Abstract
BACKGROUND
Both mitophagy and long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC). We sought to explore the characteristics of mitophagy-related gene (MRG) and mitophagy-related lncRNAs (MRL) to facilitate treatment and prognosis of OC.
METHODS
The processed data were extracted from public databases (TCGA, GTEx, GEO and GeneCards). The highly synergistic lncRNA modules and MRLs were identified using weighted gene co-expression network analysis. Using LASSO Cox regression analysis, the MRL-model was first established based on TCGA and then validated with four external GEO datasets. The independent prognostic value of the MRL-model was evaluated by Multivariate Cox regression analysis. Characteristics of functional pathways, somatic mutations, immunity features, and anti-tumor therapy related to the MRL-model were evaluated using abundant algorithms, such as GSEA, ssGSEA, GSVA, maftools, CIBERSORT, xCELL, MCPcounter, ESTIMATE, TIDE, pRRophetic and so on.
RESULTS
We found 52 differentially expressed MRGs and 22 prognostic MRGs in OC. Enrichment analysis revealed that MRGs were involved in mitophagy. Nine prognostic MRLs were identified and eight optimal MRLs combinations were screened to establish the MRL-model. The MRL-model stratified patients into high- and low-risk groups and remained a prognostic factor (P < 0.05) with independent value (P < 0.05) in TCGA and GEO. We observed that OC patients in the high-risk group also had the unfavorable survival in consideration of clinicopathological parameters. The Nomogram was plotted to make the prediction results more intuitive and readable. The two risk groups were enriched in discrepant functional pathways (such as Wnt signaling pathway) and immunity features. Besides, patients in the low-risk group may be more sensitive to immunotherapy (P = 0.01). Several chemotherapeutic drugs (Paclitaxel, Veliparib, Rucaparib, Axitinib, Linsitinib, Saracatinib, Motesanib, Ponatinib, Imatinib and so on) were found with variant sensitivity between the two risk groups. The established ceRNA network indicated the underlying mechanisms of MRLs.
CONCLUSIONS
Our study revealed the roles of MRLs and MRL-model in expression, prognosis, chemotherapy, immunotherapy, and molecular mechanism of OC. Our findings were able to stratify OC patients with high risk, unfavorable prognosis and variant treatment sensitivity, thus improving clinical outcomes for OC patients.
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