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Identification and validation of critical mitochondrial hub genes for prostate cancer

  • Authors:
    • Sha Liu
    • Liang Huang
    • Li Lin
    • Hong Shan
    • Yueming Wan
  • View Affiliations / Copyright

    Affiliations: Department of Urology, The Affiliated Cancer Hospital of Xiangya School of Medicine (Hunan Cancer Hospital), Central South University, Changsha, Hunan 410013, P.R. China, Department of Emergency Medicine, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan 410028, P.R. China, Department of Urology, Yueyang Hospital Affiliated to Hunan Normal University (Yueyang People's Hospital), Yueyang, Hunan 414000, P.R. China
    Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 66
    |
    Published online on: December 3, 2025
       https://doi.org/10.3892/ol.2025.15419
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Abstract

Prostate cancer (PCa) is one of the most common malignant tumors in men. In recent years, mitochondrial dysfunction has been found to be closely related to cancer progression. However, the role of mitochondria‑related genes in PCa remains unclear. The aim of the present study was to discover novel biomarkers based on differentially expressed mitochondrial‑related genes (DeMRGs) to aid in PCa diagnosis. In the present study, gene expression data from the Gene Expression Omnibus and The Cancer Genome Atlas databases were combined with a mitochondrial‑related gene list provided by the MitoCarta database to identify DeMRGs. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis and Gene Set Enrichment Analysis were then used to investigate the functions and related pathways of these DeMRGs. Subsequently, Cytoscape software and the STRING website were used to explore the transcription factors and microRNAs related to the DeMRGs. The degree of infiltration of immune cells in the immune landscape of patients with PCa and the controls was assessed using CIBERSORT. Finally, the correlation between characteristic DeMRGs and immune cell infiltration and mitochondrial respiration was analyzed. The results indicated that 6 characteristic genes, including acetyl‑CoA carboxylase β (ACACB), pyruvate dehydrogenase kinase 4 (PDK4), glycine amidinotransferase (GATM), methylcrotonyl‑CoA carboxylase subunit 2, mitochondrial ribosomal protein L12 (MRPL12) and fatty acid synthase, were identified from the 60 DeMRGs. The results showed a close association between the characteristic DeMRGs and immune infiltrating cells. In addition, it was found that MRPL12, PDK4, ACACB and GATM were correlated with mitochondrial respiration. These 4 genes were selected as hub genes as they are closely related to gluconeogenesis, the tricarboxylic acid cycle, lipid metabolism, amino acid metabolism and other mitochondrial metabolic pathways in PCa. In conclusion, 4 novel mitochondrial‑related gene signatures that influence mitochondrial metabolism within the immune microenvironment were identified in PCa.
View Figures

Figure 1

Differential expression of mRNA in
mitochondria between prostate cancer and normal prostate tissue.
(A) Differential analysis was conducted on three training sets
(TCGA-PRAD, GSE46602 and GSE55945). (B) The left sides are the Venn
diagram, and the right side is the expression heatmap. The top
images are for GSE55945 and the bottom images are for GSE46602;
TCGA, The Cancer Genome Atlas; PRAD, prostate adenocarcinoma; ns,
no significance; FASN, fatty acid synthase; MIPEP, mitochondrial
intermediate peptidase; AMACR, α-methylacyl-CoA racemase; PYCR1,
pyrroline-5-carboxylate reductase 1; ACSM1, acyl-CoA synthetase
medium chain family member 1; CHDH, choline dehydrogenase; N,
normal; T, tumor; FC, fold change.

Figure 2

KEGG analysis and GSEA of
differential mitochondrial mRNA between prostate cancer and normal
prostate tissue. (A) KEGG enrichment analysis of upregulated
mitochondrial genes; (B) GSEA of upregulated mitochondrial genes;
(C) KEGG enrichment analysis of downregulated mitochondrial genes;
and (D) GSEA of downregulated mitochondrial genes. KEGG, Kyoto
Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment
Analysis.

