Mitochondrial gene APOO reprograms lipid metabolism to influence the prognosis of breast cancer

Introduction

Breast cancer (BC) is the leading malignancy among women globally. Its incidence is rising and patients are younger, which challenges women’s healthcare systems worldwide (1-3). Although comprehensive treatment strategies that incorporate chemotherapy, radiotherapy, endocrine therapy, and molecular targeted approaches have significantly advanced clinical management, the implementation of neoadjuvant therapy concepts has facilitated more personalized treatment pathways (4). However, therapeutic resistance and disease recurrence, driven by tumor heterogeneity and genomic instability, continue to pose major clinical obstacles. Therefore, a systematic investigation into the molecular mechanisms governing the progression of BC and the identification of novel therapeutic targets remain crucial for improving patient outcomes.

As the energy metabolic hub of eukaryotic cells, mitochondria play a fundamental role in cellular processes, with their dysregulation being closely associated with malignant tumor progression (4,5). In BC, mitochondrial metabolic reprogramming is a critical adaptive mechanism that allows tumor cells to maintain their ability to proliferate, invade, and survive under conditions of microenvironmental stress. Emerging evidence further indicates that mitochondria not only mediate metabolic crosstalk between tumor cells and immune cells (6), but enhanced mitochondrial function and increased oxidative phosphorylation also significantly correlate with chemotherapy resistance and unfavorable prognosis (7). These insights have positioned mitochondrial-targeted metabolic interventions as promising research frontiers with substantial clinical translation potential (8-10).

The mitochondrial contact site and cristae organizing system (MICOS) complex is an essential protein assembly responsible for maintaining cristae structural integrity (11). In humans, the apolipoprotein O (APOO/MIC26) gene is located on chromosome Xp22.11 and encodes APOO, a member of the lipoprotein family. The primary translation product consists of 198 amino acids, including a 23-amino acid signal peptide (12). APOO was first identified in 2003 through cardiac transcriptome analysis in a canine model of nutritional obesity (13), and its messenger RNA (mRNA) is highly expressed in multiple adipose-rich human tissues (12).

Subsequent studies have revealed that the APOO protein exists in three distinct forms (14,15): (I) a glycosylated 55-kDa isoform secreted extracellularly; (II) a glycosylated 55-kDa isoform localized to light membrane structures such as the endoplasmic reticulum or Golgi apparatus; and (III) a non-glycosylated 22-kDa isoform, termed MIC26, exclusively localized to mitochondria. Current research primarily focuses on the secreted 55-kDa protein and the mitochondrial 22-kDa isoform (MIC26).

As a core subunit of the MICOS complex, APOO/MIC26 plays pivotal regulatory roles in metabolic homeostasis. Previous investigations demonstrate that APOO deficiency triggers mitochondrial dysfunction and aberrant lipid metabolism, promoting lipid accumulation and exacerbating obesity progression (16,17). Conversely, in diabetic cardiomyopathy models, APOO overexpression enhances fatty acid metabolism, inducing lipotoxic myocardial damage (18). Additionally, APOO expression responds to nutritional status, displays elevated expression in adipose tissue, and closely associates with cholesterol metabolic dysregulation in livers of hyperlipidemic patients (17,19). The capacity of simvastatin to modulate cholesterol synthesis through APOO regulation further underscores its central position in lipid metabolism (17). Despite these established functions in metabolic disorders, APOO’s role and mechanisms in BC pathogenesis remain largely unexplored.

At the molecular level, APOO overexpression modulates lipid metabolism through multiple mechanisms: it directly upregulates key lipid metabolism genes including long-chain acyl-CoA synthetase and FATP4, while concurrently inducing mitochondrial ultrastructural abnormalities that compromise oxidative phosphorylation capacity. When cellular fatty acid uptake exceeds mitochondrial oxidation capabilities, this imbalance leads to accumulation of lipids including toxic species, ultimately culminating in lipotoxicity (18,20,21).

This study implemented an integrated multi-omics approach combined with advanced machine learning methodologies, systematically developing an analytical framework incorporating 117 algorithmic combinations to decipher expression patterns and prognostic significance of mitochondria-related genes in BC. Through comprehensive bioinformatics analysis and experimental validation, we identified APOO as a key regulatory gene and demonstrated its crucial function in controlling lipid metabolic reprogramming and tumor progression, thereby providing new theoretical foundations and potential targets for precision diagnosis and targeted therapy in BC. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2554/rc).

Methods

Dataset collection

The analysis in this study was conducted using data from public repositories and in accordance with the Declaration of Helsinki and its subsequent amendments. RNA-seq data and matched clinicopathological information for BC samples were derived from The Cancer Genome Atlas (TCGA, https://www.cancer.gov/ccg/research/genome-sequencing/tcga),the METABRIC study (https://www.cbioportal.org/study/summary?id=brca_metabric), and the GSE96058 cohort (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96058). These data were subjected to log2 transformation, and samples with overall survival (OS) times of less than 30 days were excluded from subsequent analysis. Mitochondrial gene sets were curated and consolidated from the gene set enrichment analysis (GSEA, https://www.gsea-msigdb.org/gsea/index.jsp) platform and the MitoCarta3.0 inventor (https://personal.broadinstitute.org/scalvo/Mitocarta3.0/human.mitocarta3.0.html). All datasets utilized are publicly available.

