BACKGROUND

Patients with ovarian cancer (OC) tend to face a poor prognosis due to a lack of typical symptoms and a high rate of recurrence and chemo-resistance. Therefore, identifying representative and reliable biomarkers for early diagnosis and prediction of chemo-therapeutic responses is vital for improving the prognosis of OC.

METHODS

Expression levels, IHC staining, and subcellular distribution of eight ITGBs were analyzed using The Cancer Genome Atlas (TCGA)-Ovarian Serous Cystadenocarcinoma (OV) database, GEO DataSets, and the HPA website. PrognoScan and Univariate Cox were used for prognostic analysis. TIDE database, TIMER database, and GSCA database were used to analyze the correlation between immune functions and ITGBs. Consensus clustering analysis was performed to subtype OC patients in the TCGA database. LASSO regression was used to construct the predictive model. The Cytoscape software was used for identifying hub genes. The 'pRRophetic' R package was applied to predict chemo-therapeutic responses of ITGBs.

RESULTS

ITGBs were upregulated in OC tissues except ITGB1 and ITGB3. High expression of ITGBs correlated with an unfavorable prognosis of OC except ITGB2. In OC, there was a strong correlation between immune responses and ITGB2, 6, and 7. In addition, the expression matrix of eight ITGBs divided the TCGA-OV database into two subgroups. Subgroup A showed upregulation of eight ITGBs. The predictive model distinguishes OC patients from favorable prognosis to poor prognosis. Chemo-therapeutic responses showed that ITGBs were able to predict responses of common chemo-therapeutic drugs for patients with OC.

CONCLUSIONS

This article provides evidence for predicting prognosis, immuno-, and chemo-therapeutic responses of ITGBs in OC and reveals related biological functions of ITGBs in OC.