MRPL13 sustains mitochondrial integrity and MAPK-associated aggressiveness in esophageal squamous cell carcinoma

Abstract

Background

Mitochondrial ribosomal protein L13 (MRPL13) has been implicated in tumor progression, but its relevance to esophageal squamous cell carcinoma (ESCC) remains insufficiently defined. Because mitochondrial adaptation can influence tumor cell fitness under oncogenic and oxidative stress, we investigated whether MRPL13 is associated with aggressive ESCC biology and mitochondrial functional states.

Methods

We analyzed MRPL13 expression, clinical associations, survival relevance, mutation patterns, immune-related correlates, and pathway enrichment using TCGA and related public datasets, with an emphasis on ESCC-specific analyses. Experimental validation was performed using paired ESCC tissues and KYSE150 cells with shRNA-mediated MRPL13 knockdown. Western blotting, colony formation, transwell migration/invasion assays, mitochondrial membrane potential assessment, and ROS staining were used to evaluate protein expression, cellular behavior, and mitochondrial status.

Results

MRPL13 was up-regulated in ESCC and its expression was associated with tumor stage and unfavorable prognosis. MRPL13 expression correlated with a set of cancer-associated genes, including SIX2, SPP1, COL11A1, DSC3, and CCNA1, several of which were also increased at the protein level in paired ESCC tissues. ESCC tumors with higher MRPL13 expression showed distinct mutation and immune-related expression patterns; however, these immune associations were interpreted as correlates of tumor state rather than direct evidence of immune regulation. Pathway analyses indicated enrichment of MAPK-related signaling in MRPL13-high tumors. Consistently, MRPL13 knockdown in KYSE150 cells reduced phosphorylation of MEK1/2, ERK1/2, JNK1/2, and p38, impaired colony formation and in vitro migration/invasion, decreased mitochondrial membrane potential, and increased intracellular ROS levels.

Conclusion

These findings suggest that MRPL13 identifies an aggressive ESCC state in which mitochondrial integrity, MAPK-associated signaling, and tumor cell fitness are functionally coupled. Rather than establishing MRPL13 as a fully defined upstream driver, our data support a more restrained interpretation: MRPL13 may help ESCC cells maintain mitochondrial stress tolerance, and its loss exposes a vulnerability characterized by reduced MAPK phosphorylation, impaired clonogenic and invasive capacity, mitochondrial depolarization, and ROS accumulation.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12935-026-04420-1.

Introduction

Cancer remains one of the foremost causes of morbidity and mortality worldwide [1]. Despite significant advances in surgery, chemotherapy, radiotherapy, and more recently, targeted and immune-based therapies, the overall survival rates for many malignancies remain unsatisfactory [2]. Treatment resistance, tumor heterogeneity, and the capacity of tumors to evade immune surveillance continue to undermine clinical outcomes. These persistent challenges underscore the urgent need for novel biomarkers and therapeutic targets that can refine patient stratification and inform innovative treatment strategies.

Mitochondrial ribosomal proteins (MRPs) constitute a family of ribosomal subunits that support mitochondrial protein translation and bioenergetics [3]. Because of their central role in mitochondrial homeostasis, MRPs are increasingly recognized as participants in tumorigenesis, influencing proliferation, apoptosis, and metabolic adaptation [3, 4]. Among them, mitochondrial ribosomal protein L13 (MRPL13) is encoded at chromosome 8q24.12 and functions as a primary binding protein within the large subunit of mitochondrial ribosomes [5]. Although relatively understudied, emerging evidence suggests a potentially pivotal role for MRPL13 in cancers [6–8]. For instance, MRPL13 expression is elevated in breast cancer, where it promotes proliferation, epithelial–mesenchymal transition, and invasion via the PI3K–Akt–mTOR pathway [9]. Similarly, dysregulation of MRPL13 has been implicated in non-small cell lung cancer [10], linking mitochondrial translation to tumor progression. Yet, beyond these sporadically isolated observations, the broader oncogenic and immunological relevance of MRPL13 has remained largely unexplored.

The gap is especially striking in esophageal squamous cell carcinoma (ESCC), an aggressive malignancy with dismal prognosis and limited biomarker options. Given that ESCC progression is closely tied to both genomic instability and tumor–immune interactions [11], a deeper exploration of MRPL13 could shed new light on its role as a driver of disease aggressiveness and a potential immunomodulatory factor. This rationale motivated us to systematically evaluate MRPL13 in a pan-cancer context, with an emphasis on ESCC. In our earlier study, we performed an in silico investigation of MRPL13 in squamous cell carcinomas, including ESCC, and identified its potential oncogenic role [12]. However, that work was preliminary in scope, focusing mainly on expression patterns and prognosis. These limitations prompted us to undertake the present study, which expands the analysis to pan-cancer datasets, integrates immune and mutational correlates to more comprehensively define MRPL13’s role.

Recent advances have begun to uncover the oncogenic potential of MRPL13 in several malignancies, particularly through its regulation of mitochondrial activity. Notably, Liu et al. reported that MRPL13 enhances oxidative phosphorylation and preserves mitochondrial integrity in ovarian cancer by inhibiting aberrant mPTP opening through K48-linked ubiquitination and degradation of its mitochondrial partner SLC25A6 [5]. These observations highlight the emerging role of MRPL13 as a mitochondrial gatekeeper in solid tumors. However, whether MRPL13 exerts comparable effects in ESCC, and whether its oncogenic functions extend beyond mitochondrial maintenance to include broader signaling and immune regulatory mechanisms, remains unknown.