Identification and functional enrichment of mitochondria-related genes in BC

To identify mitochondria-related genes in BC, we performed differential expression analysis between tumor and adjacent normal tissues in the TCGA cohort using three complementary R packages: DESeq2, edgeR, and limma. To ensure robustness, genes with |log₂FC| >1 and P<0.05 in each analysis were considered differentially expressed, yielding 294 mitochondria-related differentially expressed genes (DEGs). Functional enrichment analysis of these DEGs was then conducted using the enrichGO and enrichKEGG functions from the clusterProfiler package. This analysis examined their functional involvement in Gene Ontology (GO) categories—covering biological processes (BPs), cellular components (CCs), and molecular functions (MFs)—and additionally in Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways.

Construction of a prognostic model using multiple machine learning algorithms

Clinical data from BC patients were processed by excluding those with follow-up periods shorter than 30 days. Univariate Cox regression analysis was conducted using the coxph function from the survival R package to identify prognosis-associated genes from the DEGs, with a threshold of |hazard ratio (HR)| ≠1 and P<0.05. The results were visualized as a forest plot using the forestplot package. The TCGA dataset served as the training set, while METABRIC and GSE96058 were used as validation sets. A total of 117 prediction models were constructed via cross-validation using the Mime1 package, and the C-index of each model was computed. Based on comprehensive evaluation, the optimal algorithm combination was selected to establish an mitochondria-related (MT)-risk score model, and Kaplan-Meier survival analysis was performed to compare outcomes between high- and low-risk groups.

Immune infiltration and clinical correlation analysis

To validate the reliability of the prognostic model, a comprehensive analysis of the tumor microenvironment (TME) was conducted using the TCGA-BRCA dataset. The IOBR package was employed to integrate both the ESTIMATE and CIBERSORT algorithms, enabling systematic evaluation of stromal and immune scores along with immune cell infiltration profiles. Correlation analysis between TME features and 36 candidate prognostic genes was performed, followed by literature review and tumor purity adjustment. This process identified APOO as a key gene for further investigation.

Subsequently, the correlation between APOO expression levels and clinicopathological parameters was examined. Additionally, the oncoPredict package was utilized to analyze the association between APOO expression and sensitivity to commonly used chemotherapeutic agents.

Diagnostic and prognostic validation

Immunohistochemical images of APOO protein expression were obtained from the Human Protein Atlas (HPA) database to evaluate differential protein expression between normal breast and breast cancer tissues. Receiver operating characteristic (ROC) curve analysis was conducted using the pROC package. The roc function was applied to construct the model, and the auc function was used to calculate the area under the curve (AUC) for diagnostic performance evaluation. The optimal expression cutoff value was determined using the surv_cutpoint function from the survminer package. Subsequently, Kaplan-Meier survival analysis was performed with the survfit function from the survival package to compare survival outcomes between patient groups with high and low APOO expression.

Cell line source and culture

This study utilized a human normal mammary epithelial cell line (MCF-10A) and three human BC cell lines (MCF-7, MDA-MB-231, BT-549), all sourced from the Heilongjiang Provincial Institute of Cancer Prevention and Control. All cell lines were maintained cryopreserved in liquid nitrogen and tested negative for mycoplasma contamination.

This study utilized a human normal mammary epithelial cell line (MCF-10A) and three human BC cell lines (MCF-7, MDA-MB-231, BT-549), all obtained from the Heilongjiang Provincial Institute of Cancer Prevention and Control and cryopreserved in liquid nitrogen. The cells were cryopreserved in liquid nitrogen and confirmed free of mycoplasma contamination. MCF-10A and MDA-MB-231 were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (#C11995500BT, Gibco, NY, USA), while MCF-7 and BT-549 were maintained in RPMI-1640 (#C11875500BT, Gibco), with all media supplemented with 10% fetal bovine serum (#FBS, 164210, Procel, Wuhan, China) and 1% penicillin-streptomycin (P1400,Solarbio), and incubated at 37 ℃ under 5% CO₂. For cell recovery, cryovials were rapidly thawed in a 37 ℃ water bath, and the cell suspension was diluted with complete medium, centrifuged, and seeded into T25 flasks. After 24 hours, the medium was replaced to remove residual cryoprotectant. For subculture, cells were passaged at 80–90% confluence following phosphate-buffered saline (PBS) (#PB180327, Procell) rinsing and 0.25% trypsin-EDTA (#25200072, Gibco) detachment, which was terminated with complete medium. The cells were then centrifuged, resuspended, and reseeded into new flasks for continued culture.

