Advanced search
Publication Cover
OncoImmunology Volume 9, 2020 - Issue 1
Submit an article Journal homepage
2,271
Views
19
CrossRef citations to date
0
Altmetric
Back Matter

Establishment and validation of an immunodiagnostic model for prediction of breast cancer

Cuipeng Qiua Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China;b College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, ChinaORCID Iconhttps://orcid.org/0000-0002-3956-0430View further author information
ORCID Icon,
Peng Wanga Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China;b College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, ChinaView further author information
,
Bofei Wangb College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, ChinaView further author information
,
Jianxiang Shib College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, China;c Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, ChinaView further author information
,
Xiao Wangb College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, China;c Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, ChinaView further author information
,
Tiandong Lia Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China;b College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, ChinaORCID Iconhttps://orcid.org/0000-0002-5465-9797View further author information
ORCID Icon,
Jiejie Qina Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China;b College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, ChinaView further author information
,
Liping Daib College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, China;c Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, ChinaView further author information
,
Hua Yea Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China;b College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-3657-2417View further author information
ORCID Icon &
Jianying Zhanga Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China;b College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, China;c Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, ChinaCorrespondence[email protected]
View further author information
 show all
Article: 1682382 | Received 06 Aug 2019, Accepted 14 Oct 2019, Published online: 28 Oct 2019

Figures & data

Table 1. Characteristics of study participants

Figure 1. Study design. BC, breast cancer; NHC, normal human controls; BBD, benign breast disease; ELISA, enzyme-linked immunosorbent assay

Figure 1. Study design. BC, breast cancer; NHC, normal human controls; BBD, benign breast disease; ELISA, enzyme-linked immunosorbent assay

Figure 2. Profiles of autoantibodies against 11 tumor-associated antigens tested by western blotting in 10 patients with breast cancer (B1-B10) and 10 normal controls (C1-C10)

Figure 2. Profiles of autoantibodies against 11 tumor-associated antigens tested by western blotting in 10 patients with breast cancer (B1-B10) and 10 normal controls (C1-C10)

Table 2. Serum relative concentration levels of individual autoantibody among breast cancer patients, breast benign disease and normal individual

Figure 3. Scatter plots of sera relative concentration levels of individual autoantibody among breast cancer patients (NTC = 184, NVC = 197), benign breast disease (NTC = 200, NVC = 109) and normal individual (NTC = 184, NVC = 109) in two cohorts (TC, training cohort; VC, validation cohort). BC, breast cancer; NHC, normal human controls; BBD, benign breast disease

*: P < .05, **: P < .01, ***: P < .001 (Kruskal–Wallis H test, Mann–Whitney U test).
Figure 3. Scatter plots of sera relative concentration levels of individual autoantibody among breast cancer patients (NTC = 184, NVC = 197), benign breast disease (NTC = 200, NVC = 109) and normal individual (NTC = 184, NVC = 109) in two cohorts (TC, training cohort; VC, validation cohort). BC, breast cancer; NHC, normal human controls; BBD, benign breast disease

Table 3. LOOCV results predicted by the two models of BC

Figure 4. Performance of the prediction model with five anti-TAAs autoantibodies to detect breast cancer. Receiver operating characteristic (ROC) curves from training cohort (a, b) and validation cohort (c, d). Performance shown for distinguishing individuals with BC from NHC (a, c) or BBD controls (b, d). TC, training cohort; VC, validation cohort; BC, breast cancer; NHC, normal human controls; BBD, benign breast disease; Se, sensitivity; Sp, specificity

Figure 4. Performance of the prediction model with five anti-TAAs autoantibodies to detect breast cancer. Receiver operating characteristic (ROC) curves from training cohort (a, b) and validation cohort (c, d). Performance shown for distinguishing individuals with BC from NHC (a, c) or BBD controls (b, d). TC, training cohort; VC, validation cohort; BC, breast cancer; NHC, normal human controls; BBD, benign breast disease; Se, sensitivity; Sp, specificity

Table 4. Diagnostic value of the prediction model with five anti-TAAs autoantibodies for different stages and ages of BC patients

Figure 5. Positive rates of autoantibodies against eight TAAs or combined five TAAs in individual sera of BC patients, BBD patients and NHC (a, b). The cutoff value was set at the maximum Yoden index when the specificity was greater than 95%. Positive rates of the prediction model with five anti-TAAs autoantibodies among subgroups of BC patients based on tumor stage (c-f), molecular subtypes (g), lymph node metastasis (h), histological type (i) and family history of tumor (j). TC, training cohort; VC, validation cohort; BC, breast cancer; NHC, normal human controls; BBD, benign breast disease

*: P < .05, **: P < .01, ***: P < .001 (Chi-square tests).
Figure 5. Positive rates of autoantibodies against eight TAAs or combined five TAAs in individual sera of BC patients, BBD patients and NHC (a, b). The cutoff value was set at the maximum Yoden index when the specificity was greater than 95%. Positive rates of the prediction model with five anti-TAAs autoantibodies among subgroups of BC patients based on tumor stage (c-f), molecular subtypes (g), lymph node metastasis (h), histological type (i) and family history of tumor (j). TC, training cohort; VC, validation cohort; BC, breast cancer; NHC, normal human controls; BBD, benign breast disease

Table 5. Positive reactivity of the prediction model with five anti-TAAs autoantibodies among subgroups

Supplemental material

Supplemental Material

Download ()

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.