Given these gaps, we combined pan-cancer bioinformatic analyses with experimental validation to examine MRPL13 in esophageal squamous cell carcinoma. Rather than focusing solely on expression patterns, we aimed to place MRPL13 within a broader biological context, including signaling pathways, immune-associated features, and mitochondrial function. Through this integrated approach, we sought to clarify whether MRPL13 represents a functionally relevant node in ESCC biology, while remaining cautious about inferring direct mechanistic roles.

Materials and methods

Data sources and preprocessing

Transcriptomic and clinical data for MRPL13 analyses were obtained from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas [13], using harmonized RNA-seq datasets (log₂ TPM) [14]. Normal tissue references were supplemented by the GTEx database [15], and proteomic data were accessed from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [16]. For esophageal squamous cell carcinoma (ESCC)-focused analyses, we extracted TCGA-ESCC cohorts with complete RNA-seq and clinical annotations.

Differential expression analyses

We evaluated MRPL13 expression across tumor and normal tissues using TIMER 3.0 [17] and Xiantao academic tools (www.xiantaozi.com/). For paired tumor–normal comparisons, Wilcoxon signed-rank tests were used, while Wilcoxon rank-sum tests were applied for unpaired group analyses. Stratified analyses were conducted to assess expression differences across clinical variables (e.g., age, stage). All expression data were visualized with boxplots or violin plots, with median and interquartile ranges displayed.

Survival and prognostic analyses

The prognostic value of MRPL13 and its associated genes was assessed using Kaplan–Meier survival curves, Cox proportional hazards regression, and log-rank testing. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated for overall survival (OS) and progression-free survival (PFS). Time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive accuracy at 1-, 2-, and 3-year survival endpoints, with area under the curve (AUC) values reported.

Genetic alteration and mutation profiling

Somatic mutation data were derived from TCGA-ESCC mutation annotation format (MAF) files [18]. Mutation landscapes were visualized using the maftools R package, which provided oncoplots, variant classification distributions, and variant type summaries. Survival differences between wild-type and mutant carriers of selected driver genes were evaluated using Kaplan–Meier analysis and Cox regression.

Immune-related analyses

The relationship between MRPL13 expression and immune regulation was explored across multiple dimensions. Correlation analyses with immune checkpoint molecules, TNF superfamily genes, chemokine genes, and tumor microenvironment scores (StromalScore, ImmuneScore, ESTIMATEScore) were conducted using Pearson’s correlation coefficients. Immune cell infiltration estimates were derived using deconvolution algorithms (e.g., TIMER, CIBERSORT [19]). A correlation network was generated in Cytoscape to visualize associations between MRPL13 and immune cell populations.

Functional enrichment analyses

Differentially expressed genes (DEGs) between high and low MRPL13 groups were identified using DESeq2 (|log2 fold change| > 1, adjusted P < 0.05). KEGG pathway enrichment [20] was conducted with the clusterProfiler R package, ranking pathways by –log10(q-value). Gene Set Enrichment Analysis (GSEA) [21]was performed using KEGG gene sets with 1,000 permutations; normalized enrichment scores (NES), nominal P values, and false discovery rate (FDR) values were reported.

Clinical ESCC tissue specimens

A total of 10 additional paired ESCC tumor tissues and matched adjacent normal esophageal epithelia were collected from patients undergoing surgical resection at the First Affiliated Hospital of Xinjiang Medical University. None of the patients had received neoadjuvant chemotherapy or radiotherapy prior to surgery. All samples were immediately snap-frozen in liquid nitrogen and stored at − 80 °C until protein extraction. These paired specimens were used exclusively for Western blot validation of MRPL13 and its associated proteins. The study protocols were approved by the institutional ethics committee, and written informed consent was obtained from all participants.

Cell culture and lentiviral transduction

Human ESCC cell line KYSE150 was cultured in RPMI-1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin–streptomycin at 37 °C in a humidified incubator with 5% CO₂. For MRPL13 knockdown, KYSE150 cells were infected with three independent MRPL13 shRNA-expressing lentiviruses (LV-MRPL13-RNAi, PSC43677-1; LV-MRPL13-RNAi, PSC43676-1; LV-MRPL13-RNAi, PSC43675-1; Shanghai Genechem Co., Ltd., Shanghai, China) or a non-targeting negative control lentivirus (CON053, Genechem) using HitransG P and HitransG A virus infection reagents (Genechem) according to the manufacturer’s instructions. Among the three constructs, the most efficient knockdown (shRNA-77, shorthand for PSC43677-1) was selected for subsequent functional experiments. After infection, cells were maintained in complete medium, and stable MRPL13-knockdown and control cell populations were established by antibiotic selection as recommended by the supplier. Knockdown efficiency was verified by Western blotting before subsequent functional assays.

Western blot analysis

Protein extracts from ESCC cell lines and from the 10 paired ESCC clinical tissue samples were prepared using RIPA lysis buffer (#89901,Thermo) supplemented with protease and phosphatase inhibitors (G2007-1ML, Servicebio, Beijing). Protein concentrations were measured using the BCA assay (#23227, Thermo). Equal amounts of protein (20–40 µg per lane) were separated on SDS–PAGE gels and transferred to PVDF membranes. Membranes were blocked in 5% non-fat milk for 1 h and incubated overnight at 4 °C with primary antibodies against MRPL13, SIX2, SPP1, COL11A1, DSC3, CyclinA1, Siglec-15, LAG-3, p-MEK1/2, MEK1/2, p-ERK1/2, ERK1/2, p-JNK1/2, JNK1/2, p-p38, and p38 (dilutions per manufacturer recommendations). After washing, HRP-conjugated secondary antibodies were applied for 1 h at room temperature. Bands were visualized using ECL reagents (#BL520A, biosharp), and densitometry was performed in Image J with β-actin as the loading control. Detailed information regarding the catalog numbers, antibody dilutions, and vendors used in this experiment can be found in Supplementary Table 1.