Plasmid construction and lentivirus production

The coding sequence of APOO was obtained from the NCBI database. shRNA and overexpression (OE) plasmids were designed using SnapGene software with EcoR I and Age I restriction sites. Oligo DNA sequences were synthesized and verified by sequencing at Sangon Biotech (Shanghai). The shRNA constructs were ligated into the backbone plasmid using T4 DNA Ligase (#EL0016, Fermentas, Vilnius, Lithuania), while the OE plasmids were constructed using the ClonExpress II One Step Cloning Kit (#C112, Vazyme, Nanjing, China). The resulting plasmids were transformed into TOP10 competent cells via heat shock, and positive clones were selected on ampicillin-containing LB agar plates. Monoclonal expansion was performed, followed by plasmid extraction. Lentiviruses were packaged using the helper plasmids pMD2.G and pSPAX2. The shRNA target sequences (5'→3') and corresponding viral titers are listed below (Titre: 1×10⁸ TU/mL): shAPOO-1: 5'-GCCTCCCTCTATTATCCACAA-3'; shAPOO-2: 5'-CGAGGATATATAGTCATAGAA-3'.

To generate stable cell lines, Log-phase cells were seeded in 6-well plates at 2×10⁵ cells per well and cultured overnight to reach 30–50% confluence. Lentivirus was thawed on ice and added to 1 mL fresh complete medium per well according to the calculated volume: Virus (µL) = MOI × cell number/viral titer (TU/mL) ×1000. After 8h of infection, 1 mL fresh medium was supplemented. At 24 h post-infection, cells were washed with PBS and refreshed with complete medium. GFP expression was examined at 48–72 h to assess infection efficiency. Stable transformants were selected using 1.5 µg/mL puromycin for three passages and maintained in 0.5µg/mL puromycin-containing (#P8230, Solarbio, Beijing, China) medium.

RNA extraction and reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Total RNA was isolated using FreeZol Reagent (#R711-01, Vazyme) according to the manufacturer’s instructions. RNA was reverse-transcribed into cDNA using the HiScript III All-in-one RT SuperMix (#R333-01, Vazyme), followed by a 2–3-fold dilution. Quantitative PCR was performed on an ABI StepOnePlus system with ChamQ Universal SYBR qPCR Master Mix (#Q711-02, Vazyme). Each reaction was run in triplicate with GAPDH as the endogenous control. Gene expression was quantified using the 2^–ΔΔCt method. All primers were designed using Primer Premier 5 software with assistance from the NCBI Primer-BLAST online tool for sequence alignment and specificity verification, and were synthesized by Sangon Biotech (Shanghai). The primer sequences used in this study are listed below: APOO: forward primer: 5'-TCCTGAGGGTCAATCGAAGTAT-3', reverse primer: 5'-CTCGCAATAGTGTCGGAGC-3'; GAPDH: forward primer: 5'-TGACTTCAACAGCGACACCCA-3', reverse primer: 5'-CACCCTGTTGCTGTAGCCAAA-3'.

Western blot

Proteins were extracted using RIPA lysis buffer (#P0013B, Beyotime, Shanghai, China) and quantified with a BCA assay kit (#P0010S, Beyotime). Samples were denatured in 5×SDS-PAGE loading buffer (#P0015, Beyotime) at 100 ℃ for 10 min, and 20 µg of total protein per lane was separated by 10% SDS‑PAGE. Subsequently, proteins were transferred to a PVDF membrane (IPVH00010, Merck Millipore, MA, USA), which was then blocked with 5% non-fat milk in TBST for 2 h at room temperature. The membranes were incubated overnight at 4 ℃ with the following primary antibodies: anti-APOO (1:2,000, #ab246865, Abcam, Cambridge, UK) and anti-GAPDH (1:30,000, #1E6D9, Proteintech, Wuhan, China). After washing, membranes were incubated with HRP-conjugated secondary antibodies: Goat Anti-Rabbit (1:3,000, #A0208, Beyotime) and Goat Anti-Mouse (1:3,000, #A0216, Beyotime) for 1h at room temperature. Protein bands were visualized using an enhanced chemiluminescence (ECL) detection system and quantified with ImageJ software.

Colony formation assay

Cells were seeded in 6-well plates at 700 cells/well in complete medium supplemented with 20% fetal bovine serum, with the total volume adjusted to 3 mL per well. When the majority of cell colonies contained ≥50 cells, the culture medium was removed. The cells were then washed, fixed with 4% paraformaldehyde (#P1110, Solarbio, Beijing, China), and stained with 0.1% crystal violet (#G1062, Solarbio). After staining, the plates were rinsed until colony boundaries were clearly visible, followed by microscopic imaging and quantification of the colony formation rate.

