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Wei C, Ai H, Mo D, Wang P, Wei L, Liu Z, Li P, Huang T, Liu M. A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients. Front Endocrinol (Lausanne) 2024; 15:1388861. [PMID: 39170737 PMCID: PMC11335604 DOI: 10.3389/fendo.2024.1388861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 07/24/2024] [Indexed: 08/23/2024] Open
Abstract
Background We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients. Methods The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram. Results A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system. Conclusion The nomogram incorporating inflammation, nutritional and clinicopathological variables exhibited excellent discrimination. This nomogram is a promising instrument for predicting outcomes and defining personalized treatment strategies for patients with BC.
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Affiliation(s)
- Caibiao Wei
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Huaying Ai
- Department of Injection Room, The People’s Hospital of Yingtan, Yingtan, Jiangxi, China
| | - Dan Mo
- Department of Breast, Guangxi Zhuang Autonomous Region Maternal and Child Health Care Hospital, Nanning, China
| | - Peidong Wang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Liling Wei
- Department of Anesthesiology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Zhimin Liu
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Peizhang Li
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Taijun Huang
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Miaofeng Liu
- Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China
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Zhang Z, Jiang Q, Wang J, Yang X. A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status. Oncol Lett 2024; 28:345. [PMID: 38872855 PMCID: PMC11170244 DOI: 10.3892/ol.2024.14478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/14/2024] [Indexed: 06/15/2024] Open
Abstract
Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.
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Affiliation(s)
- Ziran Zhang
- Department of Breast Diseases, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children's Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Qin Jiang
- Department of Breast Diseases, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children's Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Jie Wang
- Department of Breast Diseases, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children's Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
| | - Xinxia Yang
- Department of Breast Diseases, Jiaxing Maternity and Child Health Care Hospital, Affiliated Women and Children's Hospital of Jiaxing University, Jiaxing, Zhejiang 314000, P.R. China
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Wang XE, Bi Z, Zhang J, Wang YS. Nomograms for metastasis of non-sentinel lymph nodes or more than three lymph nodes in patients with one or two positive sentinel lymph nodes. Front Oncol 2024; 14:1413936. [PMID: 38835388 PMCID: PMC11148251 DOI: 10.3389/fonc.2024.1413936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 04/30/2024] [Indexed: 06/06/2024] Open
Abstract
Purpose The purpose of this study was to provide advice for the indication of regional nodal irradiation (RNI) in patients with one to two positive sentinel lymph nodes (SLNs) without axillary lymph node dissection (ALND). Methods We conducted a retrospective study in Shandong Cancer Hospital, Fudan University Shanghai Cancer Center, and West China Hospital. Logistic analysis was performed in order to explore the influencing factors of positive non-SLNs (NSLNs) and >3 positive nodes among patients with one to two SLNs+. Then, nomograms were constructed. Results Between May 2010 and 2020, among the 2,845 patients with one to two SLNs+ undergoing ALND (1,992 patients in the training set and 853 patients in the validation set), there were 34.3% harbored NSLNs+ and 15.6% harbored >3 positive nodes. Multivariate analysis showed that cN stage, the number of positive/negative SLN, pathological tumor stage, lympho-vascular invasion (LVI), multicenter, and molecular subtypes were significantly associated with NSLN metastasis. Similarly, multivariate analysis also showed that cN stage, the number of positive/negative SLNs, pathological tumor stage, and LVI could be independent predictors of >3 positive nodes. Then, nomograms for NSLN metastasis and >3 positive nodes were constructed using these parameters, respectively. Conclusions The nomograms will be useful in estimating positive NSLNs and >3 positive nodes, and they might provide advice for the optimization of RNI.
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Affiliation(s)
- Xue-Er Wang
- The Third Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhao Bi
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jin Zhang
- The Third Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yong-Sheng Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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James J, Law M, Sengupta S, Saunders C. Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review. World J Surg Oncol 2024; 22:127. [PMID: 38725006 PMCID: PMC11084006 DOI: 10.1186/s12957-024-03394-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/28/2024] [Indexed: 05/12/2024] Open
Abstract
Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.
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Affiliation(s)
- Justin James
- Eastern Health, Melbourne, Australia.
- Monash University, Melbourne, Australia.
- Department of Breast and Endocrine Surgery, Maroondah Hospital, Davey Drive, Ringwood East, Melbourne, VIC, 3135, Australia.
| | - Michael Law
- Eastern Health, Melbourne, Australia
- Monash University, Melbourne, Australia
| | - Shomik Sengupta
- Eastern Health, Melbourne, Australia
- Monash University, Melbourne, Australia
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Drapalik LM, Miller ME, Rock L, Li P, Simpson A, Shenk R, Amin AL. Using MammaPrint on core needle biopsy to guide the need for axillary staging during breast surgery. Surgery 2024; 175:579-586. [PMID: 37852835 DOI: 10.1016/j.surg.2023.08.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/05/2023] [Accepted: 08/16/2023] [Indexed: 10/20/2023]
Abstract
BACKGROUND At present, the only opportunity to omit axillary staging is with Choosing Wisely criteria for women ages >70 y with cT1 2N0 estrogen receptor-positive/human epidermal growth factor receptor 2-negative breast cancer. However, many women are diagnosed when pathologic node status-negative, raising the question of additional opportunities to omit sentinel lymph node biopsy. We sought to investigate the association between MammaPrint, a genomic test that estimates estrogen receptor-positive breast cancer recurrence risk, and pathologic node status, with the aim that low-risk MammaPrint could be considered for omission of sentinel lymph node biopsy if associated with pathologic node status-negative. METHODS A single-institution database was queried for all women with cT1 2N0 estrogen receptor-positive/human epidermal growth factor receptor 2-negative invasive breast cancer with breast surgery as their first treatment and MammaPrint performed from 2020 to 2021. Patient and tumor factors, including MammaPrint score, were compared with axillary node status for correlation. RESULTS A total of 668 women met inclusion criteria, with a median age of 66 y. MammaPrint was low-risk luminal A in 481 (72%) and high-risk luminal B in 187 (28%). At the time of breast surgery, 588 (88%) had sentinel lymph node biopsy, 27 (4%) had axillary lymph node dissection, and 53 (7.9%) had no axillary staging. Most women in both the pathologic node status-negative and pathologic node status-positive cohorts had low-risk MammaPrint (355 [73.3%] pathologic node status-negative vs 91 [69.5%] pathologic node status-positive), and women with low-risk MammaPrint did not have a significantly lower risk of pathologic node status-positive (P = .377). CONCLUSION Low-risk MammaPrint does not predict lower risk of pathologic node status-positive breast cancer. Based on our results, genomic testing does not appear to provide additional personalization for the ability to omit sentinel lymph node biopsy for patients outside of the Choosing Wisely guidelines.
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Affiliation(s)
- Lauren M Drapalik
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH; University Hospitals Research in Surgical Outcomes and Effectiveness (UH-RISES), University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Megan E Miller
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH; University Hospitals Research in Surgical Outcomes and Effectiveness (UH-RISES), University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Lisa Rock
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH
| | - Pamela Li
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH
| | - Ashley Simpson
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH
| | - Robert Shenk
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH; University Hospitals Research in Surgical Outcomes and Effectiveness (UH-RISES), University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Amanda L Amin
- Department of Surgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH; University Hospitals Research in Surgical Outcomes and Effectiveness (UH-RISES), University Hospitals Cleveland Medical Center, Cleveland, OH.