Clonogenic assay

KYSE150 cells transfected with control shRNA or MRPL13-targeting shRNAs were seeded into six-well plates at a density of 500–800 cells/well and cultured for 10–14 days. Colonies were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet. Plates were washed, air-dried, and imaged; colonies containing ≥ 50 cells were counted manually or using Image J. Assays were performed in triplicate.

Transwell migration and invasion assays

Cell migration and invasion abilities were assessed using 24-well Transwell chambers (8-µm pore size) (#3422, Corning, USA). For migration assays, 5 × 10⁴ cells in serum-free medium were seeded in the upper chamber; medium containing 10% FBS was added to the lower chamber as a chemoattractant. For invasion assays, the upper chamber was precoated with Matrigel (#354230, Corning), and 1 × 10⁵ cells were seeded. After 24–48 h incubation, non-migrated/non-invaded cells were removed with a cotton swab. Cells on the lower membrane surface were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted in ≥ 5 randomly selected fields under a light microscope.

Mitochondrial membrane potential (MPTP) and ROS staining assays

Mitochondrial membrane potential (MPTP) assay

Mitochondrial membrane potential was evaluated using the JC-1 fluorescent probe (Beyotime, #C2009S, Nanjing, China). After shRNA transfection, ESCC cells were incubated with working dye solution for 20–30 min at 37 °C in the dark. Cells were washed, resuspended in assay buffer, and analyzed by fluorescence microscopy. A decrease in the red/green fluorescence ratio indicated loss of mitochondrial membrane potential. Quantification was performed by calculating the ratio of red (aggregated JC-1) to green (monomeric JC-1) fluorescence intensity with Image J software (ver. 1.45 l) (NIH, Bethesda, MD, USA).

ROS detection by DCFH-DA staining

Intracellular reactive oxygen species (ROS) levels were measured using the DCFH-DA fluorescent probe (D6470, Solarbio, Beijing). Cells were incubated with 10 µM DCFH-DA for 20–30 min at 37 °C, washed three times with serum-free medium, and visualized under a fluorescence microscope. Fluorescence signals were primarily observed in the cytoplasm, and quantitative analysis was based on mean cytoplasmic fluorescence intensity. DCF fluorescence intensity was quantified from at least 5 fields per sample using Image J software (ver. 1.45 l) (NIH, Bethesda, MD, USA).

Statistical analysis

For paired tumor–normal comparisons, Wilcoxon signed-rank tests were used, while Wilcoxon rank-sum tests were applied for unpaired group analyses. For survival analysis, HR and p values were calculated using univariate Cox regression analysis. Survival analyses were corrected for multiple testing where applicable. All statistical tests were two-sided. P < 0.05 (*) was considered statistically significant, with additional thresholds indicated as P < 0.01 (**), P < 0.001 (***), and P < 0.0001 (****).

Results

MRPL13 is upregulated in ESCC and associated with tumor progression

We began our investigation by examining the expression profile of MRPL13 across a broad spectrum of cancers using TCGA datasets. A pan-cancer overview revealed that MRPL13 was significantly up-regulated in a majority of tumor types compared with corresponding normal tissues (Fig. 1A). This global pattern suggests that aberrant MRPL13 expression may represent a common feature across multiple tumor types rather than a cancer-type–specific event. To strengthen this observation, we next turned to paired tumor–normal comparisons in selected cohorts, which consistently confirmed elevated MRPL13 expression in tumor tissues relative to their matched adjacent controls (Fig. 1B). Focusing specifically on ESCC, we stratified patients by clinical and demographic parameters to assess whether MRPL13 expression varied among subgroups. Interestingly, stratification by age (≤ 60 vs. >60 years) revealed no significant difference in expression (Fig. 1C), indicating that age alone is not a determinant of MRPL13 dysregulation. However, when comparing tumor tissues directly with adjacent normals, MRPL13 was significantly overexpressed in ESCC (Fig. 1D), aligning with our pan-cancer findings. Furthermore, expression levels demonstrated a clear stage-dependent increase, with higher levels observed in advanced clinical stages (I–IV) (Fig. 1E). These findings indicate that MRPL13 dysregulation is detectable in ESCC and is associated with disease progression, although expression data alone do not establish a causal role.

Fig. 1.

Expression profile of MRPL13 in pan-cancer datasets and ESCC.A: Differential expression of MRPL13 across TCGA pan-cancer cohorts.B: Paired comparison of MRPL13 expression between tumor and matched adjacent normal tissues in selected TCGA cohorts.C: MRPL13 expression in ESCC patients stratified by age (≤ 60 vs. >60 years).D: Comparison of MRPL13 expression between ESCC tumor and adjacent normal tissues.E: MRPL13 expression across different clinical stages of ESCC.Data are presented as median with interquartile range. Statistical significance was determined using the Wilcoxon test. Ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

MRPL13-associated genes define an aggressive ESCC transcriptional state

Having established the upregulation of MRPL13, we next sought to explore its broader transcriptional network in ESCC. Correlation analysis identified the top 20 genes most strongly associated with MRPL13 expression, which we visualized in a heatmap (Fig. 2A). Among these, SIX2, SPP1, COL11A1, and DSC3 emerged as particularly notable, given their previously reported roles in cancer progression. Scatterplot correlations confirmed robust positive associations between these genes and MRPL13 (Fig. 2B). We then examined whether these MRPL13-associated genes carried clinical relevance. Stage-stratified analyses revealed that SIX2, SPP1, COL11A1, and DSC3 all showed progressive upregulation across ESCC stages (Fig. 2C), paralleling the stage-dependent increase we observed for MRPL13 itself. Importantly, survival analyses demonstrated that higher expression of each of these genes predicted significantly worse outcomes, both in terms of overall survival (OS) and progression-free survival (PFS) (Fig. 2D–E). These findings highlight the potential oncogenic relevance of MRPL13-associated genes and suggest that MRPL13 may be embedded within a broader pro-tumorigenic gene expression program in ESCC.

