MCRS1 is associated with immunosuppressive microenvironments in pan-cancer and promotes hepatocellular carcinoma malignant phenotypes

Introduction

Hepatocellular carcinoma (HCC) remains one of the most lethal malignancies globally, ranking as the third leading cause of cancer-related deaths and exhibiting a 5-year survival rate of 18% for advanced-stage patients (1). HCC is known for its extremely high mortality rate, as evidenced by the closely aligned age-standardized incidence and mortality rates worldwide, making its treatment a major global clinical challenge (2). Firstly, the disease typically remains clinically silent in its early stages, resulting in over 70% of cases being identified at advanced phases upon initial detection (3). Secondly, despite advancements in therapeutic strategies, including tyrosine kinase inhibitors (TKIs), immune checkpoint inhibitors (ICIs), and combination therapies—the clinical outcomes for HCC patients remain suboptimal due to tumor heterogeneity, immunosuppressive tumor microenvironment (TME), and a lack of reliable biomarkers for early detection and targeted intervention (4,5). Consequently, the identification of HCC-associated prediagnostic biomarkers not only elucidates the disease’s etiopathology but also facilitates early detection and therapeutic target development.

Emerging evidence highlights the TME as a dynamic ecosystem that orchestrates immune evasion, metabolic reprogramming, and therapeutic resistance in HCC (6). Multi-omics approaches have identified key regulators of these processes, including chromatin remodelers (e.g., SMARCA4) and RNA-binding proteins (e.g., IGF2BP3) (7,8). Among these, microspherule protein 1 (MCRS1, also known as MSP58) has garnered attention for its multiple roles in transcriptional regulation, mitotic control, DNA repair, non-coding RNA processing, and senescence (9,10). MCRS1 is mapped to chromosome 12q13.12​ (11) and exhibits functional interaction with p120 (a proliferation-associated protein)​(12), suggesting its involvement in cell cycle regulation. More importantly, MCRS1 is closely associated with tumorigenesis and progression. In colorectal cancer cells, the expression of MCRS1 is abnormally elevated, and MCRS1 knockdown inhibits the growth of colorectal cancer cells by regulating the Cyclin D1/CDK 4-p21 pathway (13). It has been found that MCRS1 promotes epithelial mesenchymal transition (EMT) and metastasis in non-small cell lung cancer (14). Conversely, a recent study demonstrated that MCRS1 suppresses the progression of gastric cancer (GC) through its interaction with PKMYT1 (15). These opposing roles suggest that MCRS1 may function as a molecular rheostat, balancing oncogenic and tumor-suppressive signals in a tissue-specific manner. However, systematic pan-cancer analyses of MCRS1’s expression patterns and prognostic relevance are lacking. Furthermore, no studies have explored MCRS1’s interactions with immune infiltrates or its potential as a biomarker for immunotherapy response, despite the central role of immune evasion in HCC progression.

In this study, we employed an integrative multi-omics framework to dissect MCRS1’s pan-cancer roles, with a focused investigation of its HCC-specific mechanisms. By harmonizing bulk transcriptomics, single-cell sequencing, spatial transcriptomics, and functional validation, we aim to: (I) delineate MCRS1 expression patterns and prognostic significance across 33 cancer types; (II) elucidate its epigenetic regulation via promoter methylation and alternative splicing; (III) unravel its interactions with DNA repair pathways, stemness indices, and immune cell infiltration; and (IV) validate its functional impact on HCC malignant phenotypes in vitro. This work not only expands the mechanistic understanding of chromatin remodeling in HCC but also identifies MCRS1 as a promising biomarker and therapeutic target for overcoming resistance to current therapies. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1463/rc).

Methods

SangerBox

The SangerBox platform (http://sangerbox.com/index.html), a multifunctional bioinformatics tool harmonizing multi-omics data from The Cancer Genome Atlas (TCGA) repositories, was leveraged for pan-cancer prognostic evaluation of MCRS1, with emphasis on its association with overall survival (OS) outcomes and immunomodulatory roles in HCC (16). Differential expression analysis of MCRS1 between malignant and non-neoplastic tissues was executed in R (version 3.6.4), employing Wilcoxon rank-sum tests for unpaired comparisons and signed-rank tests for paired samples to assess statistical significance. To systematically evaluate MCRS1’s prognostic utility across malignancies, univariate Cox proportional hazards regression models were implemented via the coxph module within the R survival package (v3.2-7), with hazard ratios (HRs) and corresponding P values derived from log-rank testing. Transcriptomic datasets were preprocessed through gene symbol annotation standardization, followed by computational deconvolution of tumor immune microenvironments using the Tumor Immune Estimation Resource (TIMER) framework embedded in the IOBR package (v0.99.9). This allowed quantification of infiltrating immune subsets including B lymphocytes, CD4⁺/CD8⁺ T cells, neutrophils, macrophages, and dendritic cells (DCs). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

The University of Alabama at Birmingham Cancer (UALCAN) data analysis

The UALCAN portal (https://ualcan.path.uab.edu/index.html), an interactive analytical platform aggregating multi-omics repositories including TCGA, MET500 International Consortium, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Children’s Brain Tumor Tissue Consortium data, was harnessed for systematic exploration of MCRS1-clinical correlations (17). This computational framework facilitates multi-modal biomarker discovery through integrated modules for transcriptional profiling [messenger RNA (mRNA)/protein], epigenetic regulation analysis (promoter methylation), and survival pattern interrogation, with seamless connectivity to knowledgebases such as Human Protein Reference Database, GeneCards, and PubMed for functional annotation enrichment. Of particular relevance to this investigation, the platform’s clinical subgroup stratification engine enabled comparative assessment of MCRS1 expression patterns across demographic variables (age/gender), pathological parameters (tumor grade/stage), and molecular subtypes. Proteomic validation was performed using CPTAC mass spectrometry datasets encompassing both total protein and phosphorylation-specific quantitation. All analytical workflows adhered to default parameters with Benjamini-Yekutieli false discovery rate (FDR) correction.

Gene Expression Profiling Interactive Analysis (GEPIA)

The GEPIA platform (http://gepia2.cancer-pku.cn), an open-access web interface synthesizing transcriptomic data from TCGA and GTEx consortiums, was utilized for multi-omics exploration (18). Within this systematic framework, comparative analysis of MCRS1 expression patterns between neoplastic and histologically normal specimens was conducted, with results graphically represented through box plot generation. Raw transcript quantification data underwent standardization via log₂ [transcripts per million (TPM) +1] transformation, where differential expression magnitude [log₂fold change (FC)] was arithmetically determined by contrasting tumor versus normal tissue medians. Statistically rigorous thresholds (|log₂FC| ≥1 with adjusted P<0.01) were applied to define differentially expressed genes. Prognostic stratification was achieved through median-centered binarization of cohort expression values, enabling comparative survival trajectory analysis between MCRS1 high and MCSR1 low subgroups. The proportional hazards assumption was validated prior to implementing Cox regression modeling, where HR with 95% confidence intervals served as a risk metric for pan-cancer survival associations.

Kaplan-Meier plotter

The Kaplan-Meier plotter analytical engine, harmonizing clinicogenomic datasets from TCGA, Gene Expression Omnibus (GEO), and European Genome-Phenome Archive consortia, facilitates pan-cancer interrogation of survival-associated transcriptional patterns across 21 malignancies through its automated survival modeling pipelines (19). This computational resource was interrogated to delineate the prognostic salience of MCRS1 in HCC through two complementary analytical tiers: (I) expression-driven stratification (dichotomized at median mRNA thresholds) for overall/recurrence-free survival analysis; and (II) multivariate subgroup dissection incorporating tumor-node-metastasis (TNM) staging, Child-Pugh grading, and viral etiology status to assess MCRS1’s independent predictive capacity. Cox proportional hazards modeling was implemented to derive HR with 95% confidence intervals, while log-rank testing assessed inter-strata survival divergence (P<0.05 deemed significant).

