Identification and validation of plasma biomarkers for diagnosis of breast cancer in South Asian women.
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ABSTRACT:
SUBMITTER: Rajkumar T
PROVIDER: S-EPMC8742108 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.
| S-SCDT-10_1038-S44321-024-00033-1 | biostudies-other
Project description:Metabolomic approaches have been used to identify new diagnostic biomarkers for various types of cancers, including breast cancer. In this study, we aimed to identify potential biomarkers of breast cancer using plasma metabolic profiling. Furthermore, we analyzed whether these biomarkers had relationships with clinicopathological characteristics of breast cancer. Our study used two liquid chromatography-mass spectrometry sets: a discovery set (40 breast cancer patients and 30 healthy controls) and a validation set (30 breast cancer patients and 16 healthy controls). All breast cancer patients were randomly selected from among stage I-III patients who underwent surgery between 2011 and 2016. First, metabolites distinguishing cancer patients from healthy controls were identified in the discovery set. Then, consistent and reproducible metabolites were evaluated in terms of their utility as possible biomarkers of breast cancer. Receiver operating characteristic (ROC) analysis was applied to the discovery set, and ROC cut-off values for the identified metabolites derived therein were applied to the validation set to determine their diagnostic performance. Ultimately, four candidate biomarkers (L-octanoylcarnitine, 5-oxoproline, hypoxanthine, and docosahexaenoic acid) were identified. L-octanoylcarnitine showed the best diagnostic performance, with a 100.0% positive predictive value. Also, L-octanoylcarnitine levels differed according to tumor size and hormone receptor expression. The plasma metabolites identified in this study show potential as biomarkers allowing early diagnosis of breast cancer. However, the diagnostic performance of the metabolites needs to be confirmed in further studies with larger sample sizes.
| S-EPMC6890236 | biostudies-literature
Project description:Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.
| S-EPMC11018850 | biostudies-literature
Project description:Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.
| S-EPMC8660094 | biostudies-literature
Project description:Background Stromal cells and immune cells in tumor microenvironment (TME) have been reported to have significant value in the diagnosis and prognosis of cancers. We aimed to identify key biomarkers predicting survival in the TME of breast cancer. Methods Cell type enrichment analysis was performed to estimate cell scores using the xCell method with gene expression data from public database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify key signature from the cell scores. Results Totally, 50 cells in TME had different scores between 1,078 breast cancer tissues and 112 adjacent normal tissues. We identified a 4-cell signature predicting breast cancer survival, including myocytes, natural killer T cell (NKT), conventional dendritic cell (cDC) and sebocytes, which was validated in the test set. Further analysis showed that cDC score was a key signature predicting prognosis of breast cancer. cDC score was significantly associated with molecular classification and stage of breast cancer, as well as expression level of Ki67. Spearman’s correlation analysis found that cDC score was inversely correlated with the expression level of HER2. High cDC score may predicate better pathological complete response rate. Mechanism analysis indicated high cDC score was associated with elevated immune activity; IL-2 was a key gene associated with high cDC score; and Breast cancer patients with high IL-2 expression had a longer survival time. Conclusions In conclusion, cDC score was a key signature predicting prognosis for breast cancer. cDCs may exert antitumor effects by upregulating IL-2.
| S-EPMC8798301 | biostudies-literature
Project description:IntroductionCirculating non-coding RNA is an ideal source to discover novel biomarkers for non-invasive screening. However, studies for the discovery of universal miRNAs in serum and exosomes for breast cancer early diagnosis are limited.MethodsBased on bioinformatic analysis, in vitro and in vivo studies were performed to understand the role of identified hsa-miR-423-5p in cancer proliferation, migration, cancer stem cell properties. Next, global non-coding RNA expression profiles in blood serum and exosome were performed. hsa-miR-423-5p expression from a total of 356 peripheral blood samples was evaluated and the association of hsa-miR-423-5p expression with clinical characteristics, sensitivity and specificity for breast cancer diagnosis were assessed.ResultsThe expression of serum and exosomal hsa-miR-423-5p is abnormally increased in breast cancer. Suppression of hsa-miR-423-5p inhibited cell proliferation and invasion in both T47D and MDA-MB-231 breast cancer cell lines, and tumor growth in vivo. Compared with 113 healthy women, quantification analysis of hsa-miR-423-5p in 224 breast cancer samples confirmed the abnormal expression. Serum hsa-miR-423-5p was significantly associated with the clinical stage (P=0.001) and Ki-67 level (P=0.004).ConclusionsA translational bioinformatics analysis procedure and validation by in vitro, in vivo, and clinical samples reveal that hsa-miR-423-5p could be used as a non-invasive breast cancer biomarker.