Figure 3

Interaction between differential mRNA
and miRNA and transcription factors between prostate cancer and
normal prostate tissue. (A) The 54 upregulated genes were first
used to construct a PPI network through the STRING website, which
was then imported into Cytoscape to screen out 18 core genes with a
degree value >4, namely the genes shown in red on the right; (B)
The transcription factor and miRNA interactions of these 18 genes
predicted by the NetworkAnalyst 3.0 website. Red represents the 18
core genes, and the larger the degree value, the larger the circle
size. Green represents the transcription factors and light blue
represents miRNA. (C) The 63 downregulated genes were first
constructed into a PPI network through the STRING website, which
was then imported into Cytoscape to screen out 16 core genes with a
degree value >4, namely the genes in dark blue on the right; (D)
The transcription factors and interacting miRNAs of these 16 genes
were predicted by NetworkAnalyst 3.0 and visualized by Cytoscape.
Blue represents the 16 core genes, with larger degree values shown
with larger circles. Green represents the transcription factors and
the pale orange represents miRNAs. miRNA, microRNA; PPI,
protein-protein interaction.

Figure 4

Relationship between core
differential mitochondrial mRNA and tumor immune infiltration in
PCa and normal prostate tissue. (A) TCGA-PRAD dataset was used to
calculate the immune score of the transcripts per million
expression matrix of PCa tissue (502 cases), which is shown as a
heatmap. The genes on the x-axis are the core genes selected from
TCGA-PRAD. The color of the heatmap represents the correlation
coefficient. The larger the box is, the more statistically
significant the correlation. (B) The box plot shows the 6 genes
most closely related to immunity, with P-values determined by
paired t-test. The R package ggplot2 was used to examine the
correlation between hub genes and immune cells, with the results
presented as lollipop plots. The relationship between (C) MRPL12,
(D) MCCC2, (E) FASN, (F) PDK4, (G) GTAM and (H) ACACB with immune
cell infiltration. The size of the points represents the absolute
value of the correlation coefficient and the larger the points, the
more correlated they are. The greener the point, the smaller the
P-value. *P<0.05, **P<0.01, ***P<0.001. PCa, prostate
cancer; TCGA, The Cancer Genome Atlas; PRAD, prostate
adenocarcinoma; ACACB, acetyl-CoA carboxylase β; PDK4, pyruvate
dehydrogenase kinase 4; GATM, glycine amidinotransferase; MCCC2,
methylcrotonyl-CoA carboxylase subunit 2; MRPL12, mitochondrial
ribosomal protein L12; FASN, fatty acid synthase.

Figure 5

Validation of the relationship
between MCCC2, MRPL12, FASN, PDK4, ACACB and GATM genes and tumor
immunity in prostate cancer and normal prostate tissue. (A)
Validation of the relationship between the MCCC2, MRPL12, FASN,
PDK4, ACACB and GATM genes and tumor immunity in PCa and normal
prostate tissue. The data is sourced from the validation dataset
GSE70770. (B) The proportion of 26 immune cells in PCa and adjacent
tissues in the validation dataset GSE70770. (C) The correlation
between 6 core genes and immune cells in the validation dataset
GSE70770. The darker the color, the stronger the correlation. The
upper right corner shows the correlation between these genes and
immune cells, while the lower left corner shows the correlation
between these genes and immune cells in PCa. *P<0.05,
**P<0.01, ***P<0.001; ****P<0.0001. ACACB, acetyl-CoA
carboxylase β; PDK4, pyruvate dehydrogenase kinase 4; GATM, glycine
amidinotransferase; MCCC2, methylcrotonyl-CoA carboxylase subunit
2; MRPL12, mitochondrial ribosomal protein L12; FASN, fatty acid
synthase; PCa, prostate cancer.

Figure 6

Validation of the relationship
between the MCCC2, MRPL12, FASN, PDK4, ACACB and GATMM genes and
immune cells in prostate cancer and normal prostate tissue. (A) The
relationship between the core MCCC2, MRPL12, FASN, PDK4, ACACB and
GATMM genes and immune cells, using the validation dataset
GSE70770. (B) The differences between cancer and adjacent immune
cells, as well as the relationship between the expression levels of
the 6 core genes and immune cells, using the validation dataset
GSE70770. *P<0.05, **P<0.01, ***P<0.001; ****P<0.0001.
ns, no significance; ACACB, acetyl-CoA carboxylase β; PDK4,
pyruvate dehydrogenase kinase 4; GATM, glycine amidinotransferase;
MCCC2, methylcrotonyl-CoA carboxylase subunit 2; MRPL12,
mitochondrial ribosomal protein L12; FASN, fatty acid synthase.