The Human Protein Atlas (HPA)

The HPA (https://www.proteinatlas.org), a comprehensive repository for human proteome characterization (20), provides systematic documentation of protein expression dynamics and subcellular localization across human tissues. For this investigation, HPA-derived immunohistochemistry datasets were interrogated to evaluate MCRS1 protein abundance in HCC and adjacent normal liver specimens. Tissue sections underwent immunostaining with the validated antibody HPA039057, followed by semi-quantitative assessment categorizing protein expression into four distinct levels: high, medium, low, and undetectable.

LinkedOmics

LinkedOmics (https://www.linkedomics.org), a multi-modal bioinformatics platform integrating TCGA and CPTAC datasets, enables pan-cancer exploration of molecular associations through its triad of analytical modules (LinkFinder, LinkInterpreter, and LinkCompare) complemented by WebGestalt functional enrichment tools (21). To delineate MCRS1-associated gene networks in HCC, RNA sequencing data from 377 TCGA-liver hepatocellular carcinoma (LIHC) patients were analyzed via Pearson correlation analysis, identifying transcripts with significant co-expression patterns. This integrative approach facilitated the discovery of putative MCRS1-mediated pathways in hepatocarcinogenesis.

Genomic alteration and mutational burden analyses

The cBioPortal resource (https://www.cbioportal.org), an online cancer genomics interface consolidating TCGA and International Cancer Genome Consortium datasets (22), was employed to retrieve somatic mutation profiles. MCRS1-associated genomic alterations (mutations, amplifications, and deletions) were assessed across malignancies using the platform’s Cancer Type Summary module (23). Concurrently, SangerBox 3.0 facilitated quantitative evaluation of MCRS1 expression correlations with five genomic instability indices: tumor mutational burden (TMB), microsatellite instability (MSI), homologous recombination deficiency (HRD), mutant-allele tumor heterogeneity (MATH), and neoantigen load (NEO).

DNA mismatch repair (MMR), stemness, and epigenetic modification analyses

Multidimensional analyses were conducted to explore MCRS1’s interplay with DNA repair and epigenetic regulators. Co-expression networks between MCRS1 and (I) five MMR genes, (II) four DNA methyltransferases (DNMTs) (24), and (III) 30 homologous recombination repair (HRR) genes from ARIEL3 (25) were visualized through GEPIA2. Stemness indices (DMPsi) (26) and RNA methylation regulators (N1-methyladenosine/5-methylcytosine/N6-methyladenosine) (27) were further analyzed via correlation heatmaps to identify MCRS1-associated epigenetic signatures.

Single cell RNA-sequencing (scRNA-seq) data analysis

Tumor Immune Single-cell Hub 2 (TISCH2) (28) (http://tisch.compbio.cn/gallery/?cancer=LIHC&species=Human), a curated repository of TME single-cell transcriptomes from GEO/ArrayExpress, was interrogated to resolve MCRS1 expression heterogeneity at cellular resolution. Following standardized preprocessing (quality control, batch correction, cluster annotation), MCRS1 expression patterns were mapped across cancer subtypes, cellular subpopulations, and clinical cohorts to elucidate its TME-specific roles.

Pan-cancer analyses of the immunological roles of MCRS1

The TIMER (http://timer.cistrome.org), an analytical framework for deconvoluting tumor-immune microenvironment dynamics (29), was employed to investigate MCRS1 immunomodulatory functions. Through TIMER’s computational algorithms, we evaluated MCRS1 transcriptional profiles in HCC and their spatial correlations with six tumor-infiltrating leukocyte populations: neutrophils, DCs, CD8⁺ T lymphocytes, CD4⁺ T lymphocytes, B lymphocytes, and macrophages. To account for tumor purity confounders, partial Spearman correlation analysis with purity adjustment was performed, with results visualized through regression scatterplots. Furthermore, we systematically examined associations between MCRS1 expression and 28 consensus immune cell marker genes using purity-corrected Spearman’s Rho coefficients (FDR <0.05).

Spatial transcriptomic sections

Spatial transcriptomic data from SpatialDB (30) (https://www.spatialomics.org/SpatialDB/search.php) enabled spatial mapping of MCRS1 co-expression with macrophage biomarkers (CD68, CD163) in BRCA and melanoma specimens. Coordinate-based analysis identified colocalization patterns between MCRS1 transcripts and immune cell niches.

Experimental methods

Detailed experimental protocols are provided in Appendix 1.

Statistical analysis

Survival analyses employed Kaplan-Meier curves with log-rank testing or Cox regression. Correlation strength was quantified using Pearson/Spearman coefficients (|Rho| ≥0.3 considered biologically relevant). MCRS1-DNMT associations required concordant trends (P<0.05) in ≥2/4 DNMTs without contradictory patterns. The results of the experiment were expressed as mean ± standard deviation (SD). GraphPad Prism 8 was used for statistical analysis. Comparisons between two groups were made using t-test, and one-way analysis of variance (ANOVA) was used for groups of three or more. Significance thresholds: *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; ns, non-significant.

Results

MCRS1 expression in pan-cancer and HCC

Pan-cancer transcriptomic profiling integrating TCGA datasets revealed differential MCRS1 expression across malignancies (Figure 1A). Tumor-specific overexpression was identified in 24 cancer types (GBM, GBMLGG, LGG, UCEC, BRCA, KIRP, COAD, COADREAD, STAD, HNSC, LUSC, HCC, WT, SKCM, BLCA, READ, OV, PAAD, TGCT, ALL, LAML, ACC, KICH, and CHOL), whereas significant downregulation occurred in five tumor entities including STES, KIPAN, PRAD, KIRC, and THCA (Figure 1A). Figure 1B shows the differential mRNA expression of MCRS1 in HCC and normal tissues.

Figure 1 MCRS1 expression in pan-cancer and particularly in HCC. (A) MCRS1 mRNA expression level across tumor and normal tissues through integrated analysis of TCGA datasets. Mean ± SD; GBM (P=5.5e–58), GBMLGG (P=8.5e–160), LGG (P=2.4e–130), CESC (P=0.08), UCEC (P=4.2e–7), BRCA (P=7.0e–10), KIRP (P=1.7e–15), KIPAN (P=0.03), COAD (P=4.9e–25), COADREAD (P=6.1e–28), STAD (P=7.4e–7), HNSC (P=5.9e–10), LUSC (P=1.9e–9), LIHC (P=5.3e–32), WT (P=3.8e–17), BLCA (P=6.1e–5), READ (P=8.6e–3), PAAD (P=1.6e–45), TGCT (P=4.2e–3), KICH (P=3.0e–4), CHOL (P=2.3e–9), ESCA (P=6.6e–4), STES (P=6.0e–8), PRAD (P=1.1e–18), KIRC (P=2.4e–19), THCA (P=1.5e–62). (B) HCC-specific MCRS1 mRNA expression level assessed across TCGA databases. Mean ± SD; P=1.62447832963153E-12. (C) MCRS1 protein expression in cancer vs. normal specimens using the UALCAN platform. Mean ± SD; BRCA (P=6.361298e–04), OV (P=1.265894e–01), KIRC (P=2.11917944802764e–07), UCEC (P=5.206226e–04), LUAD (P=1.53435674094086e–06), PAAD (P=2.548090e–03), HNSC (P=7.793184e–01), GBM (P=2.90204403718391e–08), HCC (P=1.921888e–02). (D) HCC tissue-specific protein quantification of MCRS1 interrogated via UALCAN. Mean ± SD; P=1.921888e–02. (E) IHC confirmation of MCRS1 expression in HCC (image available from https://www.proteinatlas.org/ENSG00000187778-MCRS1/cancer/liver+cancer#img) vs. matched normal tissues (image available from https://www.proteinatlas.org/ENSG00000187778-MCRS1/tissue/liver#img) through Human Protein Atlas. (F-I) Pan-cancer survival framework delineating prognostic associations of MCRS1 expression level with clinical outcomes. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001; -/ns, no significance. CI, confidence interval; HCC, hepatocellular carcinoma; IHC, immunohistochemistry; LIHC, liver hepatocellular carcinoma; mRNA, messenger RNA; SD, standard deviation; TCGA, The Cancer Genome Atlas; UALCAN, University of Alabama at Birmingham Cancer.