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Wu L, Huang S, Tian W, Liu P, Xie Y, Qiu Y, Li X, Tang Y, Zheng S, Sun Y, Tang H, Du W, Tan W, Xie X. PIWI-interacting RNA-YBX1 inhibits proliferation and metastasis by the MAPK signaling pathway via YBX1 in triple-negative breast cancer. Cell Death Discov 2024; 10:7. [PMID: 38182573 PMCID: PMC10770055 DOI: 10.1038/s41420-023-01771-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 12/04/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024] Open
Abstract
Breast cancer is the second leading cause of death in women worldwide, with triple-negative breast cancer (TNBC) having the worst prognosis. Although there are numerous studies on TNBC, there is no effective treatment for it, and it is still a major problem today. Studies on PIWI-interacting RNAs (piRNAs) are increasing and investigating the mechanism of piRNAs in the proliferation and metastasis of TNBC may lead to new potential treatment targets. Here, we identified a novel piRNA, piR-YBX1, which was downregulated in TNBC compared to matched normal breast tissue. Overexpression of piR-YBX1 significantly inhibited the proliferation, migration, invasion ability of TNBC cells both in vivo and in vitro. Mechanistically, piR-YBX1 could bind directly to mRNA of Y-box binding protein 1 (YBX1) and overexpression of piR-YBX1 downregulated YBX1 in both mRNA and protein levels, while the function of piR-YBX1 could be partly rescued by overexpression of YBX1. In addition, YBX1 could bind to RAF1 which is the key molecule in the MAPK signaling pathway, and overexpression of piR-YBX1 inhibited the p-MEK and p-ERK1/2, which can be reverted by YBX1. In conclusion, our findings discovered that the piR-YBX1/YBX1/MAPK axis suppresses the proliferation and metastasis of TNBC and therefore piR-YBX1 has the potential to be an effective therapeutic agent for breast cancer.
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Affiliation(s)
- Linyu Wu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Shanshan Huang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Wenwen Tian
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, No.78 Hengzhigang Road, Guangzhou, 510095, China
| | - Peng Liu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Yi Xie
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Yu Qiu
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Xing Li
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Yuhui Tang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Shaoquan Zheng
- Department of Breast Surgery, Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuying Sun
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Hailin Tang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China
| | - Wei Du
- Department of Pathology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, 415003, China.
| | - Weige Tan
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
| | - Xinhua Xie
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
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Ahmadi SM, Amirkhanloo S, Yazdian-Robati R, Ebrahimi H, Pirhayati FH, Almalki WH, Ebrahimnejad P, Kesharwani P. Recent advances in novel miRNA mediated approaches for targeting breast cancer. J Drug Target 2023; 31:777-793. [PMID: 37480323 DOI: 10.1080/1061186x.2023.2240979] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/18/2023] [Accepted: 05/05/2023] [Indexed: 07/24/2023]
Abstract
Breast cancer (BC) is considered one of the most frequent cancers among woman worldwide. While conventional therapy has been successful in treating many cases of breast cancer, drug resistance, heterogenicity, tumour features and recurrence, invasion, metastasis and the presence of breast cancer stem cells can hinder the effect of treatments, and can reduce the quality of life of patients. MicroRNAs (miRNAs) are short non-coding RNA molecules that play a crucial role in the development and progression of breast cancer. Several studies have reported that aberrant expression of specific miRNAs is associated with the pathogenesis of breast cancer. However, miRNAs are emerging as potential biomarkers and therapeutic targets for breast cancer. Understanding their role in breast cancer biology could help develop more effective treatments for this disease. The present study discusses the biogenesis and function of miRNAs, as well as miRNA therapy approaches for targeting and treating breast cancer cells.
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Affiliation(s)
- Seyedeh Melika Ahmadi
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
| | - Shervin Amirkhanloo
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
| | - Rezvan Yazdian-Robati
- Pharmaceutical Sciences Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - Hossein Ebrahimi
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Waleed H Almalki
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Pedram Ebrahimnejad
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
- Pharmaceutical Sciences Research Center, Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran
| | - Prashant Kesharwani
- Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
- Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
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Wang Z, Zhang H, Lin F, Zhang R, Ma H, Shi Y, Yang P, Zhang K, Zhao F, Mao N, Xie H. Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study. Acad Radiol 2023; 30 Suppl 2:S133-S142. [PMID: 37088646 DOI: 10.1016/j.acra.2023.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 04/25/2023]
Abstract
RATIONALE AND OBJECTIVES This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis. MATERIALS AND METHODS A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly. RESULTS In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists. CONCLUSION The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.
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Affiliation(s)
- Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000; Institute of medical imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Ran Zhang
- Artificial Intelligence and Clinical Innovation Institute, Huiying Medical Technology Co., Ltd, P. R. China, 100192
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000.
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Wang X, Zhang G, Zuo Z, Zhu Q, Liu Z, Wu S, Li J, Du J, Yan C, Ma X, Shi Y, Shi H, Zhou Y, Mao F, Lin Y, Shen S, Zhang X, Sun Q. A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer. Cancer Med 2023; 12:7039-7050. [PMID: 36524283 PMCID: PMC10067027 DOI: 10.1002/cam4.5503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 10/29/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND OR PURPOSE A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status. METHODS Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models. RESULTS In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability. CONCLUSION This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.
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Affiliation(s)
- Xue‐fei Wang
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Guo‐chao Zhang
- Department of Thoracic SurgeryNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhi‐chao Zuo
- Radiology Department, Xiangtan Central HospitalHunanChina
| | - Qing‐li Zhu
- Ultrasound Medicine DepartmentChinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College HospitalBeijingChina
| | - Zhen‐zhen Liu
- Ultrasound Medicine DepartmentChinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College HospitalBeijingChina
| | - Sha‐fei Wu
- Molecular Pathology Research Center, Department of PathologyPeking Union Medical College Hospital, Chinese Academy of Medical SciencesBeijingChina
| | - Jia‐xin Li
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Jian‐hua Du
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Cun‐li Yan
- Breast Surgery DepartmentBaoji Maternal and Child Health HospitalShaanxiChina
| | - Xiao‐ying Ma
- Breast Surgery DepartmentQinghai Provincial People's HospitalQinghaiChina
| | - Yue Shi
- Breast Surgery DepartmentShanxi Traditional Chinese Medical HospitalShanxiChina
| | - He Shi
- Breast Surgery DepartmentShanxi Traditional Chinese Medical HospitalShanxiChina
| | - Yi‐dong Zhou
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Feng Mao
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Yan Lin
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Song‐jie Shen
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Xiao‐hui Zhang
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
| | - Qiang Sun
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical CollegePeking Union Medical College and HospitalBeijingChina
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Matulić M, Gršković P, Petrović A, Begić V, Harabajsa S, Korać P. miRNA in Molecular Diagnostics. Bioengineering (Basel) 2022; 9:bioengineering9090459. [PMID: 36135005 PMCID: PMC9495386 DOI: 10.3390/bioengineering9090459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/05/2022] [Accepted: 09/07/2022] [Indexed: 11/17/2022] Open
Abstract
MicroRNAs are a class of small non-coding RNA molecules that regulate gene expression on post-transcriptional level. Their biogenesis consists of a complex series of sequential processes, and they regulate expression of many genes involved in all cellular processes. Their function is essential for maintaining the homeostasis of a single cell; therefore, their aberrant expression contributes to development and progression of many diseases, especially malignant tumors and viral infections. Moreover, they can be associated with certain states of a specific disease, obtained in the least invasive manner for patients and analyzed with basic molecular methods used in clinical laboratories. Because of this, they have a promising potential to become very useful biomarkers and potential tools in personalized medicine approaches. In this review, miRNAs biogenesis, significance in cancer and infectious diseases, and current available test and methods for their detection are summarized.