Figure 7

Relationship between mitochondrial
respiration and prostate cancer. (A) Relationship between
mitochondrial respiration and prostate cancer, as well as the
relationship with 6 core genes. The dataset used is TCGA-PRAD. The
genes of the five stages of mitochondrial respiration were provided
by the MitoCarta 3.0 database, and statistically significant genes
were screened out; (B) Using the TCGA-PRAD dataset, genes with
|statistical|>6 from (A) were selected and the correlation
between these genes and CIBERSORT immune infiltrating cells was
calculated. (C) Using the TCGA-PRAD dataset, the correlation
between the 6 core genes and mitochondrial respiratory chain genes
(|statistical|>4) was calculated. *P<0.05, **P<0.01,
***P<0.001; ****P<0.0001. TCGA, The Cancer Genome Atlas;
PRAD, prostate adenocarcinoma; ACACB, acetyl-CoA carboxylase β;
PDK4, pyruvate dehydrogenase kinase 4; GATM, glycine
amidinotransferase; MCCC2, methylcrotonyl-CoA carboxylase subunit
2; MRPL12, mitochondrial ribosomal protein L12; FASN, fatty acid
synthase;

Figure 8

Potential correlation between 4 hub
genes (MRPL12, PDK4, ACACB and GATM) and immune receptors and
mitochondrial metabolism. (A) Using the TCGA-PRAD dataset, the
differences between DAMP-related genes (11) and immune receptor-related genes
(21) in PCa and adjacent tissues
were detected; the genes with significant differences
(**P<0.001) were selected for further study. (B) The correlation
between the 4 hub genes and the significant differences in
DAMP-related genes and immune receptors selected in (a). (C)
Further investigation of the potential correlation between the 4
hub genes and DAMPs. *P<0.05, **P<0.01, ***P<0.001. PRAD,
prostate adenocarcinoma; ACACB, acetyl-CoA carboxylase β; PDK4,
pyruvate dehydrogenase kinase 4; GATM, glycine amidinotransferase;
MCCC2, methylcrotonyl-CoA carboxylase subunit 2; MRPL12,
mitochondrial ribosomal protein L12; FASN, fatty acid synthase;
TCGA, The Cancer Genome Atlas.

Figure 9

RT-qPCR validation of the core genes.
Relative RNA levels of ACACB, PDK4, GATM, MCCC2, MRPL12 and FASN
were determined by the RT-qPCR for the RWPE-1, PC-3, LNcap and
Du145 cell lines. *P<0.05, **P<0.01, ***P<0.001; n=3.
RT-qPCR, reverse transcription-quantitative PCR; ACACB, acetyl-CoA
carboxylase β; PDK4, pyruvate dehydrogenase kinase 4; GATM, glycine
amidinotransferase; MCCC2, methylcrotonyl-CoA carboxylase subunit
2; MRPL12, mitochondrial ribosomal protein L12; FASN, fatty acid
synthase.
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Copy and paste a formatted citation
Spandidos Publications style
Liu S, Huang L, Lin L, Shan H and Wan Y: Identification and validation of critical mitochondrial hub genes for prostate cancer. Oncol Lett 31: 66, 2026.
APA
Liu, S., Huang, L., Lin, L., Shan, H., & Wan, Y. (2026). Identification and validation of critical mitochondrial hub genes for prostate cancer. Oncology Letters, 31, 66. https://doi.org/10.3892/ol.2025.15419
MLA
Liu, S., Huang, L., Lin, L., Shan, H., Wan, Y."Identification and validation of critical mitochondrial hub genes for prostate cancer". Oncology Letters 31.2 (2026): 66.
Chicago
Liu, S., Huang, L., Lin, L., Shan, H., Wan, Y."Identification and validation of critical mitochondrial hub genes for prostate cancer". Oncology Letters 31, no. 2 (2026): 66. https://doi.org/10.3892/ol.2025.15419
Copy and paste a formatted citation
x
Spandidos Publications style
Liu S, Huang L, Lin L, Shan H and Wan Y: Identification and validation of critical mitochondrial hub genes for prostate cancer. Oncol Lett 31: 66, 2026.
APA
Liu, S., Huang, L., Lin, L., Shan, H., & Wan, Y. (2026). Identification and validation of critical mitochondrial hub genes for prostate cancer. Oncology Letters, 31, 66. https://doi.org/10.3892/ol.2025.15419
MLA
Liu, S., Huang, L., Lin, L., Shan, H., Wan, Y."Identification and validation of critical mitochondrial hub genes for prostate cancer". Oncology Letters 31.2 (2026): 66.
Chicago
Liu, S., Huang, L., Lin, L., Shan, H., Wan, Y."Identification and validation of critical mitochondrial hub genes for prostate cancer". Oncology Letters 31, no. 2 (2026): 66. https://doi.org/10.3892/ol.2025.15419
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