Multi-omics validation through UALCAN confirmed elevated MCRS1 protein levels in BRCA, KIRC, LUAD, HNSC, GBMLGG, and HCC (Figure 1C,1D). Spatial proteomic analysis via HPA demonstrated striking tissue-specific regulation: normal hepatic tissues showed minimal MCRS1 immunoreactivity, contrasting with intense cytoplasmic staining in HCC specimens (Figure 1E). This consistent overexpression pattern across transcriptional and translational levels suggests MCRS1 may function as an oncogenic driver in HCC pathogenesis.

Survival meta-analysis using TCGA pan-cancer data identified MCRS1 as a negative prognostic indicator across multiple endpoints. Elevated expression correlated with reduced OS in 11 malignancies (GBMLGG, LAML, HCC, ACC, ALL-R, LGG, BRCA, SKCM, MESO, UVM, and ALL), particularly in high-grade gliomas (GBMLGG HR =2.32, P=1.8e−5) (Figure 1F). Multidimensional survival assessment further revealed significant associations between MCRS1 upregulation and adverse disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) across multiple tumor types (Figure 1G-1I). The prognostic valuation of MCRS1 transcriptional profiles in HCC was systematically quantified through Kaplan-Meier plotter platform of clinicopathological subgroups, incorporating four survival endpoints (OS, disease-free survival, recurrence-free survival, and DSS) as detailed in Table S1.

MCRS1 expression distribution in subtypes of distinct clinical features

Multi-dimensional clinicopathological characterization through UALCAN revealed MCRS1 dysregulation across HCC subtypes. Compared to normal parenchyma, significant MCRS1 overexpression was observed in HCC, fibrolamellar carcinoma, and mixed hepatocholangiocarcinoma (Figure 2A). Systematic stratification by clinicodemographic variables demonstrated consistent MCRS1 elevation in HCC tissues across all subgroups (Figure 2B-2G). Stage-specific correlation analysis revealed a progressive elevation in MCRS1 TPM with advanced tumor stages, demonstrating a quasi-linear relationship between pathological progression and transcriptional abundance (Figure 2B). Ethnic disparities emerged with higher MCRS1 levels in Caucasian and Asian populations versus African-American cohorts (Figure 2C). Gender-dependent upregulation was confirmed (female: 30 TPM; male: 25 TPM), while age-stratified analysis revealed characteristic bimodal distribution, peaking in 81–100 years old and lowest TPM in 60–80 years (Figure 2D,2E). Notably, MCRS1 expression intensity was linearly correlated with the degree of tumor grade (Figure 2F). Additionally, MCRS1 expression intensity was also associated with the nodal metastasis status of HCC (Figure 2G). This clinicopathological gradient suggests MCRS1 may serve as a biomarker for HCC biological aggressiveness.

Figure 2 Clinicopathological stratification of MCRS1 expression in HCC. MCRS1 expression were systematically evaluated across seven clinical variables: (A)​histological subtypes: normal vs. hepatocellular carcinoma, P=1.11022302462516e–16; normal vs. fibrolamellar carcinoma, P=8.644800e–04; normal vs. hepatocholangiocarcinoma (mixed)​, P=6.69140000075785e–08. (B)​Individual staging: normal vs. stage 1, P<1e–12​; normal vs. stage 2, P=1.62447832963153e–12; normal vs. stage 3, P=1.62436730732907e–12; normal vs. stage 4, P=4.379800e–03. (C)​Ethnic cohorts: normal vs. Caucasian, P<1e–12; normal vs. African American, P=1.56232999999339e–05; normal vs. Asian, P<1e–12. (D)​Biological sex: normal vs. male, P=1.62447832963153e–12; normal vs. female, P<1e–12. (E)​ Patient’s age: normal vs. age (21–40 years), P=1.01039999700347e–08; normal vs. age (41–60 years), P<1e–12; normal vs. age (61–80 years), P=1.62436730732907e–12; normal vs. age (81–100 years), P=5.849200e–04. (F)​Tumor grade: normal vs. grade 1, P=8.04856181702007e–12; normal vs. grade 2, P<1e–12; normal vs. grade 3, P=1.62436730732907e–12; normal vs. grade 4, P=5.64969999999887e–05. (G)​Lymph node metastasis status: normal vs. N0, P<1e–12; normal vs. N1, P=4.659500e–02. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma; TCGA, The Cancer Genome Atlas.

Comprehensive genomic characterization of MCRS1 across malignancies

Integrative pan-cancer analysis was conducted to elucidate the genomic landscape of MCRS1 alterations. Cross-tumor examination of somatic variations across 32 cancer types revealed distinct copy number variations (CNVs). Amplification events clustered predominantly in DLBC, SARC, ACC, TGCT, and ESCA, whereas profound deletions characterized LGG and PAAD (Figure 3A,3B). The mutational spectrum in HCC demonstrated significant co-occurrence between MCRS1 overexpression and somatic mutations in 15 cancer-associated genes, including key tumor suppressors (TP53, RB1) and chromatin regulators (ZNF91, DIP2C) (Figure 3C).

Figure 3 MCRS1 expression is associated with genomic instability. (A)​Pan-cancer genomic alteration of MCRS1 (mutations, amplifications, deep deletions) profiled via TCGA. (B)​ Pan-cancer SNV topography of MCRS1, highlighting missense, frameshift deletion, and splice site mutations. (C)​The associations between MCRS1 expression and somatic mutations in 15 most frequently altered genes. (D-H)​ Multi-parametric biomarker correlations between MCRS1 expression and TMB (D), MATH (E), MSI (F), NEO (G), and HRD (H), quantified via lollipop charts. CNA, copy number alteration; FHA, forkhead-associated domain; HRD, homologous recombination deficiency; MATH, mutant-allele tumor heterogeneity; MSI, microsatellite instability; NEO, neoantigen load; SNV, single nucleotide variation; TCGA, The Cancer Genome Atlas; TMB, tumor mutational burden; VUS, variant of uncertain significance.

To establish MCRS1’s role in genomic instability, we systematically evaluated its associations with four established biomarkers: TMB, MATH, MSI, NEO, and HRD (31,32). TMB analysis demonstrated positive associations in TGCT and PCPG, whereas negative associations were observed in LAML (Figure 3D). MATH scores showed significant positive correlation with tumor progression metrics in the ECSA (Figure 3E). MSI quantification exhibited tissue-specific patterns, revealing positive associations across KIPAN (Figure 3F). NEO antigen load displayed divergent correlations, with weak positive correlation in SKCM contrasting with weak negative correlation in KICH (Figure 3G). HRD evaluation identified distinct tissue-context associations, showing positive correlation in HCC but negative correlation in CHOL (Figure 3H). To understand the regulatory mechanisms controlling MCRS1 levels, we investigated the relationship between its gene copy number and mRNA expression across multiple cancer types from the TCGA cohort. We found a strong dosage effect, where copy number loss was significantly associated with reduced expression, and copy number gain was associated with increased expression (Figure S1). This trend was consistent across most cancers, including those with high frequencies of MCRS1 deletion such as BRCA and OV, indicating that genomic loss is a primary mechanism for MCRS1 under expression in tumors. This multidimensional profiling establishes MCRS1 as a context-dependent regulator of genomic instability, with particularly strong clinical implications in hepatobiliary malignancies. The differential correlation patterns across cancer types suggest tissue-specific molecular interactions warranting further investigation.