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Affiliation(s)
- Maja Matulić
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
| | - Paula Gršković
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
| | - Andreja Petrović
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Institute of Clinical Pathology and Cytology, Merkur University Hospital, 10000 Zagreb, Croatia
| | - Valerija Begić
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Primary School “Sesvetski Kraljevec”, 10361 Sesvetski Kraljevec, Croatia
| | - Suzana Harabajsa
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Department of Pathology and Cytology, Division of Pulmonary Cytology Jordanovac, University Hospital Centre Zagreb, 10000 Zagreb, Croatia
| | - Petra Korać
- Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia
- Correspondence: ; Tel.: +385-1-4606-278
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11
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Luo N, Wen Y, Zou Q, Ouyang D, Chen Q, Zeng L, He H, Anwar M, Qu L, Ji J, Yi W. Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1-2 breast cancer. Sci Rep 2022; 12:687. [PMID: 35027588 PMCID: PMC8758717 DOI: 10.1038/s41598-021-04495-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/21/2021] [Indexed: 12/22/2022] Open
Abstract
The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1-2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR-/HER2-) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1-2 BC patients, particularly given that it can be used to adjust surgical options in the future.
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Affiliation(s)
- Na Luo
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
- Department of General Surgery, The First People's Hospital of Changde City, Changde, China
| | - Ying Wen
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Qiongyan Zou
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Dengjie Ouyang
- Department of General Surgery, Xiangya Hospital Central South University, Changsha, China
| | - Qitong Chen
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Liyun Zeng
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hongye He
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Munawar Anwar
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Limeng Qu
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jingfen Ji
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China.
| | - Wenjun Yi
- Department of General Surgery, The Second Xiangya Hospital, Central South University, Changsha, China.
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12
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A Novel Predictive Nomogram including Serum Lipoprotein a Level for Nonsentinel Lymph Node Metastases in Chinese Breast Cancer Patients with Positive Sentinel Lymph Node Metastases. DISEASE MARKERS 2021; 2021:7879508. [PMID: 34853623 PMCID: PMC8629655 DOI: 10.1155/2021/7879508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/26/2021] [Indexed: 11/18/2022]
Abstract
Background We developed a new nomogram combining serum biomarkers with clinicopathological features to improve the accuracy of prediction of nonsentinel lymph node (SLN) metastases in Chinese breast cancer patients. Methods We enrolled 209 patients with breast cancer who underwent SLN biopsy and axillary lymph node dissection. We evaluated the relationships between non-SLN metastases and clinicopathologic features, as well as preoperative routine tests of blood indexes, tumor markers, and serum lipids, including lipoprotein a (Lp(a)). Risk factors for non-SLN metastases were identified by logistic regression analysis. The nomogram was created using the R program to predict the risk of non-SLN metastases in the training set. Receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in the validation set. Results Lp(a) was significantly associated with non-SLN metastasis status. Compared with the MSKCC model, the predictive ability of our new nomogram that combined Lp(a) level and clinical variables (pathologic tumor size, lymphovascular invasion, multifocality, and positive/negative SLN numbers) was significantly greater (AUC: 0.732, 95% CI: 0.643–0.821) (C-index: 0.703, 95% CI: 0.656–0.791) in the training cohorts and also performed well in the validation cohorts (C-index: 0.773, 95% CI: 0.681–0.865). Moreover, the new nomogram with Lp(a) improved the accuracy (12.10%) of identification of patients with non-SLN metastases (NRI: 0.121; 95% CI: 0.081–0.202; P = 0.011). Conclusions This novel nomogram based on preoperative serum indexes combined with clinicopathologic features facilitates accurate prediction of risk of non-SLN metastases in Chinese patients with breast cancer.
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Sempere LF, Azmi AS, Moore A. microRNA-based diagnostic and therapeutic applications in cancer medicine. WILEY INTERDISCIPLINARY REVIEWS. RNA 2021; 12:e1662. [PMID: 33998154 PMCID: PMC8519065 DOI: 10.1002/wrna.1662] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/10/2021] [Accepted: 04/12/2021] [Indexed: 01/18/2023]
Abstract
It has been almost two decades since the first link between microRNAs and cancer was established. In the ensuing years, this abundant class of short noncoding regulatory RNAs has been studied in virtually all cancer types. This tremendously large body of research has generated innovative technological advances for detection of microRNAs in tissue and bodily fluids, identified the diagnostic, prognostic, and/or predictive value of individual microRNAs or microRNA signatures as potential biomarkers for patient management, shed light on regulatory mechanisms of RNA-RNA interactions that modulate gene expression, uncovered cell-autonomous and cell-to-cell communication roles of specific microRNAs, and developed a battery of viral and nonviral delivery approaches for therapeutic intervention. Despite these intense and prolific research efforts in preclinical and clinical settings, there are a limited number of microRNA-based applications that have been incorporated into clinical practice. We review recent literature and ongoing clinical trials that highlight most promising approaches and standing challenges to translate these findings into viable microRNA-based clinical tools for cancer medicine. This article is categorized under: RNA in Disease and Development > RNA in Disease.
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Affiliation(s)
- Lorenzo F. Sempere
- Department of Radiology, Precision Health ProgramMichigan State UniversityEast LansingMichiganUSA
| | - Asfar S. Azmi
- Department of OncologyWayne State University School of MedicineDetroitMichiganUSA
- Karmanos Cancer InstituteDetroitMichiganUSA
| | - Anna Moore
- Departments of Radiology and Physiology, Precision Health ProgramMichigan State UniversityEast LansingMichiganUSA
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Liu D, Li X, Lan Y, Zhang L, Wu T, Cui H, Li Z, Sun P, Tian P, Tian J. Models for Predicting Sentinel and Non-sentinel Lymph Nodes Based on Pre-operative Ultrasonic Breast Imaging to Optimize Axillary Strategies. ULTRASOUND IN MEDICINE & BIOLOGY 2021; 47:3101-3110. [PMID: 34362583 DOI: 10.1016/j.ultrasmedbio.2021.06.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/04/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.
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Affiliation(s)
- Dongmei Liu
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lei Zhang
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Tong Wu
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Hao Cui
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Ziyao Li
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Ping Sun
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Peng Tian
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.
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Yang A, Xiao W, Zheng S, Kong Y, Zou Y, Li M, Ye F, Xie X. Predictive Nomogram of Subsequent Liver Metastasis After Mastectomy or Breast-Conserving Surgery in Patients With Nonmetastatic Breast Cancer. Cancer Control 2021; 28:1073274821997418. [PMID: 33626925 PMCID: PMC8482719 DOI: 10.1177/1073274821997418] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Background: Metastasis accounts for the majority of deaths in patients with breast cancer. Liver metastasis is reported common for breast cancer patients. The purpose of this study was to construct a nomogram to predict the likelihood of subsequent liver metastasis in patients with nonmetastatic breast cancer, thus high-risk patient populations can be prevented and monitored. Methods: A total of 1840 patients with stage I-III breast cancer were retrospectively included and analyzed. A nomogram was constructed to predict liver metastasis based on multivariate logistic regression analysis. SEER database was used for external validation. C-index, calibration curve and decision curve analysis were used to evaluate the predictive performance of the model. Results: The nomogram included 3 variables related to liver metastasis: HER2 status (odds ratio (OR) 1.86, 95%CI 1.02 to 3.41; P = 0.045), tumor size (OR 3.62, 1.91 to 6.87; P < 0.001) and lymph node metastasis (OR 2.26, 1.18 to 4.34; P = 0.014). The C index of the training cohort, internal validation cohort and external validation cohort were 0.699, 0.814 and 0.791, respectively. The nomogram was well-calibrated, with no statistical difference between the predicted and the observed probabilities. Conclusion: We have developed and validated a robust tool enabled to predict subsequent liver metastasis in patients with nonmetastatic breast cancer. Distinguishing a population of patients at high risk of liver metastasis will facilitate preventive treatment or monitoring of liver metastasis.