Annexin V-FITC/PI apoptosis

Apoptosis was evaluated using an Annexin V-FITC/PI Apoptosis kit (#E-CK-A211, Elabscience, Wuhan, China) according to the manufacturer’s protocol. Cells from each experimental group were collected, including both adherent cells after trypsinization and floating cells from the supernatant. After centrifugation at 300 ×g for 5 min, the cell pellet was washed with ice-cold PBS and adjusted to a density of 1–5×10⁵ cells/mL. A total of 5×10⁵ cells were resuspended in 100 µL of 1× Annexin V Binding Buffer, followed by staining with 2.5 µL Annexin V-FITC and 2.5 µL propidium iodide (PI) (50 µg/mL). After incubation at room temperature for 15–20 min in the dark, 400 µL of binding buffer was added, and samples were immediately analyzed using a flow cytometer configured with PE and APC channels. The percentages of early and late apoptotic cells were subsequently quantified.

Cell migration analyses

Cells were seeded in groups and allowed to reach over 90% confluence. A uniform wound was created in the monolayer using a 200-µL pipette tip. After washing with PBS to remove detached cells, the wound areas were photographed at appropriate time intervals. The images were processed and analyzed using Image J to quantify the changes in horizontal cell migration distance.

Cell Counting Kit-8 (CCK-8) assays

Cells were plated at 2×10³ cells/well in 96-well plates and grouped by treatment. Over the next 5 days, cells were daily incubated with 10% CCK-8 reagent (#96992, Sigma, St. Louis, MO, USA) for 2 h, followed by absorbance measurement at 450 nm using a Tecan Infinite microplate reader.

Measurement of lipid metabolism parameters

Total cholesterol (TC), triglycerides (TG), and free fatty acids (FFAs) were measured using commercial assay kits (#BC1980, #BC0620, #BC0590, Solarbio, Beijing, China) according to the manufacturer’s instructions. Working solutions and standard curves were prepared as specified. Sample extracts were appropriately diluted to ensure metabolite concentrations fell within the linear detection range of each assay. Absorbance values for both standards and samples were acquired simultaneously using a multifunctional microplate reader. Analyte concentrations were calculated based on their corresponding standard curves.

Statistical analysis

All data are expressed as mean ± standard deviation (SD) from at least three independent experiments. Bioinformatics analyses were conducted using R (v4.4.2), while ImageJ-derived data were processed with GraphPad Prism 9.5.0. Statistical analyses included Pearson correlation, Chi-squared test, t-test, Wilcoxon test, and one-way analysis of variance (ANOVA) for multiple comparisons. Significance was set at P<0.05, denoted as: ns (P≥0.05), *P<0.05, **P<0.01, ***P<0.001. This study followed the STREGA reporting guidelines (22).

Results

Differential expression and functional characterization of mitochondrial genes in BC

In the TCGA cohort, differential expression analysis between BC and adjacent normal tissues was performed using three R packages (DESeq2, edgeR, and limma). This analysis identified 294 mitochondria-related DEGs, comprising 148 up-regulated and 146 down-regulated genes (Figure 1A), under the threshold of |log₂FC| >1 and P<0.05. Subsequent GO and KEGG enrichment analyses were performed to characterize the functional profiles of these DEGs. GO Functional enrichment analysis focuses on DEGs, involving BPs, MF and CC (Figure 1B). KEGG results indicated significant enrichment in several metabolism-related pathways such as carbon, lipid, and amino acid metabolism, along with the adipocytokine signaling pathway, insulin resistance, and PPAR signaling pathway (Figure 1C).

Figure 1 Identification and functional enrichment analysis of mitochondria-related differentially expressed genes in breast cancer. (A) A total of 294 mitochondria-related DEGs were identified in TCGA-BRCA. (B) GO functional enrichment analysis of the DEGs, including BP, MF, and CC. (C) KEGG pathway enrichment analysis of the DEGs, showing significantly enriched metabolic and signaling pathways. BP, biological process; CC, cellular component; CoA, coenzyme A; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; PPAR, peroxisome proliferator-activated receptor; TCGA, The Cancer Genome Atlas; TCGA-BRCA, TCGA Breast Invasive Carcinoma cohort.

Multiple machine learning approaches confirm link between mitochondrial genes and BC prognosis

Urvival data from BC patients were first curated by excluding those with follow-up periods of less than 30 days. Univariate Cox regression analysis implemented with the survival R package was used to evaluate the prognostic value of the DEGs. These genes included MRPL13, DCTPP1, NGB, GGCT, NDUFAF6, MTHFD2, TH, COMTD1, NOS1, FEZ1, MTFR2, C16orf91, ACSL1, MMP1, TIMM17A, CHPF, RAD51, APOO, STXBP1, KMO, BRI3BP, CCNB1, CASP14, MAPK10, PPP2R2B, TP53AIP1, BCL2A1, DOK7, TP63, MAFF, RNF186, PMAIP1, ADHFE1, TMEM71, ABCD2, and CCR7.