MCRS1 modulates genomic maintenance and stemness through epigenetic networks

The preservation of chromosomal integrity relies on sophisticated signaling networks that detect and resolve DNA lesions through coordinated repair mechanisms (33). Tumorigenesis frequently exploits these pathways, particularly through dysregulated MMR and HRR systems (34,35), processes that drive therapeutic resistance and stem cell-like adaptation. Our integrated multi-omics approach revealed MCRS1’s differential involvement in three hallmarks of malignant transformation: MMR efficiency, HRR capacity, and cellular stemness indices. Tissue-specific analyses identified inverse MCRS1-MMR correlations in UCS and READ (Figure 4A).

Figure 4 MCRS1 is associated with DNA repair, epigenetic modifications, and stemness. (A) The heatmap of the associations between MCRS1 and 5 MMR genes across different cancer types. (B) The relationship between the HRR characteristics of 30 genes and the expression level of MCRS1 in LIHC, KIRP, and PRAD. (C) A lollipop chart shows the correlations between MCRS1 level and stemness. Stemness index (mDNAsi) was calculated as described by Malta et al. (Cell, 2018) (26). (D) The heatmap of the correlations between MCRS1 and 4 DNMTs. (E) The heatmap of the correlations between pan-cancer RNA modulations and MCRS1 level. *, P<0.05. DNMTs, DNA methyltransferases; HRR, homologous recombination repair; LIHC, liver hepatocellular carcinoma; mDNAsi, DNA methylation-based stemness index; MMR, mismatch repair; TPM, transcripts per million.

Conversely, MCRS1 demonstrated positive HRR associations in ACC, HNSC, LGG, HCC, OV, READ, STAD, TGCT, and THYM (Figure S2). Critically, to determine whether these associations were driven by MCRS1’s biological function rather than by confounding genomic instability, we leveraged cancer types where MCRS1 copy number is predominantly neutral, thereby isolating expression-regulated mechanisms. Strikingly, in LIHC, PRAD, and KIRP—where MCRS1 alterations are exceedingly rare—the positive correlation between MCRS1 expression and HRR signature scores remained robust and statistically significant (Figure 4B). This key finding demonstrates that the relationship between MCRS1 and HRR is independent of CNVs, underscoring a direct and likely functional role for MCRS1 in modulating DNA repair capacity. Additionally, stemness evaluation uncovered context-dependent relationships, with MCRS1 expression enhancing stem-like phenotypes in UVM, LGG, and GBMLGG, while suppressing these characteristics in THYM (Figure 4C).

Epigenetic reprogramming has emerged as a critical driver of tumor evolution, with chromatin-modifying enzymes representing prime targets for pharmacological intervention (36). DNMTs, the principal mediators of cytosine-phosphate-guanine methylation, coordinate fundamental carcinogenic processes including differentiation blockade and proliferative signaling (37). Our pan-cancer mapping revealed MCRS1’s tissue-selective coordination with DNMT networks: strong positive correlations dominated in UCEC, OV, MESO, HCC, LGG, ESCA, CESC, BRCA, and ACC, whereas inverse relationships marked CHOL (Figure 4D). Further exploration identified MCRS1’s pan-cancer synergy with RNA splicing machinery, suggesting dual regulatory capacity in epigenetic modulation and transcript processing (Figure 4E). These findings position MCRS1 as a bifunctional epigenetic regulator capable of shaping both DNA methylation landscapes and RNA processing dynamics during oncogenesis.

MCRS1 promoter methylation analysis in HCC

Building upon the observed MCRS1 overexpression in HCC, we investigated the epigenetic underpinnings of this dysregulation through systematic promoter methylation analysis. Utilizing the UALCAN platform for high-resolution methylation profiling, we interrogated the epigenetic landscape of the MCRS1 promoter region—a critical regulatory domain where aberrant DNA methylation patterns frequently drive transcriptional dysregulation and oncogenic progression (38). Comparative methylation analysis revealed a hypomethylation at the MCRS1 promoter in HCC tissues relative to adjacent normal hepatocytes (Figure 5A). This hypomethylation is unlikely to be a technical artifact of copy number deletion, as the MCRS1 locus is predominantly copy-neutral in LIHC. To delineate the clinical relevance of this epigenetic alteration, we performed stratified analyses across clinicopathological dimensions. Our analysis revealed that hypomethylation was progressively more pronounced in advanced HCC stages (Figure 5B). Demographic assessments demonstrated racial disparities, with the most pronounced hypomethylation observed in Asian cohorts (Figure 5C). A female-biased magnitude of methylation loss was also evident (Figure 5D), alongside an inverse age-related dynamic (Figure 5E). Further analysis within the HCC cohort revealed a positive correlation between methylation density and tumor grade (Figure 5F), suggesting that while global promoter hypomethylation is a fundamental driver of MCRS1 activation, the methylation level is not static and may be influenced by other factors during disease progression. Specifically, grade-stratified analysis demonstrated that hypomethylation was evident in Grade 1, 2, and 3 tumors compared to normal tissue, whereas Grade 4 tumors exhibited methylation levels not significantly different from normal tissue (Figure 5F), suggesting potential complex dynamics in late-stage disease. These multi-parametric analyses collectively establish promoter hypomethylation (relative to normal tissue) as a key driver of MCRS1 transcriptional activation in HCC.

Figure 5 MCRS1 gene promoter methylation levels in HCC. (A) Sample type: normal vs. primary, P=1.4833999999464e–06. (B) Individual cancer stages: normal vs. stage 1, P=7.84799999999919e–05; normal vs. stage 2, P=1.67129999995019e–06; normal vs. stage 3, P=3.049000e–04; normal vs. stage 4, P=2.696300e–02. (C) Race: normal vs. Caucasian, P=9.51760000034163e–07; normal vs. African American, P=2.121800e–01; normal vs. Asian, P=1.754900e–04. (D) Gender: normal vs. male, P=1.64103999999998e–05; normal vs. female, P=3.66419999997536e–06. (E) Age: normal vs. age (21–40 years), P=8.120700e–03; normal vs. age (41–60 years), P=7.416000e–04; normal vs. age (61–80 years), P=6.13119999992584e–07; normal vs. age (81–100 years), N/A. (F) Grade: normal vs. grade 1, P=5.9952999999946e–05; normal vs. grade 2, P=1.60321000000074e–05; normal vs. grade 3, P=1.760560e–04; normal vs. grade 4, P=2.371200e–01. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. HCC, hepatocellular carcinoma; LIHC, liver hepatocellular carcinoma; N/A, not applicable; ns, no significance; TCGA, The Cancer Genome Atlas; yrs, years.

MCRS1 is associated with immune cell infiltration

Pan-cancer immunogenomic profiling employing Spearman correlation analysis revealed extensive MCRS1-immune infiltration associations across 38 malignancies, with HCC, THYM, PCPG, and THCA showing particularly robust correlations (Figure 6A). Advanced subtyping analysis through Tumor Immune System Interaction Database (TISIDB) demonstrated MCRS1’s differential expression patterns across six immunological classifications in 30 cancer types, indicating potential immune subtype-specific regulatory mechanisms (Figure 6B). Given the therapeutic relevance of macrophage polarization in tumor immunology (39), we specifically investigated MCRS1’s association with macrophage subsets. TIMER2.0 analysis revealed marked positive correlations between MCRS1 expression and M2-polarized macrophage infiltration, contrasting with weaker M1 subtype associations, suggesting preferential involvement in pro-tumorigenic immune modulation (Figure 6C). In HCC, comprehensive TIMER database interrogation demonstrated significant MCRS1 covariation with diverse immune populations: strongest correlations emerged with myeloid DCs (P=7.87e−30, Rho =0.560), followed by B cells (P=6.0e−18, Rho =0.442), macrophages (P=1.90e−16, Rho =0.423), T cell CD4⁺ (P=4.42e−15, Rho =0.405), and neutrophil (P=1.90e−05, Rho =0.232), while CD8⁺ T cell associations remained nonsignificant (P=4.07e−01, Rho =0.045) (Figure 6D). Spatial transcriptomic validation via Spatial DB confirmed anatomical colocalization of MCRS1 with the pan-macrophage marker CD68 in BRCA and melanoma specimens, providing histological evidence for their functional interplay within tumor niches (Figure 6E). These multi-platform findings position MCRS1 as a novel regulator of tumor immune microenvironments, potentially mediating immune evasion through macrophage polarization and coordinated modulation of antigen-presenting cell populations.