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Affiliation(s)
- Anli Yang
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Weikai Xiao
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shaoquan Zheng
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yanan Kong
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yutian Zou
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Mingyue Li
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Feng Ye
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Feng Ye and Xiaoming Xie, Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China. Emails: ;
| | - Xiaoming Xie
- Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
- Feng Ye and Xiaoming Xie, Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China. Emails: ;
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Shi YR, Xiong K, Ye X, Yang P, Wu Z, Zu XB. Development of a prognostic signature for bladder cancer based on immune-related genes. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1380. [PMID: 33313125 PMCID: PMC7723522 DOI: 10.21037/atm-20-1102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background Although the prognosis of patients with bladder cancer (BC) has improved significantly with the use of multimodal therapy, reliable prognostic biomarkers are still urgently needed due to the heterogeneity of tumors. Our aim was to develop an individualized immune-related gene pair (IRGP) signature that could precisely predict prognosis in BC patients. Methods Gene expression profiles and corresponding clinical information were collected from eight microarray data sets and one RNA-Seq data set. Results Among 1,811 immune genes, a 30-IRGP signature consisting of 52 unique genes was generated in the training cohort, which significantly stratified patients into low- and high-risk groups in terms of overall survival. In the testing and validation cohorts, the IRGP signature was also associated with patient prognosis in the univariate and multivariate Cox regression analyses. Several biological processes, including the immune response, chemotaxis, and the inflammatory response, were enriched among genes in the IRGP signature. When the signature was integrated with the TNM stage, an IRGP nomogram was developed and showed improved prognostic accuracy relative to the IRGP signature alone. Conclusions In short, we identified a robust IRGP signature for estimating overall survival in BC patients that could also be used as a promising biomarker for identifying high-risk patients for individualized therapy.
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Affiliation(s)
- Ying-Rui Shi
- Department of Radiation Oncology, Hunan Cancer Hospital & the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.,Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Kun Xiong
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
| | - Xu Ye
- Department of Radiation Oncology, Hunan Cancer Hospital & the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Pei Yang
- Department of Radiation Oncology, Hunan Cancer Hospital & the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Zheng Wu
- Department of Radiation Oncology, Hunan Cancer Hospital & the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiong-Bing Zu
- Department of Urology, Xiangya Hospital, Central South University, Changsha, China
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Liu T, Du LT, Wang YS, Gao SY, Li J, Li PL, Sun ZW, Binang H, Wang CX. Development of a Novel Serum Exosomal MicroRNA Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma. Front Oncol 2020; 10:573501. [PMID: 33123480 PMCID: PMC7573187 DOI: 10.3389/fonc.2020.573501] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/26/2020] [Indexed: 12/13/2022] Open
Abstract
Preoperative prediction of lymph node (LN) metastasis is accepted as a crucial independent risk factor for treatment decision-making for esophageal squamous cell carcinoma (ESCC) patients. Our study aimed to establish a non-invasive nomogram to identify LN metastasis preoperatively in ESCC patients. Construction of the nomogram involved three sequential phases with independent patient cohorts. In the discovery phase (N = 20), LN metastasis-associated microRNAs (miRNAs) were selected from next-generation sequencing (NGS) assay of human ESCC serum exosome samples. In the training phase (N = 178), a nomogram that incorporated exosomal miRNA model and clinicopathologic was developed by multivariate logistic regression analysis to preoperatively predict LN status. In the validation phase (n = 188), we validated the predicted nomogram's calibration, discrimination, and clinical usefulness. Four differently expressed miRNAs (chr 8-23234-3p, chr 1-17695-5p, chr 8-2743-5p, and miR-432-5p) were tested and selected in the serum exosome samples from ESCC patients who have or do not have LN metastasis. Subsequently, an optimized four-exosomal miRNA model was constructed and validated in the clinical samples, which could effectively identify ESCC patients with LN metastasis, and was significantly superior to preoperative computed tomography (CT) report. In addition, a clinical nomogram consisting of the four-exosomal miRNA model and CT report was established in training cohort, which showed high predictive value in both training and validation cohorts [area under the receiver operating characteristic curve (AUC): 0.880 and 0.869, respectively]. The Hosmer–Lemeshow test and decision curve analysis implied the nomogram's clinical applicability. Our novel non-invasive nomogram is a robust prediction tool with promising clinical potential for preoperative LN metastasis prediction of ESCC patients, especially in T1 stage.
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Affiliation(s)
- Tong Liu
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lu-Tao Du
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
| | - Yun-Shan Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
| | - Shan-Yu Gao
- Department of Surgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Juan Li
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
| | - Pei-Long Li
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
| | - Zhao-Wei Sun
- Department of Surgery, The Affiliated Hospital of Medical College Qingdao University, Qingdao, China
| | - Helen Binang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chuan-Xin Wang
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.,Shandong Provincial Clinical Medicine Research Center for Clinical Laboratory, Jinan, China
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Wu W, Deng Z, Alafate W, Wang Y, Xiang J, Zhu L, Li B, Wang M, Wang J. Preoperative Prediction Nomogram Based on Integrated Profiling for Glioblastoma Multiforme in Glioma Patients. Front Oncol 2020; 10:1750. [PMID: 33194573 PMCID: PMC7609958 DOI: 10.3389/fonc.2020.01750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 08/05/2020] [Indexed: 12/23/2022] Open
Abstract
Introduction: Traditional classification that divided gliomas into glioblastoma multiformes (GBM) and lower grade gliomas (LGG) based on pathological morphology has been challenged over the past decade by improvements in molecular stratification, however, the reproducibility and diagnostic accuracy of glioma classification still remains poor. This study aimed to establish and validate a novel nomogram for the preoperative diagnosis of GBM by using integrated data combined with feasible baseline characteristics and preoperative tests. Material and method: The models were established in a primary cohort that included 259 glioma patients who had undergone surgical resection and were pathologically diagnosed from March 2014 to May 2016 in the First Affiliated Hospital of Xi'an Jiaotong University. The preoperative data were used to construct three models by the best subset regression, the forward stepwise regression, and the least absolute shrinkage and selection operator, and to furthermore establish the nomogram among those models. The assessment of nomogram was carried out by the discrimination and calibration in internal cohorts and external cohorts. Results and discussion: Out of all three models, model 2 contained eight clinical-related variables, which exhibited the minimum Akaike Information Criterion (173.71) and maximum concordance index (0.894). Compared with the other two models, the integrated discrimination index for model 2 was significantly improved, indicating that the nomogram obtained from model 2 was the most appropriate model. Likewise, the nomogram showed great calibration and significant clinical benefit according to calibration curves and the decision curve analysis. Conclusion: In conclusion, our study showed a novel preoperative model that incorporated clinically relevant variables and imaging features with laboratory data that could be used for preoperative prediction in glioma patients, thus providing more reliable evidence for surgical decision-making.
