STING pathway. AUC scores confirmed the enrichment of
   differential genes and the cGAS-STING pathway in hepatocytes and immune
   cells (Fig. [151]4H, J). KEGG and GO analysis showed that the
   cGAS-STING pathway was significantly upregulated in the high MRE11
   expression group, exhibiting negative regulation in malignant
   epithelial cells and positive regulation in immune cells
   (Fig. [152]4I-K). GSEA analysis revealed suppressed immune responses in
   the high MRE11 expression group, along with the activation of pro-tumor
   pathways (Liver_Cancer_Krt19_Dn pathway [[153]37]), indicating that
   MRE11 may promote tumorigenesis in HCC by modulating the immune
   ecosystem (Fig. [154]5A-B, Supplementary Table 4).

   To explore MRE11’s impact on the TME, pseudotime analysis revealed that
   high MRE11 expression may drive hepatocytes towards a fibroblast-like
   phenotype, leading to a fibrotic TME and reduced drug sensitivity
   (Fig. [155]5C). Temporal trajectory analysis indicated MRE11 expression
   showed a decreasing trend over time, and the differential genes
   exhibited two opposite expression trends, further confirming MRE11
   dynamic role in HCC (Fig. [156]5D-E, Supplementary Fig. [157]6 I-J). To
   explore the specific mechanism of MRE11-mediated fibrosis in
   hepatocytes, we used CellChat and iTALK to compare cellular
   interactions between MRE11 high- and low-expression groups. Hepatocytes
   in the MRE11-high group showed significantly more frequent interactions
   with fibroblasts, endothelial cells, macrophages, and CD4 + T cells,
   whereas there was no significant difference in interactions between
   other cell types (Fig. [158]5F-G, Supplementary Fig. [159]7 A-H).
   Ligand-receptor analysis revealed that fibroblast-hepatocyte
   interactions were most prominent, with notable activation of the
   HGF-MET pathway in the MRE11-high group, suggesting a key role in liver
   fibrosis (Fig. [160]5H-I, Supplementary Fig. [161]7 I). The HGF-MET
   signaling pathway plays a crucial role in liver fibrosis, where the
   upregulation of HGF expression triggers the activation, proliferation,
   and migration of fibroblasts through the activation of MET receptors
   [[162]38]. Finally, by iTALK analysis, we further refined the immune
   checkpoint-, cytokine-, and growth factor-associated ligand–receptor
   interactions in the MRE11 high-expression group, revealing their
   important role in shaping the TME (Supplementary Fig. [163]7 J-M).
   These results suggest that MRE11 not only plays a role by regulating
   the tumor immune microenvironment, but may also contribute to the
   development and progression of HCC by enhancing intercellular
   communication, especially in the process of immune escape and fibrosis.

Fig. 7.

   [164]Fig. 7
   [165]Open in a new tab

   TME Characteristics Regulated by MRE11 in the Context of Immunotherapy.
   (A) UMAP plot based on 30,458 filtered cell scRNA-seq from
   [166]GSE151530 database (bottom); UMAP plot based on 58,471 filtered
   cell scRNA-seq from [167]GSE149614 database (top). (B) Bar charts show
   the relative percentages of eight cell types across different samples,
   illustrating changes in cell composition pre- and post-immunotherapy.
   (C) UMAP plot (left) and bar chart (right) show the distribution of
   cell types and proportions between high and low MRE11 expression groups
   in the non-immunotherapy cohort. (D) UMAP plot (left) and bar chart
   (right) of cell type distribution in the immunotherapy cohort between
   high and low MRE11 expression groups. (E) Expression levels and
   distribution of MRE11 across various cell types. (F) UMAP plot of
   AUC-based enrichment of the cGAS-STING pathway in different cell types.
   (G) Heatmap illustrating pathway activity for various cell types based
   on GSVA pathway enrichment analysis. (H) GSEA pathway enrichment
   analysis reveals significant enrichment of the Cytosolic DNA Sensing
   Pathway (P < 0.05, Enrichment Score < −0.5/>0.5)