Using the expression profiles of these 36 prognostic genes, a predictive model was constructed with the TCGA dataset as the training set and validated using the METABRIC and GSE96058 datasets. A total of 117 prediction models were built via cross-validation with the Mime1 package (23), using a fixed random seed of 6666 (Figure 2A). The C-index of each model was calculated to evaluate predictive performance (Figure 2B). Following comprehensive evaluation, the model built with the Lasso + Survival-SVM algorithm was selected as the optimal prognostic predictor. Further, higher MT-risk scores were consistently associated with worse OS across all datasets (Figure 2C-2E).

Figure 2 Construction and validation of a mitochondria-related prognostic risk model in breast cancer. (A) Construction of 117 prediction models through ten-fold cross-validation using the Mime1 package across the TCGA-BRCA, METABRIC, and GSE96058 datasets. (B) C-indices of the optimal model (Lasso + Survival-SVM) in each dataset. (C-E) OS curves based on MT-risk scores in the TCGA-BRCA, GSE96058, and METABRIC datasets, respectively. CI, confidence interval; METABRIC, Molecular Taxonomy of Breast Cancer International Consortium; MT, mitochondria-related; OS, overall survival; SVM, support vector machine; TCGA, The Cancer Genome Atlas; TCGA-BRCA, TCGA Breast Invasive Carcinoma cohort.

Functional significance and selection of APOO as a core prognostic gene

ESTIMATE analysis revealed elevated tumor purity associated with both APOO and BRI3BP (tumor purity >0.30; Figure 3). CIBERSORT analysis further indicated limited associations with immune cell infiltration and fibroblast activation. This suggests their oncogenic effects may primarily arise from direct stimulation of tumor cell proliferation, consequently reducing the relative proportion of stromal components. Such a streamlined TME potentially fosters malignant progression, underscoring the therapeutic targeting potential of these genes.

Figure 3 Correlation analysis of 36 prognostic DEGs with tumor microenvironment and immune cell infiltration in TCGA-BRCA using ESTIMATE and CIBERSORT algorithms. Red boxes highlight genes of interest selected for further analysis based on relatively high tumor purity correlation (tumor purity >0.30), limited correlation with immune/stromal infiltration, and prognostic significance in univariate Cox analysis. CIBERSORT, Cell-type Identification By Estimating Relative Subsets of RNA Transcripts; DEGs, differentially expressed genes; ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; TCGA, The Cancer Genome Atlas; TCGA-BRCA, TCGA Breast Invasive Carcinoma cohort.

Univariate Cox regression analysis of the 36 prognosis-related DEGs identified APOO and BRI3BP as high-risk genes (HR >1), wherein elevated expression was significantly correlated with poorer prognoses (Figure 4A). Notably, APOO demonstrated greater statistical significance (P=0.03) than BRI3BP (P=0.04).

Figure 4 Prognostic significance, clinicopathological relevance, and drug sensitivity association of APOO in breast cancer. (A) Univariate Cox regression analysis of the 36 prognosis-related DEGs. The red box in panel A highlights APOO, which was selected as the key gene for subsequent single-gene analyses and experimental validation based on its prognostic significance, tumor microenvironment correlation pattern, and biological relevance. (B) Univariate and multivariate Cox regression analyses of the association between APOO expression and OS in TCGA-BRCA patients. (C-E) Differential expression of APOO between tumor and normal tissues in the TCGA-BRCA cohort and its correlation with clinicopathological features. (F) Drug sensitivity analysis predicting the effect of APOO expression levels 31 on the IC₅₀ of commonly used clinical chemotherapeutic agents. *, P<0.05; **, P<0.01; ***, P<0.001. APOO, apolipoprotein O; CI, confidence interval; ER, estrogen receptor; HR, hazard ratio; IC₅₀, half maximal inhibitory concentration; M, metastasis; N, node; OS, overall survival; SD, standard deviation; T, tumor; TCGA, The Cancer Genome Atlas; TCGA-BRCA, TCGA Breast Invasive Carcinoma cohort; TPM, transcripts per million.

Based on these findings—including its unique TME profile, stronger prognostic power, and well-established functions in mitochondrial integrity and metabolic reprogramming—APOO was selected as the central subject for subsequent single-gene investigation and in vitro validation to confirm both the reliability of the prognostic model and the robustness of the associated findings.

Prognostic significance and clinical relevance of APOO in BC

Of the 36 prognosis-related DEGs, 24 were categorized as high-risk genes (HR >1) and 12 as protective genes (HR <1). APOO was identified as a high-risk gene, with its expression demonstrating a significant association with poor prognosis in patients (Figure 4A). Notably, after adjustment for clinicopathological variables including age, gender, and TNM stage, multivariate analysis identified APOO as an independent prognostic factor (HR =1.47, 95% CI: 1.05–2.00, P=0.01; Figure 4B). This observation is consistent with transcriptomic data from the TCGA database, which revealed markedly elevated APOO expression in BC tissues compared to normal controls (P<0.001; Figure 4C), suggesting a putative role for APOO in tumorigenesis.