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Affiliation(s)
- Wei Wu
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Zhong Deng
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Wahafu Alafate
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yichang Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jianyang Xiang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lizhe Zhu
- Department of Breast Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bolin Li
- Department of Cardiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Maode Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jia Wang
- Department of Neurosurgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Center of Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
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Tan W, Xie X, Huang Z, Chen L, Tang W, Zhu R, Ye X, Zhang X, Pan L, Gao J, Tang H, Zheng W. Construction of an immune-related genes nomogram for the preoperative prediction of axillary lymph node metastasis in triple-negative breast cancer. ARTIFICIAL CELLS NANOMEDICINE AND BIOTECHNOLOGY 2020; 48:288-297. [PMID: 31858816 DOI: 10.1080/21691401.2019.1703731] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Immune system disorder is associated with metastasis of triple-negative breast cancers (TNBCs). A robust, individualized immune-related genes (IRGs)-based classifier was aimed to develop and validate in our study to precisely estimate the axillary lymph node (ALN) status preoperatively in patients with early-stage TNBC. We first analyzed RNA sequencing profiles in TNBC patients from The Cancer Genome Atlas database by using bioinformatics approaches, and screened 23 differentially expressed IRGs. A 9-gene panel was generated with an area under the curve (AUC) of 0.77 [95% confidence interval (95% CI), 0.68-0.87]. We detected the 9 ALN-status-related IRGs in the training set (n = 133) and developed a reduced and optimized five-IRGs signature, which effectively distinguished TNBC patients with ALN metastasis (AUC, 0.80; 95% CI, 0.65-0.86), and was superior to preoperative ultrasound-based ALN status (AUC, 0.73; 95% CI, 0.53-0.93). Predictive efficiency (AUC, 0.77; 95% CI 0.61-0.93) of this five-IRGs signature was validated in the validation set (n = 81). Furthermore, IRGs nomogram incorporated IRGs signature with US-based ALN status showed higher ALN status prediction efficacy than US-based ALN status and five-IRGs signature alone in both training and validation sets. IRGs nomogram may aid in identifying patients who can be exempted from axillary surgery.Novelty and impact: An immune-related genes (IRGs) nomogram was first developed and externally validated in our study, which incorporated the IRGs signature with ultrasound (US)-based axillary lymph nodes (ALN) status. IRGs nomogram is superior to IRGs signature alone for preoperative estimation of ALN metastasis in patients with triple-negative breast cancer (TNBC). It is a favourable biomarker for preoperatively predicting ALN metastasis risk and may aid in clinical decision-making in early-stage TNBCs.
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Affiliation(s)
- Weige Tan
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xinhua Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Zhongying Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Lun Chen
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wei Tang
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Renjie Zhu
- East Hospital Affiliated to Tongji University, Shanghai, China
| | - Xigang Ye
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaoshen Zhang
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lingxiao Pan
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jin Gao
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Hailin Tang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Wenbo Zheng
- Department of Breast Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
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20
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Gao Y, Luo Y, Zhao C, Xiao M, Ma L, Li W, Qin J, Zhu Q, Jiang Y. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients. Eur Radiol 2020; 31:928-937. [PMID: 32845388 DOI: 10.1007/s00330-020-07181-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 06/27/2020] [Accepted: 08/11/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVES To establish a prediction model for evaluating the axillary lymph node (ALN) status of patients with T1/T2 invasive breast cancer based on radiomics analysis of US images of primary breast lesions. METHODS Between August 2016 and November 2018, a total of 343 patients with histologically proven malignant breast tumors were included in this study and randomly assigned to the training and validation groups at a ratio of 7:3. ALN tumor burden was defined as low (< 3 metastatic ALNs) or high (≥ 3 metastatic ALNs). Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram combining the breast cancer US radiomics score with patient age and lesion size was generated based on the multivariate logistic regression results. RESULTS In the training and validation cohorts, 29.1% (69/237) and 32.08% (34/106) of patients were pathologically diagnosed with more than 2 metastatic ALNs, respectively. The radiomics score consisted of 16 US features, and patient age and lesion diameter identified by US were included to construct the model. The AUC of the model was 0.846 (95% CI, 0.790-0.902) for the training cohort and 0.733 (95% CI, 0.613-0.852) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. CONCLUSIONS Our novel nomogram demonstrates high accuracy in predicting ALN tumor burden in breast cancer patients. We also suggest further development of PyRadiomics to improve US radiomics. KEY POINTS • A nomogram based on US was developed to predict ALN tumor burden (low, < 3 metastatic ALNs; high, ≥ 3 metastatic ALNs). • The nomogram could assist clinicians in evaluating treatment strategies for T1/T2 invasive breast cancer.
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Affiliation(s)
- Yuanjing Gao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Yanwen Luo
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Chenyang Zhao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Mengsu Xiao
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Li Ma
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Wenbo Li
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Jing Qin
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China
| | - Qingli Zhu
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China.
| | - Yuxin Jiang
- Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Shuaifuyuan 1st, Dongcheng District, Beijing, 100730, China.
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21
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Yang Z, Lan X, Huang Z, Yang Y, Tang Y, Jing H, Wang J, Zhang J, Wang X, Gao J, Wang J, Xuan L, Fang Y, Ying J, Li Y, Huang X, Wang S. Development and external validation of a nomogram to predict four or more positive nodes in breast cancer patients with one to three positive sentinel lymph nodes. Breast 2020; 53:143-151. [PMID: 32823167 PMCID: PMC7451418 DOI: 10.1016/j.breast.2020.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/26/2020] [Accepted: 08/04/2020] [Indexed: 11/29/2022] Open
Abstract
Objective To develop a nomogram for predicting the possibility of four or more positive nodes in breast cancer patients with 1–3 positive sentinel lymph nodes (SLN). Materials and methods Retrospective analysis of data of patients from two institutions was conducted. The inclusion criteria were: invasive breast cancer; clinically node negative; received lumpectomy or mastectomy plus SLN biopsy followed by axillary lymph node dissection (ALND); and pathologically confirmed T1-2 tumor, with 1–3 positive SLNs. Patients from one institution formed the training group and patients from the other the validation group. Univariate and multivariate analyses were performed to identify the predictors of four or more positive nodes. These predictors were used to build the nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model. Results Of the 1480 patients (966 patients in the training group, 514 in the validation group), 306 (20.7%) had four or more positive nodes. Multivariate stepwise logistic regression showed number of positive (p < .001) and negative SLN (p < .001), extracapsular extension (p < .001), pT stage (p = .016), and tumor location in outer upper quadrant (p = .031) to be independent predictors of four or more positive nodes. The nomogram was built using these five factors. The AUC was 0.845 in the training group and 0.804 in the validation group. Conclusion The proposed nomogram appears to accurately estimate the likelihood of four or more positive nodes and could help radiation oncologists to decide on use of regional nodal irradiation (RNI) for breast cancer patients with 1–3 positive nodes but no ALND. Five predictors of four or more positive nodes in breast cancer patients were identified. A nomogram was built using these five factors. The nomogram was validated on an external cohort. The proposed nomogram predicts four or more positive nodes with high accuracy. The nomogram can help in decision making on use of regional nodal irradiation.