Figure 6 The association between MCRS1 expression with immune cell infiltration patterns in cancers, particularly in HCC. (A) Pan-cancer analysis that uncovers the correlations of MCRS1 with six subtypes of immune cells in TME. (B) Comparative expression profiles of MCRS1 across immunological subtypes in 30 cancers, which are obtained from TISIDB. (C) An assessment by TIMER 2.0 of the relationships between macrophage infiltration and MCRS1 expression in various tumor types. (D) Specific associations in HCC between MCRS1 levels and six populations of tumor-infiltrating immune cells, analyzed using TIMER 2.0. (E) Spatial transcriptomic mapping that shows the colocalization patterns of MCRS1 and CD68 (a marker for macrophages), where color gradients signify the relative intensities of gene expression. *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. DC, dendritic cell; HCC, hepatocellular carcinoma; HPV, human papillomavirus; LIHC, liver hepatocellular carcinoma; TCGA, The Cancer Genome Atlas; TIMER, Tumor Immune Estimation Resource; TISIDB, Tumor Immune System Interaction Database; TME, tumor microenvironment; TPM, transcripts per million.

MCRS1 gene co-expression network and gene set enrichment analysis

To delineate MCRS1’s mechanistic roles in HCC, we conducted systematic co-expression mapping via the LinkedOmics platform, revealing 13,530 genes with significant expression covariation (8,092 positively correlated, 5,438 negatively correlated) (Figure 7A). Hierarchical clustering of the top 50 co-expressed genes (Figure 7B,7C) highlighted distinct prognostic clusters. Striking positive correlations emerged with nucleotide metabolism regulators (NUDT1, r=0.728, P=1.75e−58), mitochondrial ATP synthase components (ATP5G2, r=0.694, P=9.38e−51), and spliceosomal factors (SNRPA, r=0.693, P=9.38e−51), while inverse associations featured circadian rhythm modulators (RORA, r=−0.665, P=1.98e−45) and epigenetic writers (KAT2B, r=−0.665, P=2.29e−45). Protein interactome analysis identified functional partnerships with chromatin remodelers (KANSL3, KANSL2) and histone modification enzymes (PHF20) (Figure 7D), suggesting involvement in transcriptional regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed MCRS1’s predominant association with ribosomal biogenesis, spliceosomal machinery, and branched-chain amino acid catabolism (Figure 7E), implicating dual roles in metabolic reprogramming and post-transcriptional control. Survival stratification based on the expression of these top co-expressed genes revealed two clinically distinct patient subgroups (Figure 7F,7G). Notably, the MCRS1-associated network was enriched with prognostic genes: 11 out of the 50 positively correlated genes (e.g., NUDT1, SNRPA) were associated with poor survival outcomes, while 12 out of the 50 negatively correlated genes (e.g., RORA, AR) were linked to favorable prognosis. This pattern underscores that the MCRS1-centered co-expression network defines a molecular signature with significant prognostic value in HCC, although the precise hierarchical relationships within this network warrant further investigation.

Figure 7 The interaction network of MCRS1 and its functional enrichment profiling in HCC. (A) Co-expression patterns of MCRS1 in HCC identified by Pearson correlation analysis. Nodes colored green or red represent genes with significant negative or positive co-expression, respectively. (B,C) The hierarchically clustered heatmaps of the top 50 upregulated and downregulated genes associated with MCRS1 expression in HCC. (D) The MCRS1 protein interaction network derived from STRING. (E) KEGG pathway enrichments related to MCRS1 expression in HCC. (F,G) The prognostic heatmaps of the correlations of the 50 genes that have the strongest positive and negative regulatory relationships with MCRS1 in HCC. FDR, false discovery rate; KEGG, Kyoto Encyclopedia of Genes and Genomes; HCC, hepatocellular carcinoma; HR, hazard ratio; LIHC, liver hepatocellular carcinoma.

scRNA-seq data reveals MCRS1’s immunomodulatory role in HCC

We conducted high-resolution scRNA-seq analysis of 5,059 cells from six HCC specimens (LIHC_GSE98638), employing nonlinear dimensionality reduction [t-distributed stochastic neighbor embedding (t-SNE)] to resolve 15 transcriptionally distinct cellular clusters (Figure 8A). Spatial probability mapping demonstrated preferential MCRS1 localization within Cluster 13 (“Others”) (Figure 8B,8C), while quantitative expression analysis identified T proliferative cells (Tprolif) as the primary MCRS1-expressing population (Figure 8D). Prognostic meta-analysis revealed context-dependent clinical associations: MCRS1 expression correlated with increased mortality risk in ACC, LAML, and SKCM, yet paradoxically conferred protection in KIRP (Figure 8E). Immune landscape characterization uncovered coordinated enrichment of MCRS1 with immunosuppressive mediators in Cluster 13, including angiogenic factor VEGFA, microglial regulator TREM2, and fibrogenic cytokine TGFB1 (Figure 8F). This finding reinforces MCRS1’s linkage to immune cell infiltration while suggesting its potential function in remodeling the immune landscape of HCC.

Figure 8 Exploring the mechanism of MCRS1 in HCC by scRNA-seq analysis. (A) Fifty clusters were shown visually through t-SNE algorithm. Clusters 0, 3, and 8 were annotated by the TISCH2 database as malignant tumor cells based on canonical markers. (B,C) The expression of MCRS1 in 15 clusters. The highest expression of MCRS1 is localized to the tumor cell clusters (0, 3, 8). (D) Quantitative results of cross-cellular expression heterogeneity of MCRS1. (E) Survival meta-analysis of MCRS1 in pan-cancer. (F) Immune profiling with co-enrichment of MCRS1 and immunosuppressive genes. Color scale represents the normalized expression level of genes across individual cells, with blue indicating low or no expression and red indicating high expression. CD4Tconv, conventional CD4⁺ T cell; CD8Tex, exhausted CD8⁺ T cell; HCC, hepatocellular carcinoma; HR, hazard ratio; LIHC, liver hepatocellular carcinoma; scRNA-seq, single-cell RNA-sequencing; t-SNE, t-distributed stochastic neighbor embedding; TPM, transcripts per million; Tprolif, T proliferative cells; Treg, regulatory T cell.

Knocking down MCRS1 mitigates the malignant phenotype of tumor cells

Molecular validation through western blotting confirmed efficient MCRS1 silencing in HCC cell lines (Figure 9A). siRNA#2 was used to transfect SNU449 cells, and siRNA#3 was used to transfect MHCC97H cells before subsequent functional experiments. To comprehensively assess the functional impact of MCRS1, we first evaluated its effect on cell proliferation. Cell Counting Kit-8 (CCK-8) assays demonstrated that MCRS1 knockdown significantly impaired the proliferation of both SNU449 and MHCC97H cells compared to the negative control groups (Figure 9B). We next investigated whether MCRS1 influences metastatic phenotypes. Functional assays demonstrated MCRS1’s critical role in driving malignant phenotypes: colony formation capacity decreased by 61.7% in SNU449 cells (P<0.01) and 64.1% in MHCC97H cells (P<0.001) (Figure 9C), while wound healing assays revealed a 71.1% reduction in SNU449 cells (P<0.05) and a 51.1% reduction in MHCC97H cells (P<0.01) in migration rates (Figure 9D). Transwell invasion assays showed dramatic suppression of invasive potential, with penetrating cells reduced by 67.9% in SNU449 cells and 47.4% in MHCC97H cells, respectively (P<0.01) (Figure 9E). The reduction in migratory and invasive capacity may be attributable to both the decrease in proliferative potential and a potential direct role of MCRS1 in regulating these processes.