Survival analysis of the TCGA-BRCA cohort demonstrated that increased APOO expression was significantly associated with reduced OS in univariate Cox regression [HR =1.48, 95% confidence interval (CI): 1.11–1.87, P=0.007]. Further analysis revealed that APOO expression was significantly higher in T4 stage patients compared with T1–T3 stages (P<0.001), and exhibited elevated levels in ER/PR-positive subgroups (Figure 4D,4E).

Drug sensitivity analysis predicted that patients with high APOO expression exhibited reduced sensitivity to carboplatin (P<0.001), oxaliplatin (P<0.05), cyclophosphamide (P<0.001), and doxorubicin (P<0.01), while demonstrating relatively higher sensitivity to paclitaxel-based drugs (P<0.001; Figure 4F).

APOO is overexpressed in BC and demonstrates diagnostic and prognostic significance

Analysis of transcriptomic data from 98 paired BC and adjacent normal tissue samples in the TCGA-BRCA cohort revealed significantly upregulated APOO mRNA expression in tumor tissues (P<0.001; Figure 5A). ROC curve analysis showed that APOO had strong diagnostic performance, with an AUC of 0.906 (95% CI: 0.86–0.91; Figure 5B), indicating good discriminatory ability between tumor and normal tissues. Immunohistochemical data from the HPA further demonstrated higher APOO protein expression in breast cancer tissues than in normal breast tissues (Figure 5C), supporting the transcriptomic findings at the protein level. Consistently, RT-qPCR validation showed that APOO expression was significantly increased in three BC cell lines (MCF-7, MDA-MB-231, and BT-549) compared with the normal breast epithelial cell line MCF-10A (P<0.05; Figure 5D). Together, these findings suggest that APOO may play a role in BC pathogenesis and has potential diagnostic value.

Figure 5 Expression pattern, diagnostic value, and prognostic significance of APOO in breast cancer. (A) Paired t-test analysis of APOO mRNA expression between BC and adjacent normal tissues from the TCGA database (n=98). (B) ROC curve analysis evaluating the diagnostic performance of APOO expression in discriminating tumor from normal tissues in the TCGA-BRCA cohort. (C) Immunohistochemical comparison of APOO protein levels between BC (https://www.proteinatlas.org/ENSG00000184831-APOO/cancer/breast+cancer#img) and normal tissues (https://www.proteinatlas.org/ENSG00000184831-APOO/tissue/breast#img) from the HPA database. (D) APOO mRNA expression levels across various cell lines. (E,F) Kaplan-Meier curves depicting OS and DFS based on APOO expression stratification in TCGA-BRCA patients. *, P<0.05; ***, P<0.001. APOO, apolipoprotein O; AUC, area under the curve; BC, breast cancer; CI, confidence interval; DFS, disease-free survival; FPR, false positive rate; mRNA, messenger RNA; OS, overall survival; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; TCGA-BRCA, TCGA Breast Invasive Carcinoma cohort; TPM, transcripts per million; TPR, true positive rate.

Consistent with previous univariate Cox regression results, survival analysis based on the optimal expression cutoff demonstrated that high APOO expression was associated with significantly poorer OS (P<0.001; Figure 5E) and disease-free survival (DFS) (P<0.05; Figure 5F), reinforcing its prognostic relevance in BC.

APOO knockdown suppresses proliferation, anti-apoptotic capacity and migration in BC cells

To investigate the role of APOO in BC, this study constructed stable gene knockdown cell lines in MDA-MB-231 and BT-549 models using two small interfering RNAs (shRNAs). Efficient silencing was verified by Western blot analysis (Figure 6A, 6B) and RT-qPCR (Figure 6C,6D), demonstrating significant reductions in APOO expression in both cell lines at the protein and mRNA levels, respectively.

Figure 6 Effects of APOO knockdown on proliferation, apoptosis, and migration of breast cancer cells. (A-D) Assessment of APOO knockdown efficiency in MDA-MB-231 and BT-549 cell lines by qPCR and Western blot. (E,F) Colony formation assays showing the effect of APOO knockdown on proliferative capacity in MDA-MB-231 and BT-549 cells. Colonies were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. (G,H) Apoptosis assay evaluating the effect of APOO silencing on cell death. (I,J) Wound healing assay analyzing the role of APOO in cell migration. Representative images were captured at ×100 magnification. *, P<0.05; **, P<0.01; ***, P<0.001. APOO, apolipoprotein O; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; qPCR, quantitative polymerase chain reaction.

APOO depletion markedly impaired malignant phenotypes in vitro. Colony formation assays revealed substantial suppression of proliferative capacity in both MDA-MB-231 and BT-549 cells (Figure 6E,6F). Apoptosis analysis demonstrated that APOO knockdown significantly promoted cell death, particularly evidenced by increased early and late apoptotic populations (Figure 6G,6H). Furthermore, wound healing assays indicated impaired migratory ability upon APOO silencing, with migration reduced by approximately 50% in both cell lines (Figure 6I,6J).