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Affiliation(s)
- Zhuanbo Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaowen Lan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Zhou Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yong Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yu Tang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Hao Jing
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianyang Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianghu Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiang Wang
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jidong Gao
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jing Wang
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lixue Xuan
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yi Fang
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianming Ying
- Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yexiong Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaobo Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
| | - Shulian Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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22
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Okuno J, Miyake T, Sota Y, Tanei T, Kagara N, Naoi Y, Shimoda M, Shimazu K, Kim SJ, Noguchi S. Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer. Ann Surg Oncol 2020; 28:310-319. [PMID: 32583195 DOI: 10.1245/s10434-020-08735-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Indexed: 11/18/2022]
Abstract
BACKGROUND The aim of our study is to find microRNAs (miRNAs) associated with sentinel lymph node metastasis (SLNM) and to develop a prediction model for SLNM in ER-positive and HER2-negative (ER+/HER2-) breast cancer. PATIENTS AND METHODS In the present study, only ER+/HER2- primary breast cancer was considered. The discovery set for SLNM-associated miRNAs included 10 tumors with and 10 tumors without SLNM. The training and validation sets both included 100 tumors. miRNA expression in tumors was examined comprehensively by miRNA microarray in the discovery set and by droplet digital PCR in the training and validation sets. RESULTS In the discovery set, miR-98, miR-22, and miR-223 were found to be significantly (P < 0.001, fold-change > 2.5) associated with SLNM. In the training set, we constructed the prediction model for SLNM using miR-98, tumor size, and lymphovascular invasion (LVI) with high accuracy (AUC, 0.877). The accuracy of this prediction model was confirmed in the validation set (AUC, 0.883), and it outperformed the conventional Memorial Sloan Kettering Cancer Center nomogram. In situ hybridization revealed the localization of miR-98 expression in tumor cells. CONCLUSIONS We developed a prediction model consisting of miR-98, tumor size, and LVI for SLNM with high accuracy in ER+/HER2- breast cancer. This model might help decide the indication for SLN biopsy in this subtype.
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Affiliation(s)
- Jun Okuno
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Tomohiro Miyake
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.
| | - Yoshiaki Sota
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Tomonori Tanei
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Naofumi Kagara
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Yasuto Naoi
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Masafumi Shimoda
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Kenzo Shimazu
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Seung Jin Kim
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Shinzaburo Noguchi
- Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
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23
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Wang W, Chen D, Chen W, Xin Z, Huang Z, Zhang X, Xi K, Wang G, Zhang R, Zhao D, Liu L, Zhang L. Early Detection of Non-Small Cell Lung Cancer by Using a 12-microRNA Panel and a Nomogram for Assistant Diagnosis. Front Oncol 2020; 10:855. [PMID: 32596148 PMCID: PMC7301755 DOI: 10.3389/fonc.2020.00855] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 04/30/2020] [Indexed: 12/19/2022] Open
Abstract
Background: We previously identified a 12-microRNA (miRNA) panel (miRNA-17, miRNA-146a, miRNA-200b, miRNA-182, miRNA-155, miRNA-221, miRNA-205, miRNA-126, miRNA-7, miRNA-21, miRNA-145, and miRNA-210) that aided in the early diagnosis of non-small cell lung cancer (NSCLC). We validated the diagnostic value of this miRNA panel and compared it with that of traditional tumor markers and radiological diagnosis. We constructed a nomogram based on the miRNA panel's results to predict the risk of NSCLC. Methods: Eighty-two participants with pulmonary nodules on a CT scan and who underwent a pathological examination and surgical treatment were enrolled in our study. Patients were randomly divided into a training group or a validation group. The miRNA concentrations were quantified by RT-PCR and log-transformed for analysis. The cutoff value was determined in the training group and then applied in the validation group. A comparison between the miRNAs and traditional tumor markers [CEA, NSE, and cytokeratin 19 fragment 21-1 (Cyfra21-1)] and radiological diagnosis was performed. A nomogram based on the miRNA panel's results to predict the risk of NSCLC was constructed. Results: The expression level of these 12 miRNAs was significantly higher in NSCLC patients than in benign patients. In the validation group, the specificity and positive predictive value were 96.4 and 95.8%, respectively, which were significantly higher than those using traditional tumor markers or radiological diagnosis. The sensitivity was 42.6%, which was also higher than that using tumor markers. Moreover, the sensitivity increased to 63.6% when the nodule diameters were larger than 2 cm. The miRNAs and seven clinical factors were integrated into the nomogram, and the calibration curves showed optimal agreement between the predicted and actual probabilities. Conclusions: Our miRNA panel has clinical value for the early detection of NSCLC. A nomogram was constructed and internally validated, and the results indicate that it can assist clinicians in making treatment recommendations in the clinic.
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Affiliation(s)
- Weidong Wang
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Thoracic Surgery, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Dongni Chen
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Weiwei Chen
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Ziya Xin
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zirui Huang
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xuewen Zhang
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Department of Anesthesiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Kexing Xi
- Department of Colorectal Surgery, Peking Union Medical College, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Gongming Wang
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Rusi Zhang
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Dechang Zhao
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Li Liu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Lanjun Zhang
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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24
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van Steenbeek CD, van Maaren MC, Siesling S, Witteveen A, Verbeek XAAM, Koffijberg H. Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands. BMC Med Res Methodol 2019; 19:117. [PMID: 31176362 PMCID: PMC6556016 DOI: 10.1186/s12874-019-0761-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 05/28/2019] [Indexed: 12/21/2022] Open
Abstract
Background Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform (https://www.evidencio.com) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation. Methods Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation. Results Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods. Conclusions This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population. Electronic supplementary material The online version of this article (10.1186/s12874-019-0761-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Cornelia D van Steenbeek
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht, DT, 3511, The Netherlands.,Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Drienerlolaan 5, Enschede, NB, 7522, The Netherlands
| | - Marissa C van Maaren
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht, DT, 3511, The Netherlands. .,Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Drienerlolaan 5, Enschede, NB, 7522, The Netherlands.
| | - Sabine Siesling
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht, DT, 3511, The Netherlands.,Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Drienerlolaan 5, Enschede, NB, 7522, The Netherlands
| | - Annemieke Witteveen
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht, DT, 3511, The Netherlands.,Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Drienerlolaan 5, Enschede, NB, 7522, The Netherlands
| | - Xander A A M Verbeek
- Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht, DT, 3511, The Netherlands
| | - Hendrik Koffijberg
- Department of Health Technology & Services Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Drienerlolaan 5, Enschede, NB, 7522, The Netherlands.
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25
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Lemberger M, Loewenstein S, Lubezky N, Nizri E, Pasmanik-Chor M, Barazovsky E, Klausner JM, Lahat G. MicroRNA profiling of pancreatic ductal adenocarcinoma (PDAC) reveals signature expression related to lymph node metastasis. Oncotarget 2019; 10:2644-2656. [PMID: 31080555 PMCID: PMC6498999 DOI: 10.18632/oncotarget.26804] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 02/22/2019] [Indexed: 12/13/2022] Open
Abstract
Lymph node (LN) metastasis occurs frequently in pancreatic ductal adenocarcinoma (PDAC), representing an advanced disease stage and independently predicting patient survival. Current nodal staging is inadequate preoperatively and even less so postoperatively, and molecular biomarkers are needed to improve prognostication and selection of therapy. Recent data have suggested important roles of miRNAs in PDAC tumorigenesis and progression. The aim of the present study was to identify miRNA expression signature for nodal spread in PDAC patients. Using PDAC human tissue specimens, we identified 39 miRNAs which were differently expressed in LN positive compared to LN negative PDAC samples. Of them, six miRNAs have been reported to play a role in cancer invasion and metastasis. A high versus low six- miRNA signature score was predictive of LN metastasis in the PDAC validation cohort. We demonstrated a similar expression pattern of four out of the six miRNAs in the plasma of PDAC patients. The results of our in-vitro studies revealed that miR-141 and miR-720 are involved in the process of epithelial to mesenchymal-transition in PDAC. These miRNAs significantly inhibited in vitro proliferation, migration and invasion of PDAC cells as evidence by gain- and loss- of function studies, specifically, via ZEB-1 and TWIST1 transcription factors, as well as through the activation of the MAP4K4/JNK signaling pathway.