Figure 9 Experimental validation of the role of MCRS1 in HCC cell lines. (A) The knockdown efficiency of MCRS1 in SNU449 and MHCC97H cells. SNU449 cell: mean ± SD, n=3, one-way ANOVA; control vs. si-NC, P=0.8580; si-NC vs. si-RNA#1, P=0.0218; si-NC vs. si-RNA#2, P=0.0073; si-NC vs. si-RNA#2, P=0.0085; MHCC97H cell: mean ± SD, n=3, one-way ANOVA; control vs. si-NC, P=0.09305; si-NC vs. si-RNA#1, P=0.0003; si-NC vs. si-RNA#2, P=0.0001; si-NC vs. si-RNA#2, P<0.0001. After transfecting MCRS1 siRNAs, CCK-8 (B) and the colony formation assay (crystal violet staining; scale bar, 100 µm) (C) were used to assess the proliferation capacity of SNU449 and MHCC97H cells. SNU449 cell: mean ± SD, n=3, P=0.0003, t-test; MHCC97H cell: mean ± SD, n=3, P=0.0002, t-test (B); SNU449 cell: mean ± SD, n=3, P=0.0226, t-test; MHCC97H cell: mean ± SD, n=3, P<0.0004, t-test (C). (D) The scratch test was carried out to measure the migration ability of SNU449 and MHCC97H cells (scale bar, 200 µm). SNU449 cell: mean ± SD, n=3, P=0.0108, t-test; MHCC97H cell: mean ± SD, n=3, P=0.0037, t-test. (E) Transwell invasion assays were conducted on SNU449 and MHCC97H cells in the control and si-MCRS1 groups (crystal violet staining; scale bar, 50 µm). SNU449 cell: mean ± SD, n=3, P=0.0034, t-test; MHCC97H cell: mean ± SD, n=3, P=0.0152, t-test. *, P<0.05; **, P<0.01; ***, P<0.001. ANOVA, analysis of variance; CCK-8, Cell Counting Kit-8; HCC, hepatocellular carcinoma; NC, negative control; OD, optical density; SD, standard deviation.

Discussion

Numerous investigations have delineated the multifaceted functionality of MCRS1, demonstrating its critical involvement in cellular processes spanning (40): (I) transcriptional regulation of cellular proliferation and stress responses; (II) epigenetic modulation through histone posttranslational modifications; (III) mRNA processing and translational control; (IV) telomerase activity regulation and senescence programming; (V) mTOR signaling pathway mediation; and (VI) structural maintenance of mitotic apparatus including centrosome integrity and microtubule network organization. Emerging evidence positions this protein as a specialized modulator of oncogenic phenotypes in specific malignancies including COADREAD, LUAD, and LGG (13,14,41). To extend these findings, we executed a comprehensive pan-cancer investigation to delineate MCRS1’s expression patterns, prognostic significance, and mechanistic roles in tumor biology. The present study provides the first comprehensive pan-cancer analysis of MCRS1, integrating multi-omics datasets, single-cell resolution, and functional validation to delineate its oncogenic roles in HCC and other malignancies. Our findings establish MCRS1 as a critical driver of tumor progression through its involvement in genomic instability, epigenetic reprogramming, and immune microenvironment remodeling, while also providing novel insights into the molecular mechanisms of MCRS1-mediated tumorigenesis and its therapeutic potential.

Our pan-cancer transcriptomic profiling identified MCRS1 overexpression in 24 malignancies, including HCC, with significant associations to advanced tumor stages, poor differentiation, and adverse survival outcomes. This aligns with prior reports of MCRS1’s oncogenic roles in colorectal and lung cancers, where it drives proliferation and metastasis via Cyclin D1/CDK4-p21 and EMT pathways, respectively (13,14). However, our findings contrast with its tumor-suppressive function in GC (15). Such tissue-specificity may stem from differential interactions with epigenetic modifiers. Notably, although MCRS1 mRNA expression levels in OV, UCEC, and PAAD were higher than those in corresponding normal tissues, the protein expression of MCRS1 was significantly lower in these three cancer types compared to normal tissues. This phenomenon may be associated with post-transcriptional and post-translational modifications of MCRS1. In this study, we observed correlations between MCRS1 and multiple RNA modification-related genes, suggesting that the discrepancy between MCRS1 mRNA and protein expression levels might result from post-transcriptional modifications. Additionally, post-translational modifications could also contribute to such mRNA-protein expression discordance. As previously reported, the regulation of TEM8 protein by the E3 ubiquitin ligase ASB10 in triple-negative breast cancer tissues led to higher TEM8 protein levels compared to other breast cancer subtypes, while its transcriptional levels were lower than those in the luminal BRCA subtype (42). Nevertheless, extensive experimental validation is required to determine whether MCRS1 is regulated by post-translational modifications. This provides direction for subsequent investigations into the mechanistic role of MCRS1 in cancer.

Genomic instability, a hallmark of cancer, plays a pivotal role in disease progression and therapeutic resistance. Our study shows that MCRS1 expression correlates with various genomic instability indices, including TMB, MSI, and HRD. These associations suggest that MCRS1 may contribute to the genomic instability observed in HCC and other cancers. This is further supported by the identification of MCRS1 genomic alterations, such as amplifications and deletions, across different cancer types. These findings have important implications for understanding the molecular basis of MCRS1-mediated oncogenesis and its potential role in driving therapeutic resistance.

DNA methylation, a canonical epigenetic modification, exerts precise control over gene expression; its disruption can induce genomic instability, facilitate malignant transformation, and promote tumor-adaptive phenotypes such as unchecked proliferation (43). Promoter hypomethylation is a hallmark of oncogene activation, as seen in MYC and LINE-1 retrotransposons, which drive genomic instability and tumor evolution (37). A pivotal finding of our study is the hypomethylation of the MCRS1 promoter in HCC, correlating with transcriptional upregulation and aggressive clinicopathological features. We find that MCRS1 hypomethylation may synergize with DNMT dysregulation, given its strong co-expression with DNMT3A/B in HCC. This parallels recent reports implicating DNMTs in maintaining stemness and chemoresistance in HCC (44). The robust inverse correlation between methylation density and MCRS1 mRNA abundance further substantiates DNA demethylation as a primary regulatory mechanism. Notably, the stage-dependent methylation erosion suggests a role for MCRS1 epigenetic dysregulation in HCC progression, potentially enabling selective advantages during metastatic evolution. Additionally, we observed a female-biased magnitude of hypomethylation and ethnic disparities, potentially linked to hormonal signaling or environmental exposures. This finding is supported by studies demonstrating sex-divergent DNA methylation profiles that are distinct from canonical X-chromosome inactivation mechanisms (45), highlighting the role of epigenetic regulation in sexual dimorphism. Critically, while the epigenetic change (hypomethylation) was more pronounced in females, the oncogenic consequences of MCRS1 overexpression—as evidenced by its strong correlation with advanced stage, poor differentiation, and poor survival—were most significant in clinical contexts where HCC is most prevalent and aggressive, which often align with male-dominated populations. The stage-dependent methylation erosion further suggests a role for MCRS1 epigenetic dysregulation in HCC progression. These findings underscore the complex interplay between epigenetics, tumor biology, and demography. They suggest that strategies aimed at counteracting the downstream effects of MCRS1 activation (rather than directly targeting its methylation state) could be particularly beneficial in high-risk subgroups.