MRE11 enhances malignant Cell-CAF interaction via HGF-MET pathway

   Using UMAP dimensionality reduction, we identified cell populations
   within the [168]GSE125449 dataset, including malignant cells, HPC-like
   cells (normal hepatocytes), B cells, CD4 + T cells, NKT cells, TAMs
   (Tumor-associated Macrophages), ECs, and CAFs (Fig. [169]6A). We then
   examined the differential expression of MRE11 between malignant and
   HPC-like cells, finding it significantly upregulated in malignant
   cells, suggesting that it may play a potential role in tumorigenesis.
   Pseudotime analysis showed a bifurcation in hepatocyte
   differentiation—toward either normal or malignant states—supporting
   MRE11’s involvement in malignant transformation (Fig. [170]6B-D).

   CellChat analysis revealed enhanced communication between
   MRE11-positive malignant cells and CAFs (Fig. [171]6E). Spatial
   transcriptomics further showed that MRE11 + malignant cells were
   primarily localized in tumor regions, particularly near CAF-rich areas
   (Fig. [172]6F-G). By analyzing the intercellular communication of the
   HGF-MET signaling pathway, we detailed the interaction direction
   between HGF (ligand) and MET (receptor) among malignant cells and CAFs.
   The results showed highly active signaling of the HGF-MET pathway
   between MRE11 + malignant cells and CAFs, further verifying the key
   role of MRE11 in regulating the HGF-MET pathway (Fig. [173]6H-J).
   Meanwhile, spatial analysis confirmed significant co-localization of
   MRE11 + CAFs and malignant cells (Fig. [174]6K), while Fig. [175]6L
   demonstrated stronger interactions between MRE11 + CAFs and malignant
   cells.

MRE11 regulation of the TME Post-Immunotherapy

   To further investigate the role of MRE11 in immunotherapy, we analyzed
   the [176]GSE151530 single-cell dataset, which includes 30,458 cells
   from HCC tissues pre- and post-immunotherapy, comprising 10,856 cells
   from untreated tissues and 19,602 cells from treated tissues
   (Fig. [177]7A). After data integration and batch effect correction
   (Supplementary Fig. [178]8 A), the major cell populations identified
   included malignant cells, B cells, CD4 + T cells, CD8 + T cells, NK
   cells, TAMs, Tumor Endothelial Cells (TECs), and CAFs (Fig. [179]7A).
   Post-immunotherapy, the proportion of malignant cells significantly
   decreased, while immune cell infiltration increased, particularly in
   the MRE11 high-expression group, indicating a pivotal role for MRE11 in
   modulating immunotherapy response (Fig. [180]7B-D). Conversely, in
   patients who did not receive immunotherapy, the MRE11 high-expression
   group exhibited a higher proportion of malignant cells and reduced
   immune cell infiltration (Fig. [181]7B-D). MRE11 was broadly expressed
   in both malignant and immune cells in the immunotherapy group,
   suggesting its potential regulatory role in reshaping the tumor
   microenvironment (Fig. [182]7E). Pathway enrichment analysis revealed
   significant enrichment of the cGAS-STING pathway in various immune
   cells—including macrophages, B cells, NK cells, CD4 + T cells, and
   CD8 + T cells—in the MRE11 high-expression group (Fig. [183]7F).
   Further enrichment analysis confirmed that this pathway was markedly
   upregulated in TAMs and NK cells but downregulated in malignant cells
   and CAF subpopulations (Fig. [184]7G-H). Notably, we also observed a
   significant downregulation of the PPAR signaling pathway [[185]39]
   (Supplementary Fig. [186]8 B, Supplementary Table 5).Previous studies
   have implicated this pathway in angiogenesis and metabolic regulation
   in cancer, yet its precise role in tumor growth and progression remains
   controversial [[187]40, [188]41]. Collectively, these findings suggest
   that MRE11 may influence antitumor immune responses by dynamically
   modulating immune regulation and tumor metabolic pathways.

Fig. 8.

