Integrative multi-omics analysis identifies mitochondria- and ferroptosis-related prognostic genes in cervical cancer.

Abstract

Background: Mitochondria and ferroptosis are crucial in tumorigenesis. However, their specific role in cervical cancer (CC) remains unclear. This study aimed to identify and validate prognostic genes linked to mitochondrial function and ferroptosis in CC.

Methods: Publicly available datasets were analyzed, including 306 CC tumor samples from The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC), with survival data for 293 samples, a training set of 24 normal and 33 tumor tissues (GSE9750), and a validation set of 300 tumor tissues (GSE44001). Prognostic genes associated with mitochondria-related genes (MRGs) and ferroptosis-related genes (FRGs) were identified through machine learning, univariate Cox regression, Weighted Gene Co-expression Network Analysis (WGCNA), Mendelian randomization (MR), differential expression analysis, and multivariate Cox analysis. A risk model was constructed and validated, with the High-Risk Group (HRG) and Low-Risk Group (LRG) defined by optimal risk score thresholds. Independent prognostic analysis, functional enrichment, immune infiltration profiling, and single-cell resolution studies were conducted to explore the underlying molecular mechanisms. Additionally, gene expression was validated in five paired clinical samples (5 tumor/5 normal tissues) using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results: HSDL2, AMACR, and CBR3 were identified as prognostic genes. The risk model indicated significantly poorer survival rates in HRG patients (P < 0.05). It demonstrated strong predictive performance, with area under the curve (AUC) values exceeding 0.7 in both the training and validation sets. The risk score, tumor (T) stage, and lymph node (N) stage were identified as independent prognostic factors for a nomogram model (hazard ratio (HR ≠ 1, P < 0.5). Pathways co-enriched by these markers, such as allograft rejection, were investigated. Immune infiltration analysis revealed significant differences between HRG and LRG in M0 macrophages and resting myeloid dendritic cells (mDCs) (P < 0.5). Macrophages and epithelial/cancer cells were identified as key contributors to CC progression, exhibiting 13 and 7 distinct differentiation states, respectively, in pseudo-time analysis. Notably, HSDL2 and CBR3 expression levels were significantly different between normal and CC samples (P < 0.05).

Conclusion: HSDL2, AMACR, and CBR3 were established as prognostic biomarkers for CC. The risk model demonstrated robust predictive accuracy, offering a scientific foundation for clinical prognosis prediction in CC.