Dataset Information

Distant metastasis prediction via a multi-feature fusion model in breast cancer.

ABSTRACT: This study aimed to develop a model that fused multiple features (multi-feature fusion model) for predicting metachronous distant metastasis (DM) in breast cancer (BC) based on clinicopathological characteristics and magnetic resonance imaging (MRI). A nomogram based on clinicopathological features (clinicopathological-feature model) and a nomogram based on the multi-feature fusion model were constructed based on BC patients with DM (n=67) and matched patients (n=134) without DM. DM was diagnosed on average (17.31±13.12) months after diagnosis. The clinicopathological-feature model included seven features: reproductive history, lymph node metastasis, estrogen receptor status, progesterone receptor status, CA153, CEA, and endocrine therapy. The multi-feature fusion model included the same features and an additional three MRI features (multiple masses, fat-saturated T2WI signal, and mass size). The multi-feature fusion model was relatively better at predicting DM. The sensitivity, specificity, diagnostic accuracy and AUC of the multi-feature fusion model were 0.746 (95% CI: 0.623-0.841), 0.806 (0.727-0.867), 0.786 (0.723-0.841), and 0.854 (0.798-0.911), respectively. Both internal and external validations suggested good generalizability of the multi-feature fusion model to the clinic. The incorporation of MRI factors significantly improved the specificity and sensitivity of the nomogram. The constructed multi-feature fusion nomogram may guide DM screening and the implementation of prophylactic treatment for BC.

SUBMITTER: Ma W

PROVIDER: S-EPMC7585122 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Distant metastasis prediction via a multi-feature fusion model in breast cancer.

Ma Wenjuan W Wang Xin X Xu Guijun G Liu Zheng Z Yin Zhuming Z Xu Yao Y Wu Haixiao H Baklaushev Vladimir P VP Peltzer Karl K Sun Henian H Kharchenko Natalia V NV Qi Lisha L Mao Min M Li Yanbo Y Liu Peifang P Chekhonin Vladimir P VP Zhang Chao C

Aging 20200928 18

This study aimed to develop a model that fused multiple features (multi-feature fusion model) for predicting metachronous distant metastasis (DM) in breast cancer (BC) based on clinicopathological characteristics and magnetic resonance imaging (MRI). A nomogram based on clinicopathological features (clinicopathological-feature model) and a nomogram based on the multi-feature fusion model were constructed based on BC patients with DM (n=67) and matched patients (n=134) without DM. DM was diagnose ...[more]

PMID: 32989175

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Project description:BackgroundDistant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC.MethodsA total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.ResultsMultivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001.ConclusionsWe developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.

Project description:BackgroundTandem C2 domains, nuclear (TC2N) is a C2 domain-containing protein that belongs to the carboxyl-terminal type (C-type) tandem C2 protein family, and acts as an oncogenic driver in several cancers. Previously, we preliminarily reported that TC2N mediates the PI3K-Akt signaling pathway to inhibit tumor growth of breast cancer (BC) cells. Beyond that, its precise biological functions and detailed molecular mechanisms in BC development and progression are not fully understood.MethodsTumor tissues of 212 BC patients were subjected to tissue microarray and further assessed the associations of TC2N expression with pathological parameters and FASN expression. The protein levels of TC2N and FASN in cell lines and tumor specimens were monitored by qRT-PCR, WB, immunofluorescence and immunohistochemistry. In vitro cell assays, in vivo nude mice model was used to assess the effect of TC2N ectopic expression on tumor metastasis and stemness of breast cancer cells. The downstream signaling pathway or target molecule of TC2N was mined using a combination of transcriptomics, proteomics and lipidomics, and the underlying mechanism was explored by WB and co-IP assays.ResultsHere, we found that the expression of TC2N remarkedly silenced in metastatic and poorly differentiated tumors. Function-wide, TC2N strongly inhibits tumor metastasis and stem-like properties of BC via inhibition of fatty acid synthesis. Mechanism-wise, TC2N blocks neddylated PTEN-mediated FASN stabilization by a dual mechanism. The C2B domain is crucial for nuclear localization of TC2N, further consolidating the TRIM21-mediated ubiquitylation and degradation of FASN by competing with neddylated PTEN for binding to FASN in nucleus. On the other hand, cytoplasmic TC2N interacts with import proteins, thereby restraining nuclear import of PTEN to decrease neddylated PTEN level.ConclusionsAltogether, we demonstrate a previously unidentified role and mechanism of TC2N in regulation of lipid metabolism and PTEN neddylation, providing a potential therapeutic target for anti-cancer.

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Distant metastasis prediction via a multi-feature fusion model in breast cancer.

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Distant metastasis prediction via a multi-feature fusion model in breast cancer.

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