Fig. 2.

Identification and clinical relevance of MRPL13-associated genes in ESCC.A: Heatmap showing the top 20 genes most strongly correlated with MRPL13 expression in TCGA-ESCC.B: Correlation analyses between MRPL13 and representative associated genes, including SIX2, SPP1, COL11A1, and DSC3.C: Expression of representative MRPL13-associated genes (SIX2, SPP1, COL11A1, and DSC3) across different clinical stages of ESCC.D: Kaplan–Meier analysis of overall survival according to expression levels of representative MRPL13-associated genes.E: Kaplan–Meier analysis of progression-free survival according to expression levels of representative MRPL13-associated genes

MRPL13 retains prognostic relevance across clinically defined ESCC subgroups

To further evaluate the clinical significance of MRPL13, we examined whether its prognostic value remained consistent across different patient subgroups. Forest plot analysis demonstrated that elevated MRPL13 expression was associated with unfavorable overall survival in multiple clinically defined categories, including age, sex, TNM stage, recurrence status, and treatment-related subgroups (Fig. 3A). Although the magnitude of association varied among individual strata, the overall trend consistently favored poorer outcomes in patients with higher MRPL13 expression.

Fig. 3.

Clinical and immune-related associations of MRPL13 in ESCC.A: Forest plot showing the association between MRPL13 expression and overall survival across clinically defined patient subgroups.B: Correlation network illustrating the associations between MRPL13 expression and immune-cell populations in ESCC.C: Time-dependent ROC curves evaluating the prognostic performance of MRPL13 and selected MRPL13-associated genes (COL11A1, SIX2, DSC3, SPP1, and CCNA1) for 1-, 2-, and 3-year overall survival in ESCC

We next explored the relationship between MRPL13 and immune-cell populations within the ESCC microenvironment. Correlation network analysis revealed extensive associations between MRPL13 expression and multiple immune-cell subsets (Fig. 3B), suggesting that MRPL13-high tumors may exist within a distinct immune-associated context. Notably, both positive and negative correlations were observed, indicating that the immune landscape associated with MRPL13 expression is unlikely to be uniformly immunosuppressive or immune-activating.

To assess the potential prognostic performance of MRPL13-related biomarkers, time-dependent ROC analyses were performed. MRPL13 and several representative associated genes, including COL11A1, SIX2, DSC3, SPP1, and CCNA1, demonstrated measurable predictive value for 1-, 2-, and 3-year overall survival (Fig. 3C). While the predictive performance of individual genes varied across time points, the overall pattern supported the clinical relevance of the MRPL13-associated transcriptional program.

Taken together, these findings suggest that MRPL13 is not merely differentially expressed in ESCC but is embedded within a broader biological state characterized by adverse prognosis, immune-related associations, and clinically informative transcriptional features.

High-MRPL13 ESCC tumors display distinct somatic mutation patterns

Given that transcriptional states and genomic alterations often evolve together during tumor progression, we next examined whether MRPL13 expression was associated with distinct mutational landscapes in ESCC. Oncoplot analyses revealed clear differences between the high- and low-MRPL13 groups (Fig. 4A–B). Tumors with elevated MRPL13 expression exhibited a broader distribution of somatic alterations and a greater representation of recurrently mutated genes, suggesting increased genomic complexity.

Fig. 4.

Distinct mutational landscapes associated with MRPL13 expression in ESCC.A: Oncoplot showing the mutational landscape of ESCC tumors with high MRPL13 expression.B: Oncoplot showing the mutational landscape of ESCC tumors with low MRPL13 expression.C-D: Summary of mutation classifications, variant types, and the most frequently mutated genes in the high- and low-MRPL13 expression groups, respectively.E: Kaplan–Meier analysis of overall survival according to mutation status of selected driver genes in the high- and low-MRPL13 expression groups

Further characterization of mutation classifications and variant types demonstrated that the high-MRPL13 group contained a larger proportion of missense and frameshift events, whereas the low-expression group showed a comparatively restricted mutational spectrum (Fig. 4C–D). These observations indicate that MRPL13-high tumors tend to occupy a genetically more altered state. Whether MRPL13 contributes to this landscape or simply marks tumors that have already accumulated greater genomic instability cannot be determined from the present dataset.

We next asked whether these mutational differences carried clinical relevance. Kaplan–Meier analyses showed that the mutation status of selected driver genes significantly influenced overall survival within both MRPL13 expression strata (Fig. 4E). Together, these findings suggest that elevated MRPL13 expression is accompanied not only by transcriptional reprogramming but also by a distinct genomic context that may contribute to adverse clinical behavior in ESCC.

MRPL13-associated transcriptomic programs converge on MAPK signaling

Having observed consistent clinical, mutational, and immune-related associations, we next asked whether these observations converged on a common biological program.

KEGG enrichment analysis identified multiple pathways associated with tumor progression among genes differentially expressed between high- and low-MRPL13 tumors (Fig. 5A). These included cell-cycle regulation, PI3K–Akt signaling, MAPK signaling, and other pathways frequently implicated in malignant progression. Rather than pointing to a single dominant mechanism, these results suggested that MRPL13 expression is embedded within a broader oncogenic transcriptional landscape.

Fig. 5.