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Affiliation(s)
- Moran Lemberger
- Laboratory of Surgical Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Division of Surgery, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Shelly Loewenstein
- Laboratory of Surgical Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Division of Surgery, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Nir Lubezky
- Laboratory of Surgical Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Division of Surgery, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Eran Nizri
- Laboratory of Surgical Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Division of Surgery, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | | | - Eli Barazovsky
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel
| | - Joseph M Klausner
- Division of Surgery, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.,The Nikolas and Elizabeth Shlezak Cathedra for Experimental Surgery, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Guy Lahat
- Laboratory of Surgical Oncology, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Division of Surgery, Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.,Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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Yang J, Wang T, Yang L, Wang Y, Li H, Zhou X, Zhao W, Ren J, Li X, Tian J, Huang L. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method. Sci Rep 2019; 9:4429. [PMID: 30872652 PMCID: PMC6418289 DOI: 10.1038/s41598-019-40831-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 02/14/2019] [Indexed: 12/13/2022] Open
Abstract
It is difficult to accurately assess axillary lymph nodes metastasis and the diagnosis of axillary lymph nodes in patients with breast cancer is invasive and has low-sensitivity preoperatively. This study aims to develop a mammography-based radiomics nomogram for the preoperative prediction of ALN metastasis in patients with breast cancer. This study enrolled 147 patients with clinicopathologically confirmed breast cancer and preoperative mammography. Features were extracted from each patient's mammography images. The least absolute shrinkage and selection operator regression method was used to select features and build a signature in the primary cohort. The performance of the signature was assessed using support vector machines. We developed a nomogram by incorporating the signature with the clinicopathologic risk factors. The nomogram performance was estimated by its calibration ability in the primary and validation cohorts. The signature was consisted of 10 selected ALN-status-related features. The AUC of the signature from the primary cohort was 0.895 (95% CI, 0.887-0.909) and 0.875 (95% CI, 0.698-0.891) for the validation cohort. The C-Index of the nomogram from the primary cohort was 0.779 (95% CI, 0.752-0.793) and 0.809 (95% CI, 0.794-0.833) for the validation cohort. Our nomogram is a reliable and non-invasive tool for preoperative prediction of ALN status and can be used to optimize current treatment strategy for breast cancer patients.
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Affiliation(s)
- Jingbo Yang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Tao Wang
- Department of Radiology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710068, China
| | - Lifeng Yang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Hongmei Li
- Department of Breast Diseases, Yan'an University Affiliated Hospital, Yan'an, Shaanxi, 716000, China
| | - Xiaobo Zhou
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina, 27157, USA.
| | - Weiling Zhao
- Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina, 27157, USA
| | - Junchan Ren
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Xiaoyong Li
- Department of Breast Diseases, Yan'an University Affiliated Hospital, Yan'an, Shaanxi, 716000, China
| | - Jie Tian
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China.
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Sethi S, Sethi S, Bluth MH. Clinical Implication of MicroRNAs in Molecular Pathology: An Update for 2018. Clin Lab Med 2019; 38:237-251. [PMID: 29776629 DOI: 10.1016/j.cll.2018.02.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
MicroRNAs (miRNAs) are poised to provide diagnostic, prognostic, and therapeutic targets for several diseases including malignancies for precision medicine applications. The miRNAs have immense potential in the clinical arena because they can be detected in the blood, serum, tissues (fresh and formalin-fixed paraffin-embedded), and fine-needle aspirate specimens. The most attractive feature of miRNA-based therapy is that a single miRNA could be useful for targeting multiple genes that are deregulated in cancers, which can be further investigated through systems biology and network analysis that may provide cancer-specific personalized therapy.
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Affiliation(s)
- Seema Sethi
- Department of Pathology, University of Michigan and VA Hospital, E300, 2215 Fuller Road, Ann Arbor, MI 48105, USA.
| | - Sajiv Sethi
- Department of Gastroenterology, University of South Florida, 12901 Bruce B. Downs Boulevard, MDC 82, Tampa, FL 33612, USA
| | - Martin H Bluth
- Department of Pathology, Wayne State University, School of Medicine, 540 East Canfield Street, Detroit, MI 48201, USA; Pathology Laboratories, Michigan Surgical Hospital, 21230 Dequindre Road, Warren, MI 48091, USA
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Li J, Ma W, Jiang X, Cui C, Wang H, Chen J, Nie R, Wu Y, Li L. Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer. J Cancer 2019; 10:1263-1274. [PMID: 30854136 PMCID: PMC6400691 DOI: 10.7150/jca.32386] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 01/08/2019] [Indexed: 12/15/2022] Open
Abstract
Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria. Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test. Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater. Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.
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Affiliation(s)
- Jiao Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Weimei Ma
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Xinhua Jiang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Chunyan Cui
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Hongli Wang
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Jiewen Chen
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Runcong Nie
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Yaopan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
| | - Li Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, People's Republic of China
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Zhang P, Yin J, Yuan L, Wang Q, Du X, Dong R, Wang C, Bai Q, Ji L, Zhang G, Lu J. MicroRNA-139 suppresses hepatocellular carcinoma cell proliferation and migration by directly targeting Topoisomerase I. Oncol Lett 2019; 17:1903-1913. [PMID: 30675254 DOI: 10.3892/ol.2018.9746] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 02/01/2018] [Indexed: 12/21/2022] Open
Abstract
microRNAs (miRNAs) have been determined to be associated with cancer progression and metastasis. Mir-139 is located on 11q13.4 and exhibits anti-oncogenic and anti-metastatic activity in human cancers. It is downregulated in various malignant tumor types. In the present study, the potential functions and targets of miR-139 in hepatocellular carcinoma (HCC) were explored. Using a combinational analysis of four miRNA target prediction tools and biological experiments, it was determined that Topoisomerase I (TOP1) is a direct target of miR-139 in HCC. Several traditional topoisomerase inhibitors have demonstrated anticancer activity, but their side effects outnumbered their anticancer potential. The present study determined that overexpression of miR-139 significantly inhibits HCC cell proliferation (P<0.05) and migration (P<0.05), which is largely due to TOP1 downregulation. The present study indicated that miR-139 exerts a tumor-suppressive effect during hepatocarcinogenesis via the suppression of expression of TOP1; therefore, miR-139 is a promising target for the treatment of HCC.