   [189]Fig. 8
   [190]Open in a new tab

   Mechanistic Insights into MRE11 Under Immunotherapy. A. Pseudo-time
   analysis demonstrates the transition of malignant cells and endothelial
   cells to fibroblast-like states in the MRE11 high-expression group
   under immunotherapy, with cells colored by pseudo-time (right) and cell
   type (left). B-C. CellChat analysis compares cell interaction weights
   between MRE11 high (B) and low (C) expression groups under
   immunotherapy. D-E. Interaction weight network diagrams of malignant
   cells (D) and endothelial cells (E) highlight their roles in
   immunotherapy. F. A heatmap displays the interaction potential between
   macrophage receptors and the top 30 ligands in malignant cells and
   endothelial cells, with color representing interaction potential. G. A
   dot plot presents the expression and interaction potential of the top
   20 ligands from NicheNet analysis about differential MRE11 expression,
   with dot size indicating the proportion of cells expressing each
   ligand. H. Circle plot based on iTALK tool analysis, showing the
   interactions among cytokines across various cell types. I. UMAP plot of
   macrophage subpopulations based on immune functional attributes. J. Bar
   plot of macrophage subpopulation proportions between MRE11 high- and
   low-expression groups under immunotherapy. K. Pseudo-time analysis
   shows the transition of macrophage subpopulations from immature to
   immunosuppressive cell states. GMP: Granulocyte-Macrophage Progenitor;
   Pro-immune TAMs: Pro-immune Tumor-Associated Macrophages;
   Immunosuppressive TAMs: Immunosuppressive Tumor-Associated Macrophages

   Monocle v3 analysis revealed that hepatic malignant cells and
   endothelial cells partially differentiated into mature malignant cells
   and CAFs (Fig. [191]8A), suggesting that MRE11 may drive tumor
   progression and drug resistance through promoting fibrosis and immune
   evasion. Pseudo-time trajectory analysis demonstrated dynamic changes
   in differential gene expression during tumor evolution, further
   indicating the role of MRE11 in regulating the HCC immune
   microenvironment (Supplementary Fig. [192]8 C). Given the dynamic
   reprogramming of TME cellular communication in HCC, we employed
   CellChat, NicheNet, and iTALK tools to dissect cell-to-cell
   communication in the MRE11 high-expression group. The analysis showed
   that ligand-receptor interactions between malignant cells, endothelial
   cells, and macrophages were significantly enhanced in the MRE11
   high-expression group (Fig. [193]8B-E, Supplementary Fig. [194]8D-I).
   NicheNet analysis revealed that APOE-LRP1 ligand-receptor pairs were
   particularly active in malignant cell-macrophage interactions, while
   A2M-TNFRSF14 pairs were prominent in the endothelial cell-macrophage
   axis (Fig. [195]8F-G). MRE11 high expression significantly alters the
   ligand-receptor networks of cytokines, immune checkpoints, growth
   factors, and other factors, especially in the immune therapy group.
   Enhanced interactions between macrophages and other immune cells
   suggest that MRE11 is crucial in tumor immune responses by modulating
   intercellular communication within the TME (Fig. [196]8H, Supplementary
   Fig. [197]8J-L). Subpopulation analysis of macrophages indicates a
   higher proportion of immunosuppressive macrophages in the MRE11
   high-expression group compared to the low-expression group. Pseudotime
   analysis reveals an increased differentiation of monocytes and
   granulocyte-monocyte progenitors (GMP) into immunosuppressive
   macrophages (Figs. [198]8I-K). These results underscore MRE11’s
   critical role in the polarization of tumor-associated macrophages
   (TAMs) toward an immunosuppressive phenotype.