Compared to pro-tumor M2 macrophages, M1 macrophages exhibit stronger antitumor activity, suggesting that therapeutic strategies targeting macrophage repolarization from M2 to M1 phenotypes could enhance clinical efficacy (24). Preclinical studies have demonstrated that shifting macrophage polarization toward the M1 state effectively inhibits tumor progression in BRCA models by hindering tumor vascularization and reducing BRCA growth (46). Our immunogenomic analyses reveal MCRS1’s robust association with M2 macrophage infiltration and myeloid DC abundance in HCC. M2 macrophages are key architects of immunosuppressive microenvironments, secreting IL-10 and TGF-β to inhibit cytotoxic T-cell activity and promote angiogenesis (39). Our findings suggest that MCRS1 is closely associated with CD4⁺ T cell infiltration but shows no significant correlation with CD8⁺ T cells​. As is well-known, CD4⁺ T cells, as a type of helper T cell, can enhance the anti-tumor effects of CD8⁺ T cells (47). However, our findings suggest that MCRS1 is closely associated with CD4⁺ T cell infiltration but shows no significant correlation with CD8⁺ T cells. This observation was further confirmed through subsequent scRNA-seq data analysis. Our discovery may partially explain why many T-cell enriched tumors fail to respond to ICIs therapy. Our spatial transcriptomic and scRNA-seq analyses position MCRS1 within an immunosuppressive niche, characterized by its co-localization with macrophages and enrichment in T-cell clusters expressing VEGFA and TGFB1. This strong association suggests a potential link between MCRS1 overexpression and the establishment of an immune-resistant TME, which could provide a plausible explanation for immunotherapy resistance in HCC. However, future functional studies using co-culture models and MCRS1-knockout in vivo systems are required to definitively establish a causal role for MCRS1 in driving macrophage polarization and immune evasion. The spatial colocalization of MCRS1 with CD68⁺ macrophages in tumor niches further supports its role in immune evasion. Intriguingly, scRNA-seq data linked MCRS1 to Tprolif clusters enriched with TGFB1 and VEGFA, cytokines known to foster regulatory T-cell expansion and vascular permeability (48). These observations resonate with recent studies identifying M2 polarization as a predictor of anti-PD-1 resistance in HCC (49). Thus, it is imperative to identify potential MCRS1 inhibitors of which its combination with existing therapies may enhance tumor immunotherapeutic sensitivity.

Co-expression network analysis revealed a robust positive association between MCRS1 and NUDT1, a 18-kDa naked pyrophosphatase (50) required for RAS/ROS-mediated transformation and pivotal in sustaining cellular viability (51). A recent study indicated that HIF2α mitigates oxidative stress by upregulating NUDT1 expression, thereby facilitating tumor progression in KIRC (52). Thus, MCRS1 may also reduce oxidative stress by upregulating NUDT1 expression, thereby promoting HCC progression. Additionally, a robust negative relationship between MCRS1 and RORA was found in co-expression network analysis. This clock gene, RORA, was found to activate antitumor T-cell effects via corepressor complex–mediated PD-L1 inhibition (53). We therefore postulate that MCRS1-mediated suppression of RORA expression may mechanically underline the limited therapeutic efficacy of immunotherapy in HCC. Further experiments are needed to validate these hypotheses. KEGG enrichment analysis indicated that MCRS1 was mainly related to ribosome biogenesis which was a central player in cancer metastasis and therapeutic resistance (54). Therefore, ribosome biogenesis may be a downstream signaling pathway for MCRS1 to regulate HCC progression. Undoubtedly, rigorous in vivo and in vitro experiments are warranted to validate these hypotheses mechanistically.

While our study provides comprehensive insights, several limitations merit consideration. First, the reliance on bulk transcriptomic data may obscure intratumoral heterogeneity, necessitating spatial multi-omics validation. Second, although we identified MCRS1’s association with immune subsets, mechanistic studies are needed to delineate whether MCRS1 directly regulates M2-polarized macrophage or indirectly modulates cytokine networks. Third, our functional experiments focused on proliferation and invasion; evaluating MCRS1’s role in therapy resistance (e.g., to TKIs or ICIs) would strengthen its clinical relevance. Future studies should aim to elucidate the precise molecular mechanisms underlying MCRS1-mediated oncogenesis, including its interactions with immune cells and the TME. Additionally, preclinical and clinical trials investigating the efficacy and safety of MCRS1-targeting therapies are warranted to translate these findings into clinical practice.

Conclusions

In conclusion, MCRS1 is aberrantly overexpressed in 24 malignancies, including HCC, in which it correlates with advanced tumor stages, poor differentiation, and dismal survival outcomes. Mechanistically, MCRS1 appears to drive HCC progression via promoter hypomethylation-mediated transcriptional activation, fostering genomic instability through interactions with DNA repair pathways and stemness indices. Strikingly, MCRS1 may contribute to an immunosuppressive TME by promoting M2 macrophage polarization and myeloid DC infiltration, while suppressing CD8⁺ T-cell activity, which could provide a plausible explanation for immunotherapy resistance in HCC. These findings open new avenues for future research into HCC mechanisms and targeted therapies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1463/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1463/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1463/prf

