Prognosis prediction and drug guidance of ovarian serous cystadenocarcinoma through mitochondria gene-based model.

BACKGROUND

Mitochondrial dysregulation contributes to the chemoresistance of multiple cancer types. Yet, the functions of mitochondrial dysregulation in Ovarian serous cystadenocarcinoma (OSC) remain largely unknown.

AIM

We sought to investigate the function of mitochondrial dysregulation in OSC from the bioinformatics perspective. We aimed to establish a model for prognosis prediction and chemosensitivity evaluation of the OSC patients by targeting mitochondrial dysregulation.

METHODS

Differentially expressed genes (DEGs) were screened from the Cancer Genome Atlas (TCGA)-OV dataset and the mitochondrial-related DEGs were identified from the Human MitoCarta 3.0 database. Prognosis-related mitochondria-related genes (MRGs) were screened to establish the MRGs-based risk score model for prognosis prediction. To validate the risk score model, the risk score model was then evaluated by IHC staining intensity and survival curves from clinical specimens of OSC patients. Migration and proliferation assays were performed to elucidate the role of carcinogenic gene ACSS3 in serous ovarian cancer cell lines.

RESULTS

Using consensus clustering algorithm, we identified 341 MRGs and two subtypes of OSC patients. Moreover, we established a novel prognostic risk score model by combining the transcription level, intensity and extent scores of MRGs for prognosis prediction purpose. The model was established using 7 MRGs (ACOT13, ACSS3, COA6, HINT2, MRPL14, NDUFC2, and NDUFV2) significantly correlated to the prognosis of OSC. Importantly, by performing the drug sensitivity analysis, we found that the OSC patients in the low-risk group were more sensitive to cisplatin, paclitaxel and docetaxel than those in the high-risk group, while the latter ones were more sensitive to VEGFR inhibitor Axitinib and BRAF inhibitors Vemurafenib and SB590885. In addition, patients in the low-risk group were predicted to have better response in anti-PD-1 immunotherapy than those in the high-risk group. The risk score model was then validated by survival curves of high-risk and low-risk groups determined by IHC staining scores of OSC clinical samples. The carcinogenic effect of ACSS3 in OSC was confirmed through the knockdown of ACSS3 in SKOV3 and HO-8910 cells.

CONCLUSION

To summarize, we established a novel 7 MRGs - based risk score model that could be utilized for prognosis prediction and chemosensitivity assessment in OSC patients.