Identification of differential genes and construction of a robust prognostic
model

   After establishing MRE11 as a poor prognostic indicator for HCC, we
   further utilized the identified transcriptional features by selecting
   95 overlapping genes from scRNA-seq and bulk-seq datasets
   (Fig. [199]9A). A LOOCV framework was used to fit 101 machine learning
   models, with the C-index calculated for each model in both training and
   validation cohorts. The optimal predictive model identified was a
   combination of RSF and Ridge regression, with an average C-index of
   0.71 (Fig. [200]9B, Supplementary Fig. 9 A). RSF importance analysis
   identified 61 differential genes when ntree = 800 (Fig. [201]9C-D). The
   optimal lambda value (Log λ = 0.46) was determined using the LOOCV
   framework when the partial likelihood deviance reached its minimum
   (Fig. [202]9E-F). Rigorous internal and external validation of the
   model was conducted. K-M survival analysis of the TCGA-LIHC and
   [203]GSE14520 datasets showed that patients in the high-risk group, as
   classified by model-derived risk scores, had significantly lower OS
   than those in the low-risk group (P < 0.0001, Fig. [204]9G-H).

Fig. 9.

   [205]Fig. 9
   [206]Open in a new tab

   Differential Gene Screening and Prognostic Model Construction for
   MRE11. (A) Venn diagram shows the intersection of MRE11 differential
   genes ([207]GSE149614), scRNA-seq-derived differential genes
   ([208]GSE149614) between cancerous and normal tissues, and TCGA-LIHC
   dataset differential genes. (B) LOOCV framework was used to calculate
   the C-index for 101 combinatory prediction models, evaluating the
   model’s performance in both training (TCGA) and validation cohorts
   ([209]GSE14520). C-D. Random survival forest analysis displays error
   rate variation with an increasing number of trees (C) and the
   importance ranking of predictor genes (D). E. The process of lambda
   value selection in ridge regression analysis, the Y-axis represents the
   model deviation, the X-axis is log(lambda) (0.46), the red line
   indicates the best lambda value, and the gray shaded part is the 95%
   confidence interval. F. Path diagram of the coefficients in the ridge
   regression analysis, the regression coefficients of different genes
   gradually converge with the change of lambda value. G-H. Kaplan-Meier
   OS curves show significant survival differences between high- and
   low-risk groups in the TCGA-LIHC training cohort (G) and the
   [210]GSE14520 validation cohort (H). I-N. ROC curves demonstrate the
   model’s accuracy in predicting 1-year (I, L), 3-year (J, M), and 5-year
   (K, N) survival rates in both the TCGA-LIHC training cohort (I-K) and
   [211]GSE14520 validation cohort (L-N)

   Time-dependent ROC (Fig. [212]9I-N) and Time-dependent C-index analyses
   (Supplementary Fig. 9 B-C) confirmed the model’s predictive
   performance, with AUC and C-index values exceeding 0.7 in both the
   training and validation cohorts. Calibration and DCA demonstrated good
   model fit and accuracy (Supplementary Fig. 9 D-K). These results
   confirmed the robust predictive performance of the 61 genes identified
   from the MRE11 differential gene screening, suggesting that the
   constructed model may serve as a potential prognostic tool for HCC
   patients.

Discussion

   This study, using SMR analysis, identified MRE11A as the only DDR gene
   with a causal association with HCC. Through multi-omics analysis and
   experimental validation, we comprehensively investigated MRE11’s key
   role in HCC and its potential prognostic value. MRE11 was significantly
   upregulated in HCC, as confirmed by TCGA, single-cell, and spatial
   transcriptomics. Validation through qPCR, immunohistochemistry, and
   Western blot confirmed its differential expression at both RNA and
   protein levels. Survival analysis indicated a strong correlation
   between MRE11 and several prognostic factors (OS, RFS, PFS, DSS),
   underscoring its role in HCC progression and patient survival. A
   predictive model based on MRE11, TNM stage, T stage, and age achieved
   high accuracy, with AUC values exceeding 0.75 for 1-, 3-, and 5-year
   survival, reinforcing MRE11 as a reliable prognostic marker. Mutation
   and Mendelian randomization analyses revealed a positive causal
   relationship between MRE11 and TP53, with MRE11 exhibiting a high
   mutation frequency linked to HCC progression. We also constructed a
   lncRNA-miRNA-MRE11 regulatory network in HCC. Eight miRNAs, identified
   from multiple prediction tools, target both MRE11 and lncRNAs,
   negatively correlating with MRE11 expression. These miRNAs likely
   regulate MRE11 through lncRNA-mediated miRNA sponging, supporting the
   ceRNA hypothesis. This dual-layered regulation underscores MRE11’s
   complex control in HCC. The identified miRNAs and lncRNAs are potential
   therapeutic targets, warranting further validation. Pathway enrichment
   analysis linked MRE11 to DDR and the cGAS-STING pathway, both critical
   in tumor immune response and surveillance [[213]1, [214]3, [215]10].
   Immune infiltration analysis showed that high MRE11 expression
   activated several immune cell types and positively correlated with
   immune checkpoints (PD-1, PD-L1, and CTLA-4), suggesting involvement in
   immune evasion. This highlights MRE11 as a potential therapeutic target
   in HCC. Drug sensitivity analysis further indicated differential
   responses between high and low MRE11 expression groups, emphasizing its
   potential in personalized HCC treatment.

