Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease. Recent evidence suggests that the pathogenesis of IPF may involve abnormalities in mitochondrial energy metabolism. This

study aimed to identify mitochondrial energy metabolism related differentially expressed genes (MEMRDEGs) and to elucidate their potential mechanistic involvement in IPF. We employed a

multistep bioinformatics approach, including data extraction from the Gene Expression Omnibus database, removal of batch effects, and normalization and differential gene expression analyses.

We then conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses. A protein-protein interaction network was constructed from the STRING

gene set enrichment analysis revealed significant differences in the abundances of specertainfic immune cell types between IPF samples and controls. In conclusion, COX5A, EHHADH, and SDHB

are potential biomarkers for the high-accuracy diagnosis of IPF. These findings pave the way for further investigations into the molecular mechanisms underlying IPF.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with unknown causes that mainly manifests as a chronic progressive development and primarily affects older people. The

global incidence of IPF is estimated to be approximately 10.7 per 100,000 person-years with a median survival time of 3–5 years1. Despite recent advances in therapeutic interventions,

options remain limited and are often associated with significant side effects and only modest efficacy in slowing disease progression2. Lung transplantation remains the only curative option;

however, it is available for only a small subset of patients, owing to stringent eligibility criteria and organ availability3. Early diagnosis is conducive to timely intervention, delaying

the progression of the disease.The need for improved diagnostic and therapeutic strategies underscores the urgency of research into the molecular mechanisms underlying IPF. Our primary

research objective is to provide a basis for improving early diagnosis and guiding treatment strategies by identifying diagnostic biomarkers in IPF.

Mitochondrial dysfunction has been implicated in various pulmonary diseases, including IPF, and alterations in energy metabolism may contribute to its pathophysiology4. Studies have shown

that mitochondrial energy metabolism related differentially expressed genes (MEMRDEGs) play pivotal roles in other fibrotic diseases, suggesting common therapeutic pathways5. Moreover,

mitochondrial dynamics and bioenergetics have been linked to cellular senescence and myofibroblast differentiation, which are key features of IPF pathology6. These findings highlight the

potential of MEMRDEGs as diagnostic biomarkers and therapeutic targets in IPF.

In recent years, studies have shown that the immune microenvironment plays a crucial role in the pathogenesis of IPF. Immune cells, incluchding macrophages and T cells, promote the fibrotic

process by releasing pro-inflammatory and profibrotic cytokines (e.g., TGF-β, IL-6)7,8. Meanwhile, mitochondrial dysfunction is closely related to the immune response, which may further

exacerbate IPF through oxidative stress and metabolic9.

Our study aimed to elucidate the role of MEMRDEGs in IPF by integrating multi-platform datasets and employing a series of bioinformatic analyses. We conducted differential expression

analysis, functional enrichment studies, protein-protein interaction (PPI) network construction, regulatory network mapping, diagnostic receiver operating characteristic (ROC) curve

evaluation, and immune infiltration evaluation. Gene network analysis and complex network topology are integral to understanding these processes and have been effectively utilized in recent

bioinformatics research10,11.This comprehensive approach enabled the identification of the key genes and pathways involved in IPF pathogenesis.

Our study provides new insights into the mitochondrial energy metabolism signature in IPF and offers promising avenues for future diagnostic and therapeutic development. Identifying the

crucial MEMRDEGs and their regulatory networks will enhance our understanding of the molecular mechanisms underlying this debilitating disease.

We obtained the IPF datasets GSE2420612,13 and GSE11014713,14 from the Gene Expression Omnibus (GEO) database15 using the R package GEOquery16 ( https://www.ncbi.nlm.nih.gov/geo/ ). The

samples from the GSE24206 and GSE110147 datasets were all from Homo sapiens, and the tissue source was the lungs. The chip platform for dataset GSE24206 was GPL570, while the chip platform

for dataset GSE110147 was GPL6244. Specific information is shown in Table 1.The dataset GSE24206 contained 17 IPF and 6 control samples. This study included both IPF and control samples.

Dataset GSE110147 contained 22 IPF, 10 non-specific interstitial pneumonia, 5 IPF-non-specific interstitial pneumonia and 11 control samples, additionally 22 IPF and 11 control samples were

included in this study.

We collected Mitochondrial Energy Metabolism related genes(MEMRGs) from the GeneCards database17 ( https://www.genecards.org/) and relevant literature. We used the term “Mitochondrial Energy

Metabolism” as the keyword, and 47 MEMRGs were obtained after keeping only genes with “Protein Coding” and “Relevance Score > 1.” Additionally, “Mitochondrial Energy Metabolism” was used as

a keyword on the PubMed website (https://pubmed.ncbi.nlm.nih.gov/) to find published literature18 on the Mitochondrial Energy Metabolism related genes set. A total of 28 MEMRGs were

included in this study. A total of 74 MEMRGs were obtained after combined duplication removal; detailed information is shown in Table S1.

The R package sva19 was used to debatch GSE24206 and GSE110147 to obtain the combined GEO datasets. The combined datasets included 39 IPF and 17 control samples. Finally, we used the R

package limma20 to ormardlize the combined GEO dataset, annotate probes, and standardize them. The expression matrices before and after removing the batch effect were subjected to principal

component analysis (PCA)21 to verify the efficacy of removing the batch effect. Data were transformed into low-dimensional data and displayed as two- or three-dimensional graphs.

The samples were divided into IPF and control groups according to the sample grouping of the combined GEO datasets. Differential analysis of genes between the IPF and control samples was

performed using the R package limma. To incorporate as many differentially expressed genes with potential biological significance as possible, and to comprehensively identify potential

important biomarkers, | logFC | > 0.25 and adj. p