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Objective:To explore the prognostic factors of epithelial ovarian carcinoma (EOC), construct a nomogram model, and evaluate the prognosis of EOC patients.Methods:A retrospective analysis was performed on clinicopathological data of 208 cases of EOC patients who received initial treatment in the First Affiliated Hospital of Army Medical University from August 11, 2016 to July 11, 2018, including age, preoperative ascites, preoperative neoadjuvant chemotherapy, surgical method, pathological type, pathological differentiation degree, surgical pathology stage, preoperative and post-chemotherapy serum cancer antigen 125 (CA 125) level, human epididymal protein 4 (HE4) level, platelet count and platelet/lymphocyte number ratio (PLR). The univariate and multivariate Cox risk ratio models were used to analyze the related factors affecting progression free survival (PFS) in EOC patients, and the prediction nomogram of PFS in EOC patients was established to evaluate its efficacy in predicting PFS. Results:Univariate analysis showed that preoperative neoadjuvant chemotherapy, pathological type, pathological differentiation degree, surgical pathology stage, serum CA 125 and HE4 level before operation and after chemotherapy, platelet count and PLR before operation and after chemotherapy were significantly correlated with PFS in EOC patients (all P<0.05). Multivariate analysis showed that surgical pathology stage, preoperative PLR, serum CA 125 and HE4 level after chemotherapy were independent prognostic factors affecting PFS of EOC patients (all P<0.01). The index coefficient of the prediction model for the prognosis of EOC patients established by this method was 0.749 (95% CI: 0.699-0.798), which had good prediction ability, and could help clinicians to more accurately evaluate the prognosis of EOC patients. Conclusion:The nomogram model constructed based on surgical pathology stage, preoperative PLR, serum CA 125 and HE4 level after chemotherapy could effectively predict the PFS of EOC patients after initial treatment, could help clinicians to screen high-risk patients, provide individualized treatment, and improve the prognosis of EOC patients.
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Mass spectrometry and database searching are necessary to identify proteins and peptides. With the rapid development of mass spectrometry technology, mass spectrometry data in proteomics are acquired very quickly, providing a powerful method to identify large-scale proteins and peptides, making mass spectrometry data-based proteomics research more and more into the mainstream. The traditional database searching method has many limitations to identify post-translational modifications of peptides. This paper systematically reviews the development, theoretical concept and applications of spectral network method, and the advantages of spectral network library to identify peptides.