Functional enrichment analyses identify MAPK-associated transcriptomic programs in ESCC with high MRPL13 expression.A: KEGG pathway enrichment analysis of differentially expressed genes between high- and low-MRPL13 expression groups in TCGA-ESCC. The top significantly enriched pathways are shown.B: Gene set enrichment analysis (GSEA) demonstrating enrichment of the MAPK signaling pathway in the MRPL13-high group.C-D: Representative GSEA plots showing enrichment of the PI3K–Akt signaling pathway and cell-cycle–related programs in the MRPL13-high group, respectively

Gene set enrichment analysis further refined this observation. Among the enriched pathways, MAPK signaling emerged as one of the most consistently represented programs in MRPL13-high tumors (Fig. 5B), accompanied by enrichment of PI3K–Akt signaling and cell-cycle–related gene sets (Fig. 5C–D). The convergence of independent enrichment approaches increased our confidence that these associations were biologically meaningful rather than analytical artifacts.

To determine whether the transcriptomic observations were reflected at the protein level, we examined MAPK pathway activity following MRPL13 knockdown. Although total MEK1/2, ERK1/2, JNK1/2, and p38 levels remained largely unchanged, phosphorylation of each pathway component was markedly reduced after MRPL13 depletion (Fig. 7E–H). Thus, both computational and experimental analyses pointed toward an association between MRPL13 expression and MAPK pathway activity.

Fig. 7.

Functional impact of MRPL13 knockdown on tumor-associated proteins, MAPK signaling, cell-cycle progression and apoptosis in ESCC cells.A: Western blot validation of MRPL13 knockdown efficiency in KYSE150 cells using three independent shRNA constructs (shRNA-75, shRNA-76 and shRNA-77).B: Densitometric quantification of MRPL13 protein expression following shRNA-mediated knockdown.C: Western blot analysis of proteins associated with the MRPL13-high tumor state, including SIX2, SPP1, COL11A1, DSC3, CyclinA1, and Siglec-15.D: Western blot analysis of cell-cycle regulators (Cyclin B1 and CDK1) and apoptosis related proteins (cleaved Caspase-3, Bax, Bcl-2 and SLC7A11) in MRPL13-silenced cells.E: Western blot analysis of phosphorylated and total MAPK signaling proteins, including MEK1/2, ERK1/2, JNK1/2 and p38, in KYSE150 cells with or without stable MRPL13 knockdown.F-H: Densitometric quantification of the Western blot bands shown in panels C–E, respectively. Values were normalized to β-actin and presented as mean ± SD from three independent experiments. ns, non-significant versus control; **P < 0.01, ***P < 0.001

It should be emphasized, however, that these findings establish association rather than direct mechanistic control. MRPL13 is not itself a canonical signaling molecule. Consequently, reduced MAPK phosphorylation may arise secondarily from broader cellular alterations induced by MRPL13 loss, including mitochondrial dysfunction, oxidative stress, or changes in cell-cycle status. Nevertheless, among the pathways examined, MAPK signaling emerged as the most consistently supported candidate linking MRPL13 to aggressive ESCC phenotypes.

MRPL13 and related tumor-state proteins are elevated in ESCC tissues

Bioinformatic associations are inherently indirect. We therefore sought protein-level evidence in an independent set of paired ESCC clinical specimens.

Western blot analyses of ten paired tumor and adjacent non-tumor tissues confirmed that MRPL13 protein expression was consistently elevated in ESCC tumors (Fig. 6). Importantly, several proteins identified through transcriptomic analyses—including SIX2, SPP1, COL11A1, DSC3, and CyclinA1—showed a similar pattern of increased expression in tumor tissues. The concordance between transcriptomic and protein-level observations strengthens the biological relevance of the MRPL13-associated expression program identified in silico.

Fig. 6.

Validation of MRPL13-associated proteins in paired ESCC clinical tissues. Representative Western blot analyses of MRPL13, DSC3, CyclinA1, SPP1, SIX2, Siglec-15, COL11A1, and LAG-3 in ten paired ESCC tumor (T) and adjacent non-tumor (N) tissues. β-actin was used as a loading control. Densitometric quantification of protein expression is shown in the lower panels. Data are presented as mean ± SD. ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001

We also examined two immune-related molecules, Siglec-15 and LAG-3. Both proteins tended to be more abundant in tumor tissues, although the magnitude of increase varied among individual samples. Such variability is perhaps unsurprising given the substantial biological heterogeneity of ESCC and the limited sample size available for protein validation.

Overall, these tissue-level observations provide independent support for the existence of an MRPL13-associated tumor state characterized by coordinated upregulation of multiple proteins linked to proliferation, invasion, extracellular matrix remodeling, and immune-related signaling.

MRPL13 depletion suppresses tumor-associated proteins and alters cell-cycle and apoptosis-related regulators

To investigate the functional consequences of MRPL13 loss, three independent shRNA constructs targeting MRPL13 were introduced into KYSE150 cells. Western blot analysis confirmed efficient knockdown of MRPL13, with shRNA-77 producing the most pronounced reduction in protein expression and therefore being selected for subsequent experiments (Fig. 7A–B).

We next examined whether depletion of MRPL13 influenced proteins previously identified through transcriptomic and clinical tissue analyses. Notably, expression of several MRPL13-associated proteins, including SIX2, SPP1, COL11A1, DSC3, CyclinA1, and Siglec-15, was consistently reduced following MRPL13 knockdown (Fig. 7C and F). The concordance between bioinformatic predictions, tissue-level validation, and knockdown experiments strengthens the notion that these molecules belong to a common MRPL13-associated tumor program.