Collectively, these results establish APOO as a multi-functional regulator in BC pathogenesis, governing proliferative capacity, apoptotic resistance, and migratory behavior. These experimental findings align with clinical observations of APOO overexpression in tumor tissues (Figure 5A-5C) and its correlation with adverse patient outcomes (Figure 5E,5F), reinforcing its dual role as both a prognostic indicator and functional oncogene in BC progression.

APOO modulates lipid metabolic reprogramming in BC

BC development is accompanied by profound metabolic alterations, with lipid metabolic reprogramming emerging as a critical molecular hallmark (24). While the specific mechanism of APOO in BC lipid metabolism requires further investigation, accumulating evidence supports its involvement in mitochondrial function and lipid metabolic processes—consistent with our previous GO/KEGG enrichment analysis of DEGs. Spearman correlation analysis demonstrated significant positive correlations between APOO expression and key lipid metabolism genes (HMGCR, DGAT1, and ACLY), which are centrally involved in TG, TC, and fatty acid metabolism, respectively (Figure 7A-7C).

Figure 7 APOO promotes lipid metabolic reprogramming in breast cancer cells. (A-C) Pearson correlation analysis of co-expression relationships between APOO and key lipid metabolism genes (HMGCR, DGAT1, ACLY) in TGCA dataset, involved in triglyceride, total cholesterol, and free fatty acid metabolism. (D-F) Measurement of triglyceride, total cholesterol, and free fatty acid levels in stable transfected cells validated the impact of APOO knockdown on lipid metabolism. (G,H) APOO overexpression rescued the decrease in cell viability induced by the lipid metabolism inhibitor SB204990. *, P<0.05; **, P<0.01; ***, P<0.001. APOO, apolipoprotein O; DMSO, dimethyl sulfoxide; NC, negative control; OD, optical density; TCGA, The Cancer Genome Atlas.

To further investigate APOO’s functional role, we assessed changes in key lipid metabolites following APOO knockdown. The results showed that APOO gene silencing significantly inhibited lipid synthesis in BC cells, as evidenced by marked reductions in TG, TC, and FFAs (Figure 7D-7F). These suppressive effects were consistently observed (25) in both MDA-MB-231 and BT-549 cell lines, confirming the robustness of APOO’s regulatory function in lipid metabolism.

Furthermore, to substantiate the impact of APOO on lipid metabolic reprogramming, we conducted CCK-8 assays using the ACLY inhibitor SB-204990. Functional rescue experiments confirmed that APOO overexpression effectively restored cell viability suppressed by this lipid metabolism inhibitor (Figure 7G,7H). Together, these results establish APOO as a crucial regulator of lipid metabolic homeostasis in BC cells and provide new insights into metabolic adaptations that drive tumor progression.

Discussion

The pursuit of overcoming therapeutic resistance and tumor recurrence represents a central challenge in BC treatment, directing increasing attention toward tumor metabolism, with a specific focus on mitochondrial metabolic reprogramming as a key regulatory mechanism in disease progression. Substantial evidence now underscores that mitochondrial functionality extends well beyond bioenergetics, critically influencing diverse malignant phenotypes in BC, including proliferation, survival, invasion, and treatment failure (5,6,26).

To systematically investigate the mechanistic roles of mitochondria-associated genes, this study integrated data from the GSEA and MitoCarta3.0 databases with RNA-seq data from the TCGA-BRCA cohort. Our initial analyses revealed potential roles for mitochondrial-related DEGs in BC pathogenesis. Subsequent univariate Cox regression identified 36 prognosis-linked genes, and the application of multiple machine learning algorithms led to the construction and validation of a robust MT-risk scoring model, offering a novel prognostic tool. Among the candidate genes, APOO/MIC26—a core subunit of the MICOS complex—emerged as a focus due to its established role in maintaining cristae architecture and metabolic homeostasis. APOO encodes multiple isoforms, including a 55-kDa glycosylated form and a 22-kDa mitochondrial protein (MIC26), the latter being the main functional variant. Recent discoveries have redefined APOO/MIC26—once viewed as a mere structural component of the mitochondrial inner membrane—as an active metabolic rheostat that dynamically tunes mitochondrial substrate selection between glucose oxidation and fatty acid oxidation (27). Beyond this intramitochondrial role, APOO deficiency has been shown to disrupt mitochondria-peroxisome crosstalk via PPARα dysregulation, causing accumulation of very long-chain fatty acids and subsequent lipotoxicity in brown adipose tissue (16). Moreover, studies using Apoo^–/– mouse models have uncovered that APOO governs cholesterol metabolism through the NRF2/CYB5R3 pathway independently of the LDL receptor (17). Collectively, these findings establish APOO as a multifaceted metabolic hub with previously unrecognized regulatory functions, and strongly suggest that similar mechanisms may be co-opted in BC to support the metabolic demands of tumor progression. APOO’s role in BC was unclear despite its involvement in metabolic disorders like obesity and atherosclerosis. Our research showed that APOO is significantly upregulated in BC tissues, and this upregulation correlates with poor patient outcomes. Experiments revealed that APOO knockdown inhibited cell proliferation and migration, induced apoptosis, and decreased TG, TC, and FFAs. APOO overexpression also reversed the inhibitory effects on lipid metabolism caused by SB-204990, indicating that APOO may promote tumorigenesis in BC by altering lipid metabolism.