   Through single-cell sequencing analysis, we further explored MRE11
   heterogeneity within the HCC TME. We found that high MRE11 expression
   was associated with a higher proportion of malignant hepatocytes,
   indicating a strong correlation between MRE11 overexpression and tumor
   cell proliferation and expansion. Moreover, high MRE11 expression is
   significantly associated with a lower proportion of immune cells,
   suggesting that it may promote tumor immune evasion by regulating
   immune cell infiltration and function. InferCNV analysis confirmed
   higher frequencies of CNVs in the high MRE11 group, suggesting that
   MRE11 contributes to genomic instability, further driving malignant
   transformation in HCC. Differential gene expression and KEGG/GO
   enrichment analyses revealed that MRE11 negatively regulates cGAS-STING
   pathway in malignant epithelial cells, while it positively regulates
   this pathway in immune cells. Pseudotime trajectory analysis uncovered
   the potential role of high MRE11 expression in promoting the transition
   of tumor cells to fibroblasts, suggesting its involvement in tumor
   fibrosis. Cell-cell communication analysis further demonstrated
   enhanced interactions between hepatocytes and macrophages, endothelial
   cells, and fibroblasts in the high MRE11 expression group, particularly
   through the HGF-MET ligand-receptor axis, implicating MRE11 in liver
   fibrosis and tumor invasion.

   Utilizing the [216]GSE125449 dataset, we examined the role of MRE11 in
   the communication between malignant cells and CAFs. High MRE11
   expression in malignant cells is crucial for the malignant
   transformation of hepatocytes. Pseudotime analysis revealed a
   bidirectional differentiation trajectory from hepatocytes to malignant
   cells, reinforcing MRE11’s role as a driving factor. CellChat analysis
   showed significantly enhanced communication between MRE11 + malignant
   cells and CAFs, particularly through the HGF-MET signaling pathway,
   underscoring MRE11’s regulatory influence on their interactions.
   Spatial transcriptomics further confirmed the colocalization of
   MRE11 + malignant cells and CAFs in the tumor center, indicating that
   MRE11 promotes their interaction by activating the HGF-MET pathway.
   Previous studies have shown that CAFs mediate the HGF/MET signaling
   pathway to enhance cancer cell invasion and immune evasion [[217]42].
   However, whether MRE11 expression regulates the HGF/MET pathway in
   modulating tumor-CAF interactions in HCC remains unreported. Further
   studies are required to validate this potential mechanism.
   Collectively, these results support that high MRE11 expression
   reprograms the immune ecosystem, particularly by regulating the
   cGAS-STING pathway and the HGF-MET axis, driving HCC progression from
   DNA repair dysfunction to malignancy.