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Affiliation(s)
- Pengfei Zhang
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Jikai Yin
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Lijuan Yuan
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Qing Wang
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Xilin Du
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Rui Dong
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Chengguo Wang
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Qiangshan Bai
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Ling Ji
- Department of Orthopedics, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Guizhi Zhang
- Department of Liver Disease, The Third People's Hospital, Linfen, Shanxi 041000, P.R. China
| | - Jianguo Lu
- Department of General Surgery, Tangdu Hospital of The Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
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Shiino S, Matsuzaki J, Shimomura A, Kawauchi J, Takizawa S, Sakamoto H, Aoki Y, Yoshida M, Tamura K, Kato K, Kinoshita T, Kitagawa Y, Ochiya T. Serum miRNA-based Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Clin Cancer Res 2018; 25:1817-1827. [PMID: 30482779 DOI: 10.1158/1078-0432.ccr-18-1414] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 09/07/2018] [Accepted: 11/20/2018] [Indexed: 11/16/2022]
Abstract
PURPOSE Sentinel lymph node biopsy (SLNB) is the gold-standard procedure for evaluating axillary lymph node (ALN) status in patients with breast cancer. However, the morbidity of SLNB is not negligible, and the procedure is invasive for patients without ALN metastasis. Here, we developed a diagnostic model for evaluating ALN status using a combination of serum miRNAs and clinicopathologic factors as a novel less-invasive biomarker.Experimental Design: Preoperative serum samples were collected from patients who underwent surgery for primary breast cancer or breast benign diseases between 2008 and 2014. A total of 958 serum samples (921 cases of primary breast cancer, including 630 cases in the no ALN metastasis group and 291 cases in the ALN metastasis group, and 37 patients with benign breast diseases) were analyzed by miRNA microarray. Samples were randomly divided into training and test sets. Logistic LASSO regression analysis was used to construct diagnostic models in the training set, which were validated in the test set. RESULTS An optimal diagnostic model was identified using a combination of two miRNAs (miR-629-3p and miR-4710) and three clinicopathologic factors (T stage, lymphovascular invasion, and ultrasound findings), which showed a sensitivity of 0.88 (0.84-0.92), a specificity of 0.69 (0.61-0.76), an accuracy of 0.818, and an area under the receiver operating characteristic curve of 0.86 in the test set. CONCLUSIONS Serum miRNA profiles may be useful for the diagnosis of ALN metastasis before surgery in a less-invasive manner than SLNB.
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Affiliation(s)
- Sho Shiino
- Department of Breast Surgery, National Cancer Center Hospital, Tokyo, Japan.,Keio University School of Medicine, Tokyo, Japan
| | - Juntaro Matsuzaki
- Division of Molecular and Cellular Medicine, National Cancer Center Research Institute, Tokyo, Japan
| | - Akihiko Shimomura
- Department of Breast and Medical Oncology, National Cancer Center Hospital, Tokyo, Japan
| | | | | | - Hiromi Sakamoto
- Department of Biobank and Tissue Resources, National Cancer Center Research Institute, Tokyo, Japan
| | | | - Masayuki Yoshida
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan
| | - Kenji Tamura
- Department of Breast and Medical Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Ken Kato
- Department of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Takayuki Kinoshita
- Department of Breast Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Takahiro Ochiya
- Division of Molecular and Cellular Medicine, National Cancer Center Research Institute, Tokyo, Japan.
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Qu A, Yang Y, Zhang X, Wang W, Liu Y, Zheng G, Du L, Wang C. Development of a preoperative prediction nomogram for lymph node metastasis in colorectal cancer based on a novel serum miRNA signature and CT scans. EBioMedicine 2018; 37:125-133. [PMID: 30314890 PMCID: PMC6284350 DOI: 10.1016/j.ebiom.2018.09.052] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 09/25/2018] [Accepted: 09/30/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Preoperative prediction of lymph node (LN) status is of crucial importance for appropriate treatment planning in patients with colorectal cancer (CRC). In this study, we sought to develop and validate a non-invasive nomogram model to preoperatively predict LN metastasis in CRC. METHODS Development of the nomogram entailed three subsequent stages with specific patient sets. In the discovery set (n = 20), LN-status-related miRNAs were screened from high-throughput sequencing data of human CRC serum samples. In the training set (n = 218), a miRNA panel-clinicopathologic nomogram was developed by logistic regression analysis for preoperative prediction of LN metastasis. In the validation set (n = 198), we validated the above nomogram with respect to its discrimination, calibration and clinical application. FINDINGS Four differently expressed miRNAs (miR-122-5p, miR-146b-5p, miR-186-5p and miR-193a-5p) were identified in the serum samples from CRC patients with and without LN metastasis, which also had regulatory effects on CRC cell migration. The combined miRNA panel could provide higher LN prediction capability compared with computed tomography (CT) scans (P < .0001 in both the training and validation sets). Furthermore, a nomogram integrating the miRNA-based panel and CT-reported LN status was constructed in the training set, which performed well in both the training and validation sets (AUC: 0.913 and 0.883, respectively). Decision curve analysis demonstrated the clinical usefulness of the nomogram. INTERPRETATION Our nomogram is a reliable prediction model that can be conveniently and efficiently used to improve the accuracy of preoperative prediction of LN metastasis in patients with CRC.
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Affiliation(s)
- Ailin Qu
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| | - Yongmei Yang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan 250012, Shandong Province, China
| | - Xin Zhang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan 250012, Shandong Province, China
| | - Wenfei Wang
- Humanistic Medicine Research Center, Qilu Hospital, Shandong University, Jinan 250012, Shandong Province, China; Humanistic Medicine Research Center, Shandong University, Jinan 250012, Shandong Province, China
| | - Yingjie Liu
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan 250012, Shandong Province, China
| | - Guixi Zheng
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan 250012, Shandong Province, China
| | - Lutao Du
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China
| | - Chuanxin Wang
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China.
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Zheng Y, Fu S, He T, Yan Q, Di W, Wang J. Predicting prognosis in resected esophageal squamous cell carcinoma using a clinical nomogram and recursive partitioning analysis. Eur J Surg Oncol 2018; 44:1199-1204. [PMID: 29784506 DOI: 10.1016/j.ejso.2018.04.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 01/25/2018] [Accepted: 04/08/2018] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Development demand of precise medicine in resectable esophageal squamous cell carcinoma (ESCC) require to recognize patients at high risk treated by surgery alone. Thus, our aim was to construct a clinical nomogram and recursive partitioning analysis (RPA) to predict long-term survival in ESCC treated by surgery alone. METHODS Based on the patients with ESCC who treated by three-incisional esophagectomy and two-field lymphadenectomy alone, we identified and integrated significant prognostic factors for survival to build a nomogram. The nomogram was calibrated for overall survival (OS) and the predictive accuracy and discriminative ability was measured by concordance index (c-index) and Akaike information criterion (AIC). Based on the nomogram, the RPA was performed for risk stratification. RESULTS A total of 747 patients were included for analysis. Five independent prognostic factors were identified and entered into the nomogram. The calibration curves for probability of 1-, 3-, and 5-year OS showed optimal agreement between nomogram prediction and actual observation. The AIC value of the nomogram was lower than that of the 7th edition staging system, whereas the c-index of the nomogram was higher than that of the 7th edition staging system. The risk groups stratified by RPA allowed significant distinction between survival curves within respective TNM categories. CONCLUSION The RPA based on a clinical nomogram appears to be suitable for risk stratification in OS for resected ESCC. This practical system may help clinicians in decision making and design of clinical studies.
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Affiliation(s)
- Yuzhen Zheng
- Department of Thoracic Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, PR China
| | - Shenshen Fu
- Department of Ultrasonography, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, PR China
| | - Tiancheng He
- Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation, Guangzhou, Guangdong, PR China
| | - Qihang Yan
- Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation, Guangzhou, Guangdong, PR China
| | - Wenyu Di
- Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation, Guangzhou, Guangdong, PR China
| | - Junye Wang
- Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation, Guangzhou, