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Prognostic significance of annexin A2 and annexin A4 expression in patients with cervical cancer.

Choi CH, Chung JY, Chung EJ, Sears JD, Lee JW, Bae DS, Hewitt SM - BMC Cancer (2016)

Bottom Line: ANXA2 overexpression was positively correlated with advanced cancer phenotypes, whereas ANXA4 expression was associated with resistance to radiation with or without chemotherapy (p = 0.029).Notably, high ANXA2 and ANXA4 expression was significantly associated with shorter disease-free survival (p = 0.004 and p = 0.033, respectively).Furthermore, a random survival forest model using combined ANXA2, ANXA4, and clinical variables resulted in improved predictive power (mean C-index, 0.76) compared to that of clinical-variable-only models (mean C-index, 0.70) (p = 0.006).

View Article: PubMed Central - PubMed

Affiliation: Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, MSC 1500, Bethesda, MD, 20892, USA.

ABSTRACT

Background: The annexins (ANXs) have diverse roles in tumor development and progression, however, their clinical significance in cervical cancer has not been elucidated. The present study was to investigate the clinical significance of annexin A2 (ANXA2) and annexin A4 (ANXA4) expression in cervical cancer.

Methods: ANXA2 and ANXA4 immunohistochemical staining were performed on a cervical cancer tissue microarray consisting of 46 normal cervical epithelium samples and 336 cervical cancer cases and compared the data with clinicopathological variables, including the survival of cervical cancer patients.

Results: ANXA2 expression was lower in cancer tissue (p = 0.002), whereas ANXA4 staining increased significantly in cancer tissues (p < 0.001). ANXA2 expression was more prominent in squamous cell carcinoma (p < 0.001), whereas ANXA4 was more highly expressed in adeno/adenosquamous carcinoma (p < 0.001). ANXA2 overexpression was positively correlated with advanced cancer phenotypes, whereas ANXA4 expression was associated with resistance to radiation with or without chemotherapy (p = 0.029). Notably, high ANXA2 and ANXA4 expression was significantly associated with shorter disease-free survival (p = 0.004 and p = 0.033, respectively). Multivariate analysis indicated that ANXA2+ (HR = 2.72, p = 0.003) and ANXA2+/ANXA4+ (HR = 2.69, p = 0.039) are independent prognostic factors of disease-free survival in cervical cancer. Furthermore, a random survival forest model using combined ANXA2, ANXA4, and clinical variables resulted in improved predictive power (mean C-index, 0.76) compared to that of clinical-variable-only models (mean C-index, 0.70) (p = 0.006).

Conclusions: These findings indicate that detecting ANXA2 and ANXA4 expression may aid the evaluation of cervical carcinoma prognosis.

No MeSH data available.


Related in: MedlinePlus

Comparison of survival predictive power of the clinical variables and the combined clinical and molecular data. We used the clinical variables (FIGO stage, lymph node metastasis, lymphovascular invasion, stromal depth of invasion, parametrial involvement, and resection margin) for the analysis. During 100 random splits, 80 % of all samples were used to train the model and the remaining 20 % were used as the test set for the C-index calculations. The white box highlights the model built from the clinical variables, and the grey-colored box highlights the models integrating the ANXA2 and ANXA4 data and clinical variables. The combined clinical and ANXA2/ANXA4 model predicting recurrence showed similar predictive power with the clinical variable model (a). However, the combined clinical/molecular-variable model showed better performance than that based only on clinical variables in the model predicting death (Mann-Whitney U-test, p = 0.006) (b). The dashed lines mark the C-index equivalent to a random guess (C-index = 0.5)
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Fig5: Comparison of survival predictive power of the clinical variables and the combined clinical and molecular data. We used the clinical variables (FIGO stage, lymph node metastasis, lymphovascular invasion, stromal depth of invasion, parametrial involvement, and resection margin) for the analysis. During 100 random splits, 80 % of all samples were used to train the model and the remaining 20 % were used as the test set for the C-index calculations. The white box highlights the model built from the clinical variables, and the grey-colored box highlights the models integrating the ANXA2 and ANXA4 data and clinical variables. The combined clinical and ANXA2/ANXA4 model predicting recurrence showed similar predictive power with the clinical variable model (a). However, the combined clinical/molecular-variable model showed better performance than that based only on clinical variables in the model predicting death (Mann-Whitney U-test, p = 0.006) (b). The dashed lines mark the C-index equivalent to a random guess (C-index = 0.5)

Mentions: To examine whether molecular data and ANXA2 and ANXA4 protein expression provided additional prognostic power when used with the clinical variables, we compared the predictive models using only the clinical variables and the combined clinical/molecular variables. The combined clinical/molecular-variable model predicting death resulted in significantly improved power (mean C-index, 0.76; range, 0.73–0.79) compared to the clinical-variable-only model (mean C-index, 0.70; range, 0.68–0.73) (p = 0.006) (Fig. 5). For models predicting recurrence, the combined clinical and ANXA2/ANXA4 model showed similar predictive power (mean C-index, 0.76, 95 % CI, 0.75–0.78) to clinical-variable-only model (mean C-index, 0.75, 95 % CI, 0.73–0.77).Fig. 5


Prognostic significance of annexin A2 and annexin A4 expression in patients with cervical cancer.

Choi CH, Chung JY, Chung EJ, Sears JD, Lee JW, Bae DS, Hewitt SM - BMC Cancer (2016)

Comparison of survival predictive power of the clinical variables and the combined clinical and molecular data. We used the clinical variables (FIGO stage, lymph node metastasis, lymphovascular invasion, stromal depth of invasion, parametrial involvement, and resection margin) for the analysis. During 100 random splits, 80 % of all samples were used to train the model and the remaining 20 % were used as the test set for the C-index calculations. The white box highlights the model built from the clinical variables, and the grey-colored box highlights the models integrating the ANXA2 and ANXA4 data and clinical variables. The combined clinical and ANXA2/ANXA4 model predicting recurrence showed similar predictive power with the clinical variable model (a). However, the combined clinical/molecular-variable model showed better performance than that based only on clinical variables in the model predicting death (Mann-Whitney U-test, p = 0.006) (b). The dashed lines mark the C-index equivalent to a random guess (C-index = 0.5)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4940752&req=5

Fig5: Comparison of survival predictive power of the clinical variables and the combined clinical and molecular data. We used the clinical variables (FIGO stage, lymph node metastasis, lymphovascular invasion, stromal depth of invasion, parametrial involvement, and resection margin) for the analysis. During 100 random splits, 80 % of all samples were used to train the model and the remaining 20 % were used as the test set for the C-index calculations. The white box highlights the model built from the clinical variables, and the grey-colored box highlights the models integrating the ANXA2 and ANXA4 data and clinical variables. The combined clinical and ANXA2/ANXA4 model predicting recurrence showed similar predictive power with the clinical variable model (a). However, the combined clinical/molecular-variable model showed better performance than that based only on clinical variables in the model predicting death (Mann-Whitney U-test, p = 0.006) (b). The dashed lines mark the C-index equivalent to a random guess (C-index = 0.5)
Mentions: To examine whether molecular data and ANXA2 and ANXA4 protein expression provided additional prognostic power when used with the clinical variables, we compared the predictive models using only the clinical variables and the combined clinical/molecular variables. The combined clinical/molecular-variable model predicting death resulted in significantly improved power (mean C-index, 0.76; range, 0.73–0.79) compared to the clinical-variable-only model (mean C-index, 0.70; range, 0.68–0.73) (p = 0.006) (Fig. 5). For models predicting recurrence, the combined clinical and ANXA2/ANXA4 model showed similar predictive power (mean C-index, 0.76, 95 % CI, 0.75–0.78) to clinical-variable-only model (mean C-index, 0.75, 95 % CI, 0.73–0.77).Fig. 5

Bottom Line: ANXA2 overexpression was positively correlated with advanced cancer phenotypes, whereas ANXA4 expression was associated with resistance to radiation with or without chemotherapy (p = 0.029).Notably, high ANXA2 and ANXA4 expression was significantly associated with shorter disease-free survival (p = 0.004 and p = 0.033, respectively).Furthermore, a random survival forest model using combined ANXA2, ANXA4, and clinical variables resulted in improved predictive power (mean C-index, 0.76) compared to that of clinical-variable-only models (mean C-index, 0.70) (p = 0.006).

View Article: PubMed Central - PubMed

Affiliation: Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, MSC 1500, Bethesda, MD, 20892, USA.

ABSTRACT

Background: The annexins (ANXs) have diverse roles in tumor development and progression, however, their clinical significance in cervical cancer has not been elucidated. The present study was to investigate the clinical significance of annexin A2 (ANXA2) and annexin A4 (ANXA4) expression in cervical cancer.

Methods: ANXA2 and ANXA4 immunohistochemical staining were performed on a cervical cancer tissue microarray consisting of 46 normal cervical epithelium samples and 336 cervical cancer cases and compared the data with clinicopathological variables, including the survival of cervical cancer patients.

Results: ANXA2 expression was lower in cancer tissue (p = 0.002), whereas ANXA4 staining increased significantly in cancer tissues (p < 0.001). ANXA2 expression was more prominent in squamous cell carcinoma (p < 0.001), whereas ANXA4 was more highly expressed in adeno/adenosquamous carcinoma (p < 0.001). ANXA2 overexpression was positively correlated with advanced cancer phenotypes, whereas ANXA4 expression was associated with resistance to radiation with or without chemotherapy (p = 0.029). Notably, high ANXA2 and ANXA4 expression was significantly associated with shorter disease-free survival (p = 0.004 and p = 0.033, respectively). Multivariate analysis indicated that ANXA2+ (HR = 2.72, p = 0.003) and ANXA2+/ANXA4+ (HR = 2.69, p = 0.039) are independent prognostic factors of disease-free survival in cervical cancer. Furthermore, a random survival forest model using combined ANXA2, ANXA4, and clinical variables resulted in improved predictive power (mean C-index, 0.76) compared to that of clinical-variable-only models (mean C-index, 0.70) (p = 0.006).

Conclusions: These findings indicate that detecting ANXA2 and ANXA4 expression may aid the evaluation of cervical carcinoma prognosis.

No MeSH data available.


Related in: MedlinePlus