   In the context of immunotherapy, we observed significant changes in the
   proportion of malignant and immune cells among patients receiving
   immunotherapy compared to those who did not. Notably, in the
   immunotherapy group, the ratio of malignant cells to immune cells
   shifted markedly in the MRE11 high-expression subgroup, suggesting that
   MRE11 may play a pivotal role in modulating tumor response to
   immunotherapy. MRE11 was highly expressed in tumor cells and
   downregulated immune-related pathways, notably the cGAS-STING pathway,
   in malignant cells. Interestingly, this pathway was upregulated in
   immune cells, particularly TAMs, highlighting MRE11’s dual regulatory
   role in immune responses. These findings indicate MRE11’s potential for
   shaping the tumor immune microenvironment by suppressing immune
   pathways in malignant cells while promoting immune activation in TAMs.
   Furthermore, single-cell trajectory analysis revealed that in the high
   MRE11 group, malignant cells differentiated into both mature malignant
   cells and fibroblasts, while endothelial cells differentiated into
   fibroblasts, further promoting tumor proliferation, metastasis, and
   drug resistance. Using tools such as CellChat and iTALK, we found that
   the interaction between malignant cells and macrophages was notably
   active, especially through the APOE-LRP1 [[218]43] and A2M-TNFRSF14
   [[219]44, [220]45] ligand-receptor pairs, suggesting that MRE11
   regulates immune response and tumor progression through these axes.
   Subpopulation analysis of macrophages revealed a higher proportion of
   immunosuppressive macrophages in the high MRE11 group, with pseudotime
   analysis further confirming the differentiation of monocytes and GMPs
   into immunosuppressive macrophages.

   After establishing MRE11 as a clear predictor of poor prognosis in HCC,
   we developed a robust prognostic model for HCC based on the
   differentially expressed genes associated with MRE11. We screened 95
   differently genes from single-cell RNA-seq and bulk-seq datasets and
   applied 101 machine-learning models to validate key genes associated
   with HCC prognosis. Using a combination of RSF and Ridge regression, we
   successfully developed a prediction model based on the LOOCV framework,
   which exhibited strong performance across multiple independent
   datasets. Internal and external validation using K-M survival analysis,
   time-dependent ROC curves, and C-index analyses, along with calibration
   and decision curves, confirmed the model’s accuracy and robustness.
   These results suggest that the 61 genes identified from the high- and
   low-expression MRE11 subgroups exhibit strong predictive accuracy, and
   the developed prognostic model may serve as a valuable tool for
   forecasting outcomes in HCC patients.

   Despite the comprehensive multi-omics analysis and experimental
   validation presented in this study, several limitations remain. First,
   although we demonstrated the significant role of MRE11 in HCC
   progression and immune regulation, the underlying molecular mechanisms,
   particularly its interaction with the cGAS-STING and HGF-MET pathways,
   require further in-depth functional studies. Second, while single-cell
   and spatial transcriptomics provided valuable insights into MRE11
   heterogeneity within the tumor microenvironment, validation in larger,
   independent cohorts is needed to confirm its clinical relevance.
   Finally, the immunosuppressive role of MRE11 in HCC immunotherapy
   warrants further investigation using preclinical models and clinical
   samples to elucidate its potential as a therapeutic target. Future
   studies integrating advanced techniques, such as CRISPR-Cas9 and
   organoid models, will help address these limitations and strengthen the
   translational value of our findings.

   By integrating external database analyses with internal experimental
   validation, this study systematically elucidates the critical role of
   the MRE11 gene in HCC progression and treatment response. Our research
   highlights that high MRE11 expression significantly reshapes the HCC
   TME, especially in primary tumors and under immunotherapy, underscoring
   its central role in tumor immune escape and therapeutic response.
   Furthermore, the 61 differentially expressed genes associated with
   MRE11, identified from the high- and low-expression subgroups, serve as
   reliable prognostic biomarkers for HCC and enable a robust predictive
   model for patient outcomes. In the future, combining multi-omics
   approaches, MRE11 holds promise as a potential target for HCC diagnosis
   and personalized therapy, laying the foundation for targeted treatment
   strategies against MRE11.

Supplementary Information

   [221]Supplementary Material 1^ (134.1KB, xlsx)
   [222]Supplementary Material 2^ (34.7MB, docx)

Acknowledgements