Given the observed reduction in proliferation-related proteins, we further evaluated regulators of cell-cycle progression and apoptosis. Silencing MRPL13 decreased the expression of Cyclin B1 and CDK1, two key drivers of G2/M transition, while simultaneously altering apoptosis-related proteins, including Bax, Bcl-2, cleaved Caspase-3, and SLC7A11 (Fig. 7D and G). Collectively, these changes indicate that MRPL13 depletion shifts ESCC cells away from a proliferative state and toward a stress-associated phenotype characterized by impaired cell-cycle progression and enhanced apoptotic susceptibility.

MRPL13 depletion impairs ESCC cell aggressiveness and disrupts mitochondrial integrity

We next examined whether the molecular alterations observed after MRPL13 depletion translated into measurable phenotypic consequences.

Silencing MRPL13 markedly reduced clonogenic growth capacity, as demonstrated by a substantial decline in colony formation (Fig. 8A). Likewise, Transwell assays revealed pronounced suppression of both migratory and invasive behavior (Fig. 8B–C). Consistent with these findings, EdU incorporation and wound-healing analyses demonstrated reduced proliferative activity and impaired wound closure following MRPL13 knockdown (Fig. 8D–F). Together, these results indicate that MRPL13 contributes to the maintenance of aggressive cellular phenotypes in ESCC.

Fig. 8.

MRPL13 knockdown impairs ESCC cell proliferation and invasion, disrupts mitochondrial integrity, and promotes ROS accumulation.A: Colony formation assay showing reduced clonogenic growth after MRPL13 silencing.B-C: Representative images of Transwell invasion (Matrigel-coated) and migration (without Matrigel) assays in KYSE150 cells transfected with shRNA-MRPL13-77 or control shRNA.D: EdU incorporation assay evaluating proliferative activity in KYSE150 cells after MRPL13 silencing;E: Quantitative analysis of EdU incorporation and wound closure rates.F: Representative images of wound-healing assays at 0 and 24 h following MRPL13 knockdown.G: Mitochondrial membrane potential (ΔΨm) assessed using JC-1 staining assessment using fluorescent probes shows attenuated mitochondrial integrity following MRPL13 knockdown.H: DCFH-DA staining reveals elevated intracellular ROS accumulation in MRPL13-depleted cells. Quantification is based on the ratio of red to green fluorescence intensity. Data are presented as mean ± SD from three independent experiments. Statistical significance was determined using the Mann–Whitney U test. *P < 0.05, P < 0.01 versus control

Given the mitochondrial origin of MRPL13, we next investigated whether these phenotypic changes were accompanied by alterations in mitochondrial integrity. JC-1 staining demonstrated a significant reduction in mitochondrial membrane potential after MRPL13 depletion (Fig. 8G), indicating mitochondrial depolarization. At the same time, intracellular ROS levels increased markedly, as detected by DCFH-DA staining (Fig. 8H), suggesting enhanced oxidative stress.

Notably, mitochondrial depolarization and ROS accumulation occurred in parallel with reductions in proliferative and invasive capacity. Although these data do not establish causality, they raise the possibility that impaired mitochondrial stress tolerance contributes to the vulnerable phenotype observed following MRPL13 depletion. Collectively, these findings support a model in which MRPL13 helps maintain mitochondrial integrity, thereby supporting aggressive cellular behavior in ESCC.

Discussion

In this study, we investigated MRPL13 in ESCC by combining public-dataset analyses, paired clinical tissue validation, and functional experiments in ESCC cells. The main message is deliberately restrained. MRPL13 should not be viewed as a master regulator of ESCC, but rather as a mitochondrial ribosomal protein associated with an aggressive ESCC state. This state is characterized by increased MRPL13 expression, adverse clinical associations, MAPK-associated signaling, and vulnerability to MRPL13 depletion. The data support a biologically coherent link between MRPL13, mitochondrial integrity, and aggressive cellular behavior, although they do not fully establish the complete causal architecture.

The strongest evidence for the relevance of MRPL13 comes from the convergence of several independent observations. MRPL13 was upregulated in ESCC and increased with clinical stage. Its expression correlated with several cancer-associated genes, including SIX2 [22], SPP1 [23], COL11A1 [24], DSC3 [25], and CCNA1, and these genes were associated with unfavorable clinical outcomes. Protein validation in paired ESCC tissues further supported increased expression of MRPL13 and several related tumor-state proteins. These findings suggest that MRPL13 is embedded in a broader aggressive ESCC program rather than acting in isolation. This distinction matters, because a single mitochondrial ribosomal protein is unlikely to explain all malignant features by itself. A more realistic interpretation is that high MRPL13 expression marks, and may partly sustain, a cellular state favorable for tumor progression.

This interpretation is supported by the observation that MRPL13 expression was tightly associated with several genes linked to aggressive tumor behavior, including SIX2 (osteopontin) [26], SPP1 [27, 28], COL11A1 [29], and DSC3 [30]. Notably, these genes are not mitochondrial ribosomal proteins themselves. We therefore favor the interpretation that they represent downstream transcriptional or signaling programs associated with an MRPL13-high cellular state rather than direct mitochondrial targets. This distinction is important because it avoids overstating mechanistic directionality while still providing biological context for the observed associations.

Our findings are broadly consistent with previous observations in breast [6, 9] and lung cancers [10, 31], where MRPL13 has been linked to proliferation, epithelial–mesenchymal transition, invasion, and unfavorable clinical outcomes [32, 33]. However, earlier studies focused primarily on growth-related phenotypes and signaling pathways, whereas immune-related features received comparatively little attention. In this regard, the present study extends previous work [12] by integrating mutation profiles, immune-associated correlates, and experimental validation in ESCC. At the same time, our data do not support the view that MRPL13 acts as a universal oncogenic driver across tumor types. Rather, its biological consequences are likely to be shaped by tissue context and tumor state.