This finding aligns with the recognized importance of lipid metabolic reprogramming as a cancer hallmark, known to promote tumor growth, metastasis, and immune evasion by enhancing lipid uptake, synthesis, and storage (28,29). In BC, disrupted lipid metabolism leads to aberrant FFA accumulation, which in turn induces excessive mitochondrial reactive oxygen species (ROS) production, oxidative stress, and macromolecular damage, thereby perpetuating a vicious cycle that fuels tumor progression (30,31). Although no prior studies had directly linked APOO to the regulation of lipid metabolic pathways in BC, our enrichment analysis indicated significant involvement of mitochondria-related genes, including APOO, in lipid metabolism pathways. Corroborating this, in vitro results demonstrated that APOO knockdown not only attenuated malignant phenotypes but also significantly reduced multiple lipid species, collectively identifying APOO as a potential key regulator of lipid metabolic reprogramming in BC.

Based on the current evidence, we propose the following hypothesis: APOO, as a core subunit of the MICOS complex, not only maintains mitochondrial cristae integrity but also actively regulates cellular energy metabolism and lipid homeostasis, thereby playing an oncogenic role in BC progression.

Mechanistically, APOO localizes to the inner mitochondrial membrane and interacts with subunits such as MIC60, MIC27, and MIC10 to participate in cristae junction formation and maintenance (15,32). This structural foundation not governs the supramolecular assembly of the respiratory chain and oxidative phosphorylation efficiency but also influences lipid metabolic remodeling, particularly cholesterol metabolism (17). At the metabolic level, APOO promotes mitochondrial uncoupling, enhances basal oxygen consumption, and accelerates fatty acid uptake and β-oxidation by upregulating fatty acid transporters and long-chain acyl-CoA synthetase activity (18,27,33). This process increases reactive oxygen species generation, activates the AMPK signaling pathway, and subsequently reshapes the cellular metabolic network (18).

In BC, sustained APOO overexpression may hijack these mitochondrial functions to support malignant phenotypes: enhanced respiratory efficiency and fatty acid oxidation provide energy and biosynthetic precursors for rapid cancer cell proliferation; stabilized cristae architecture prevents cytochrome c release, conferring apoptosis resistance (34); and increased mitochondrial ROS (mtROS) generation has been demonstrated to upregulate MAP2K1/MEK1 expression, activate ROS-sensitive transcription factors NF-κB and AP1, and consequently stimulate BC cell migration (35). Collectively, APOO exerts its oncogenic effects through a tripartite regulatory network encompassing structure, metabolism, and signaling within mitochondria. Targeting APOO-mediated mitochondrial metabolic remodeling may represent a potential therapeutic strategy. It is important to acknowledge several limitations of our study. The functional conclusions are primarily drawn from APOO knockdown models, and future studies incorporating overexpression rescue experiments are needed to confirm phenotypic specificity. Furthermore, the lack of in vivo animal models has limited our understanding of APOO’s functions in TMEs and physiological conditions. Since previous studies have reported that MIC26 induces significant alterations in mitochondrial ultrastructure (15,27,36), this study did not employ transmission electron microscopy for ultrastructural analysis of mitochondrial cristae morphology. The specific downstream effectors of APOO’s lipid metabolism and its potential interactions with other MICOS components remain unclear. Therefore, future research should prioritize constructing animal models for systematic in vivo validation.

Conclusions

This study shows that mitochondrial metabolic reprogramming, regulated by APOO/MIC26, drives BC progression; a mitochondria-related gene risk model helps predict outcomes, and APOO depletion limits cancer cell growth, movement, and fat buildup while boosting cell death by altering lipid metabolism, suggesting APOO could be a useful marker and treatment target, with more research needed on its in vivo effects and combination therapy potential.

Acknowledgments

We are deeply grateful to all the contributing authors for their crucial contributions to the research. We also owe our thanks to the Harbin Medical University Cancer Hospital for its backing.

Footnote

Reporting Checklist: The authors have completed the MDAR and TRIPOD reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2554/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2554/prf

Funding: This study was supported by the Wu Jie Ping Medical Foundation (grant No. 320.6750.18215), the Haiyan Science Foundation (grant No. JJZD2021-02), and the China Primary Health Care Foundation (grant No. cphcf-2023-017).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-aw-2554/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/.

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