Funding: This study was supported by the Doctor Initiation Fund of the Affiliated Hospital of Southwest Medical University (grant No. 24086) and Applied Basic Research Program of Southwest Medical University (grant No. 2024ZKY064).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1463/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Devarbhavi H, Asrani SK, Arab JP, et al. Global burden of liver disease: 2023 update. J Hepatol 2023;79:516-37. [Crossref] [PubMed]
2. Gabbia D, Cannella L, De Martin S. The Role of Oxidative Stress in NAFLD-NASH-HCC Transition-Focus on NADPH Oxidases. Biomedicines 2021;9:687. [Crossref] [PubMed]
3. Zhang X, Zhao L, Ngo LH, et al. Prediagnostic plasma proteomics profile for hepatocellular carcinoma. J Natl Cancer Inst 2024;116:1343-55. [Crossref] [PubMed]
4. Zheng J, Wang S, Xia L, et al. Hepatocellular carcinoma: signaling pathways and therapeutic advances. Signal Transduct Target Ther 2025;10:35. [Crossref] [PubMed]
5. Rizzo A, Brunetti O, Brandi G. Hepatocellular Carcinoma Immunotherapy: Predictors of Response, Issues, and Challenges. Int J Mol Sci 2024;25:11091. [Crossref] [PubMed]
6. Lawal G, Xiao Y, Rahnemai-Azar AA, et al. The Immunology of Hepatocellular Carcinoma. Vaccines (Basel) 2021;9:1184. [Crossref] [PubMed]
7. Liu C, Dou X, Zhao Y, et al. IGF2BP3 promotes mRNA degradation through internal m(7)G modification. Nat Commun 2024;15:7421. [Crossref] [PubMed]
8. Concepcion CP, Ma S, LaFave LM, et al. Smarca4 Inactivation Promotes Lineage-Specific Transformation and Early Metastatic Features in the Lung. Cancer Discov 2022;12:562-85. [Crossref] [PubMed]
9. Lin DY, Shih HM. Essential role of the 58-kDa microspherule protein in the modulation of Daxx-dependent transcriptional repression as revealed by nucleolar sequestration. J Biol Chem 2002;277:25446-56. [Crossref] [PubMed]
10. Hsu CC, Lee YC, Yeh SH, et al. 58-kDa microspherule protein (MSP58) is novel Brahma-related gene 1 (BRG1)-associated protein that modulates p53/p21 senescence pathway. J Biol Chem 2012;287:22533-48. [Crossref] [PubMed]
11. Liang Y, Liu M, Wang P, et al. Analysis of 20 genes at chromosome band 12q13: RACGAP1 and MCRS1 overexpression in nonsmall-cell lung cancer. Genes Chromosomes Cancer 2013;52:305-15. [Crossref] [PubMed]
12. Cui J, Xi H, Cai A, et al. Increased Expression of 58-kDa Microspherule Protein (MSP58) in Human Gastric Cancer Promotes Cell Proliferation and Correlates with Poor Patient Survival. Clin Lab 2016;62:993-1001. [Crossref] [PubMed]
13. Wang LM, Wang P, Chen XM, et al. Thioparib inhibits homologous recombination repair, activates the type I IFN response, and overcomes olaparib resistance. EMBO Mol Med 2023;15:e16235. [Crossref] [PubMed]
14. Liu MX, Zhou KC, Cao Y. MCRS1 overexpression, which is specifically inhibited by miR-129*, promotes the epithelial-mesenchymal transition and metastasis in non-small cell lung cancer. Mol Cancer 2014;13:245. [Crossref] [PubMed]
15. Wang XM, Li QY, Ren LL, et al. Effects of MCRS1 on proliferation, migration, invasion, and epithelial mesenchymal transition of gastric cancer cells by interacting with Pkmyt1 protein kinase. Cell Signal 2019;59:171-81. [Crossref] [PubMed]
16. Shen W, Song Z, Zhong X, et al. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. Imeta 2022;1:e36. [Crossref] [PubMed]
17. Chandrashekar DS, Karthikeyan SK, Korla PK, et al. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 2022;25:18-27. [Crossref] [PubMed]
18. Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017;45:W98-102. [Crossref] [PubMed]
19. Győrffy B. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. Innovation (Camb) 2024;5:100625. [Crossref] [PubMed]
20. Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science 2015;347:1260419. [Crossref] [PubMed]
21. Vasaikar SV, Straub P, Wang J, et al. LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res 2018;46:D956-63. [Crossref] [PubMed]
22. Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2:401-4. [Crossref] [PubMed]
23. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6:pl1. [Crossref] [PubMed]
24. Li R, Yan L, Jiu J, et al. PSME2 offers value as a biomarker of M1 macrophage infiltration in pan-cancer and inhibits osteosarcoma malignant phenotypes. Int J Biol Sci 2024;20:1452-70. [Crossref] [PubMed]
25. Coleman RL, Oza AM, Lorusso D, et al. Rucaparib maintenance treatment for recurrent ovarian carcinoma after response to platinum therapy (ARIEL3): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 2017;390:1949-61. [Crossref] [PubMed]
26. Malta TM, Sokolov A, Gentles AJ, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 2018;173:338-354.e15. [Crossref] [PubMed]
27. Shi H, Chai P, Jia R, et al. Novel insight into the regulatory roles of diverse RNA modifications: Re-defining the bridge between transcription and translation. Mol Cancer 2020;19:78. [Crossref] [PubMed]
28. Han Y, Wang Y, Dong X, et al. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res 2023;51:D1425-31. [Crossref] [PubMed]
29. Li T, Fu J, Zeng Z, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 2020;48:W509-14. [Crossref] [PubMed]
30. Fan Z, Chen R, Chen X. SpatialDB: a database for spatially resolved transcriptomes. Nucleic Acids Res 2020;48:D233-7. [PubMed]
31. Ben-David U, Amon A. Context is everything: aneuploidy in cancer. Nat Rev Genet 2020;21:44-62. [Crossref] [PubMed]
32. Qu X, Li H, Braziel RM, et al. Genomic alterations important for the prognosis in patients with follicular lymphoma treated in SWOG study S0016. Blood 2019;133:81-93. [Crossref] [PubMed]
33. Huang R, Zhou PK. DNA damage repair: historical perspectives, mechanistic pathways and clinical translation for targeted cancer therapy. Signal Transduct Target Ther 2021;6:254. [Crossref] [PubMed]
34. Germano G, Amirouchene-Angelozzi N, Rospo G, et al. The Clinical Impact of the Genomic Landscape of Mismatch Repair-Deficient Cancers. Cancer Discov 2018;8:1518-28. [Crossref] [PubMed]
35. Moynahan ME, Jasin M. Mitotic homologous recombination maintains genomic stability and suppresses tumorigenesis. Nat Rev Mol Cell Biol 2010;11:196-207. [Crossref] [PubMed]
36. Ushijima T, Clark SJ, Tan P. Mapping genomic and epigenomic evolution in cancer ecosystems. Science 2021;373:1474-9. [Crossref] [PubMed]
37. Papanicolau-Sengos A, Aldape K. DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis. Annu Rev Pathol 2022;17:295-321. [Crossref] [PubMed]
38. Suraweera A, O'Byrne KJ, Richard DJ. Epigenetic drugs in cancer therapy. Cancer Metastasis Rev 2025;44:37. [Crossref] [PubMed]
39. van Elsas MJ, Middelburg J, Labrie C, et al. Immunotherapy-activated T cells recruit and skew late-stage activated M1-like macrophages that are critical for therapeutic efficacy. Cancer Cell 2024;42:1032-1050.e10. [Crossref] [PubMed]
40. Huang CJ, Lyu X, Kang J. The molecular characteristics and functional roles of microspherule protein 1 (MCRS1) in gene expression, cell proliferation, and organismic development. Cell Cycle 2023;22:619-32. [Crossref] [PubMed]
41. Lin W, Li XM, Zhang J, et al. Increased expression of the 58-kD microspherule protein (MSP58) is correlated with poor prognosis in glioma patients. Med Oncol 2013;30:677. [Crossref] [PubMed]
42. Xu J, Yang X, Deng Q, et al. TEM8 marks neovasculogenic tumor-initiating cells in triple-negative breast cancer. Nat Commun 2021;12:4413. [Crossref] [PubMed]
43. Pacaud R, Thomas S, Chaudhuri S, et al. Low dose DNA methyltransferase inhibitors potentiate PARP inhibitors in homologous recombination repair deficient tumors. Breast Cancer Res 2025;27:8. [Crossref] [PubMed]
44. Cheng T, Zhou C, Bian S, et al. Coordinated activation of DNMT3a and TET2 in cancer stem cell-like cells initiates and sustains drug resistance in hepatocellular carcinoma. Cancer Cell Int 2024;24:110. [Crossref] [PubMed]
45. Shepherd R, Cheung AS, Pang K, et al. Sexual Dimorphism in Innate Immunity: The Role of Sex Hormones and Epigenetics. Front Immunol 2020;11:604000. [Crossref] [PubMed]
46. Chen L, Zhou L, Wang C, et al. Tumor-Targeted Drug and CpG Delivery System for Phototherapy and Docetaxel-Enhanced Immunotherapy with Polarization toward M1-Type Macrophages on Triple Negative Breast Cancers. Adv Mater 2019;31:e1904997. [Crossref] [PubMed]
47. Magen A, Hamon P, Fiaschi N, et al. Intratumoral dendritic cell-CD4(+) T helper cell niches enable CD8(+) T cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat Med 2023;29:1389-99. [Crossref] [PubMed]
48. Yu Y, Liang Y, Xie F, et al. Tumor-associated macrophage enhances PD-L1-mediated immune escape of bladder cancer through PKM2 dimer-STAT3 complex nuclear translocation. Cancer Lett 2024;593:216964. [Crossref] [PubMed]
49. Weng J, Wang Z, Hu Z, et al. Repolarization of Immunosuppressive Macrophages by Targeting SLAMF7-Regulated CCL2 Signaling Sensitizes Hepatocellular Carcinoma to Immunotherapy. Cancer Res 2024;84:1817-33. [Crossref] [PubMed]
50. Maki H, Sekiguchi M. MutT protein specifically hydrolyses a potent mutagenic substrate for DNA synthesis. Nature 1992;355:273-5. [Crossref] [PubMed]
51. Gad H, Koolmeister T, Jemth AS, et al. MTH1 inhibition eradicates cancer by preventing sanitation of the dNTP pool. Nature 2014;508:215-21. [Crossref] [PubMed]
52. Shi J, Xiong Z, Wang K, et al. HIF2α promotes tumour growth in clear cell renal cell carcinoma by increasing the expression of NUDT1 to reduce oxidative stress. Clin Transl Med 2021;11:e592. [Crossref] [PubMed]
53. Liu D, Wei B, Liang L, et al. The Circadian Clock Component RORA Increases Immunosurveillance in Melanoma by Inhibiting PD-L1 Expression. Cancer Res 2024;84:2265-81. [Crossref] [PubMed]
54. Elhamamsy AR, Metge BJ, Alsheikh HA, et al. Ribosome Biogenesis: A Central Player in Cancer Metastasis and Therapeutic Resistance. Cancer Res 2022;82:2344-53. [Crossref] [PubMed]