The functional data support this interpretation. MRPL13 knockdown reduced colony formation, migration, and invasion in KYSE150 cells, accompanied by changes in cell-cycle and apoptosis-related proteins [7, 31]. These results are consistent with previous reports [5, 6, 31] linking MRPL13 to tumor cell proliferation and invasive behavior in other cancer types. However, the present study should not be overread as direct evidence of in vivo metastasis. The transwell and wound-healing assays used here assess in vitro motility and invasive capacity under simplified experimental conditions. Although these assays were quantified, they remain indirect models of tumor dissemination. We did not perform tail-vein injection, orthotopic implantation, or spontaneous metastasis assays in animals. Therefore, our data support the conclusion that MRPL13 contributes to aggressive cellular phenotypes, but they do not prove that MRPL13 drives metastatic colonization in vivo.

A central observation of this study is that MRPL13 depletion disrupted mitochondrial integrity. Knockdown cells showed reduced mitochondrial membrane potential and increased ROS accumulation. These findings are consistent with the known role of mitochondrial ribosomal proteins in maintaining mitochondrial protein synthesis and organelle function [5]. They also provide a plausible explanation for the reduction in growth and invasive behavior after MRPL13 knockdown. Still, alternative explanations should be considered. The observed phenotypes may reflect a relatively broad mitochondrial stress response rather than a pathway-specific effect of MRPL13. For example, impaired mitochondrial function can secondarily affect cell-cycle progression [34], apoptosis-related proteins, redox balance [35], and signaling pathways. Thus, mitochondrial disruption may be upstream of several observed changes, but the precise sequence of events remains unresolved.

The relationship between MRPL13 and MAPK signaling [36–38]should also be interpreted cautiously. Pathway enrichment analyses indicated MAPK-related signatures in MRPL13-high tumors, and MRPL13 knockdown reduced phosphorylation of MEK1/2, ERK1/2, JNK1/2, and p38. These findings support an association between MRPL13 and MAPK pathway activity. However, MRPL13 is not a kinase, and the present data do not prove that MRPL13 directly regulates MAPK signaling. It is equally possible that reduced MAPK phosphorylation occurs as a downstream consequence of mitochondrial dysfunction, oxidative stress, altered cell-cycle status, or broader cellular stress after MRPL13 depletion. A falsifiable model for future work would be that restoring mitochondrial function or buffering ROS should rescue, at least partly, MAPK phosphorylation and aggressive phenotypes in MRPL13-deficient cells. Without such rescue experiments, the MAPK data should be regarded as pathway-associated rather than pathway-defining.

Interestingly, this interpretation differs somewhat from earlier reports emphasizing PI3K–Akt–mTOR signaling downstream of MRPL13 in breast cancer [39] and non-small cell lung cancer [10]. Such differences are not necessarily contradictory. Distinct tumor types may rely on different signaling outputs downstream of mitochondrial adaptation, and ESCC is characterized by substantial genomic instability and environmental exposures that may shape signaling dependencies differently from breast or lung cancers. Therefore, whether MRPL13 preferentially engages MAPK, PI3K–Akt–mTOR or other stress-responsive pathways may be context dependent rather than universally conserved.

The immune-related findings are informative but should not be overstated. MRPL13 expression correlated with immune checkpoint molecules, chemokines, immune infiltration estimates, and tumor microenvironment scores. These associations suggest that MRPL13-high tumors may exist within a distinct immune-related tumor state.

One notable aspect of the present study is that the immune-related analyses were performed not only across pan-cancer datasets but also within an ESCC-focused cohort. This distinction is relevant because TCGA esophageal cancer datasets are dominated by adenocarcinoma cases, whereas ESCC represents a biologically distinct disease entity. By deliberately restricting key analyses to ESCC, we sought to minimize subtype-related confounding and better capture tumor-type–specific associations. Nevertheless, the current findings remain correlative and should not be interpreted as evidence that MRPL13 directly regulates immune checkpoint expression or immune-cell behavior.

Furthermore, computational immune correlations do not demonstrate that MRPL13 directly regulates immune evasion. Bulk transcriptomic signals may reflect tumor purity, stromal content, immune-cell admixture, or aggressive tumor biology rather than a direct immunological function of MRPL13. In addition, immune-related proteins such as Siglec-15 and LAG-3 were assessed in tumor tissues by Western blotting, but we did not perform immunohistochemistry, multiplex immunofluorescence or spatial analysis to determine which cell types expressed these proteins. This is an important limitation, because immune checkpoint expression in tumor cells, stromal cells, and infiltrating immune cells can have very different biological meanings.

Some negative or less definitive results are also worth noting. MRPL13 expression did not differ significantly between age subgroups, suggesting that its dysregulation is unlikely to be simply explained by patient age. In addition, some tissue-level protein changes were not uniformly strong across all paired samples, which is not surprising given the heterogeneity of ESCC tissues and the small number of clinical specimens used for Western blot validation. These observations do not weaken the entire study, but they remind us that MRPL13 is probably not a universal marker that behaves identically in every tumor. ESCC is biologically heterogeneous, and MRPL13 may be more relevant in particular tumor states than across all cases.

It is also important to place MRPL13 within the broader mitochondrial ribosomal protein family [31]. MRPL13 is only one component of the large mitochondrial ribosomal subunit, and our findings should not be interpreted to imply that other MRPL family members are biologically irrelevant [4]. A plausible alternative explanation is that multiple mitochondrial ribosomal proteins cooperate to maintain mitochondrial homeostasis, with partial redundancy among family members. From this perspective, MRPL13 may represent one accessible node within a larger mitochondrial translational network rather than a uniquely indispensable factor. Testing this possibility will require systematic comparative studies across the MRPL family.