| S-EPMC7906147 | biostudies-literature
Project description:OBJECTIVE: To examine tumour stage at diagnosis, treatment, patient and provider delays to diagnosis/treatment and survival of South Asian patients with breast cancer in Yorkshire in comparison with the general population. DESIGN: Retrospective study, using Yorkshire Cancer Registry population-based data on breast cancer. Data on 16,879 women with breast cancer diagnosed between 1986 and 1994 was available, of which 120 patients were South Asian. All-cause survival, controlling for age, socio-economic profile, tumour stage and treatment was examined. Effects of ethnicity on tumour stage at diagnosis, treatment, patient and provider delays to diagnosis and treatment were described. Over the period 1986-1994, an increase in the number of registered South Asian patients with breast cancer was observed. South Asian patients were significantly younger at the time of diagnosis and presented with larger primary tumours. They received similar treatment to non-Asian patients, but a higher mastectomy rate was noted. South Asian patients' survival, after controlling for age differences was similar to non-South Asian patients. South Asian patients had a significantly longer patient-related delay between initial symptoms and presentation to GP and a slightly longer provider-related delay in time to diagnosis and treatment. In conclusion, outcomes of breast cancer treatment in South Asian patients were similar to non-Asian patients. Asian patients presented later to their GPs, with larger primary tumours and more frequently had mastectomy.
| S-EPMC2410283 | biostudies-other
Project description:BACKGROUND: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND METHODS: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). RESULTS: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). CONCLUSION: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.
| S-EPMC3973579 | biostudies-literature
Project description:BackgroundThe prognostic or predictive value of commonly used multigene assays in young patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer is unclear. In this study, we assessed the prognostic value of the GenesWell BCT assay according to age group.MethodsWe identified patients with pN0-1, HR+/HER2- breast cancer in a prospective cohort of women who underwent surgery between 2005 and 2017. The GenesWell BCT assay was performed on tissue samples from selected patients. Distant metastasis-free survival (DMFS) and disease-free survival (DFS) were compared between the risk groups assigned by the BCT score.ResultsA total of 712 patients were eligible for analysis. The median follow-up time was 7.47 years. The BCT score was prognostic in patients aged ≤50 years (n = 404) and those aged >50 years (n = 308). In both age groups, the 10-year DMFS and DFS rates for patients classified as high risk by the BCT score were significantly lower than those for patients classified as low risk. A multivariate analysis revealed that the BCT score was an independent prognostic factor for DFS in patients aged ≤50 years (hazard ratio, 1.28; 95% CI, 1.05-1.56; P = 0.015), as well as those aged >50 years.ConclusionThe BCT score could be used to identify low-risk patients who will not benefit from adjuvant chemotherapy to treat HR+/HER2- early breast cancer regardless of age. A further prospective study to assess the prognostic and predictive value of the BCT score is required.
| S-EPMC7942224 | biostudies-literature
Project description:The majority of breast cancer patients receive endocrine therapy, including aromatase inhibitors known to cause increased bone resorption. Bone-related biomarkers at the time of breast cancer diagnosis may predict future risk of osteoporosis and fracture after endocrine therapy.In a large population of 2,401 female breast cancer patients who later underwent endocrine therapy, we measured two bone remodeling biomarkers, TRAP5b and BAP, and two bone regulating biomarkers, RANKL and OPG, in serum samples collected at the time of breast cancer diagnosis. We analyzed these biomarkers and their ratios with patients' demographic, lifestyle, clinical tumor characteristics, as well as bone health history.The presence of bone metastases, prior bisphosphonate (BP) treatment, and blood collection after chemotherapy had a significant impact on biomarker levels. After excluding these cases and controlling for blood collection time, several factors, including age, race/ethnicity, body mass index, physical activity, alcohol consumption, smoking, and hormonal replacement therapy, were significantly associated with bone biomarkers, while vitamin D or calcium supplements and tumor characteristics were not. When prior BP users were included in, recent history of osteoporosis and fracture was also associated.Our findings support further investigation of these biomarkers with bone health outcomes after endocrine therapy initiation in women with breast cancer.
| S-EPMC5243170 | biostudies-literature