The present study has several limitations. First, functional validation was mainly performed in one ESCC cell line. Additional ESCC models, including cell lines with different genetic backgrounds and endogenous MRPL13 levels, would strengthen the generalizability of the findings. Second, we used knockdown experiments but did not include rescue or overexpression experiments, which limits causal interpretation. Third, mitochondrial function was assessed using membrane potential and ROS staining, but we did not measure oxygen consumption, ATP production, mitochondrial translation, or respiratory reserve. Therefore, the term mitochondrial integrity is more appropriate than broader claims about mitochondrial fitness. Fourth, the in vitro migration and invasion assays cannot substitute for in vivo metastasis models. Finally, the immune analyses were largely computational and tissue-level, without direct functional testing of tumor–immune interactions.

Despite these limitations, the study provides a focused view of MRPL13 in ESCC. The most defensible conclusion is that MRPL13 helps sustain mitochondrial integrity, thereby supporting MAPK-associated aggressive phenotypes in ESCC cells. Its loss exposes a vulnerable state characterized by mitochondrial depolarization, ROS accumulation, reduced MAPK phosphorylation and impaired clonogenic and invasive capacity. From a translational perspective, the present findings should be viewed as hypothesis-generating rather than practice-changing. Although MRPL13 showed prognostic associations and correlated with immune-related features, we did not evaluate therapeutic responses or treatment stratification. It therefore remains unknown whether MRPL13 has predictive value for immunotherapy or targeted therapy. Nevertheless, the consistent association between MRPL13 expression, mitochondrial integrity, and aggressive tumor phenotypes suggests that mitochondrial adaptation may represent vulnerability worth further exploration in ESCC.

Future studies should test this model more directly by using rescue experiments, ROS or mitochondrial-function modulation, additional ESCC models, spatial immune profiling, and in vivo metastasis assays. Such work will be necessary to determine whether MRPL13 is merely a marker of an aggressive mitochondrial state or a functionally targetable dependency in ESCC.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

MRPL13

Mitochondrial Ribosomal Protein L13

SIX2

Sine Oculis Homeobox Homolog 2

SPP1

Secreted Phosphoprotein 1

COL11A1

Collagen Type XI Alpha 1 Chain

DSC3

Desmocollin 3

ceRNA

competing endogenousRNA

OS

Overall Survival

AUC

Area Under the Curve

CI

Confidence Interval

HR

Hazard ratio

GSEA

Gene Set Enrichment Analysis

NES

Normalized Enrichment Score

GO

Gene Ontology

TCGA

The Cancer Genome Atlas

GEO

Gene Expression Omnibus

GTEx

Genotype Tissue Expression

TIMER

Tumor Immune Microenvironment

ACC

Adrenocortical Carcinoma

BLCA

Bladder Urothelial Carcinoma

BRCA

Breast Invasive Carcinoma

CESC

Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

CHOL

Cholangiocarcinoma

COAD

Colon Adenocarcinoma

DLBC

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

ESCA

Esophageal Cancer

ESCC

Esophageal Squamous Cell Carcinoma

ESAD

Esophageal Adenocarcinoma

GBM

Glioblastoma Multiforme

HNSC

Head and Neck Squamous Cell Carcinoma

KICH

Kidney Chromophobe

KIRC

Kidney Renal Clear Cell Carcinoma

KIRP

Kidney Renal Papillary Cell Carcinoma

LAML

Acute Myeloid Leukemia

LGG

Brain Lower Grade Glioma

LIHC

Liver Hepatocellular Carcinoma

LUAD

Lung Adenocarcinoma

LUSC

Lung Squamous Cell Carcinoma

MESO

Mesothelioma

OV

Ovarian Serous Cystadenocarcinoma

PAAD

Pancreatic Adenocarcinoma

PCPG

Pheochromocytoma and Paraganglioma

PRAD

Prostate Adenocarcinoma

READ

Rectum Adenocarcinoma

SARC

Sarcoma

SKCM

Skin Cutaneous Melanoma

STAD

Stomach Adenocarcinoma

TGCT

Testicular Germ Cell Tumors

THCA

Thyroid Carcinoma

THYM

Thymoma

UCEC

Uterine Corpus Endometrial Carcinoma

UCS

Uterine Carcinosarcoma

UVM

Uveal Melanoma

MPTP

mitochondrial permeability transition pore

DCFH-DA

2′,7′-dichlorodihydrofluorescein diacetate

Author contributions

Shujuan Luo and Tao Liu (1st) contributed equally to this work, which were primarily responsible for data collection, bioinformatic analyses, and drafting the initial manuscript. Qiaojuan Wang and Bangwu Cai participated in preparation of figures. Aididar Nurbahati assisted in literature review. Qing Liu was involved in critical revision of the manuscript for intellectual content. Xiaomei Lu and Shutao Zheng served as corresponding authors. They conceived and designed the study, acquired funding, coordinated the project, and provided overall supervision. All authors read and approved the final version of the manuscript.

Funding

The study was partly supported by Tianshan Talent Cultivation Plan for Young Top Talents Project of Xinjiang Uygur Autonomous Region (2024TSYCCX0103), and partly by Science and Technology Aid to Xinjiang Program (No. 2026E02076) and the State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia Fund (SKL-HIDCA-2024-SG3), and Key Project of Xinjiang Uygur Autonomous Region Natural Science Foundation(2022D01D69), Major Scientific Research Plan Project Cultivation Project of Xinjiang Medical University(XYD2024ZX02) and by Innovative team training project of the First Affiliated Hospital of Xinjiang Medical University (LXM).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.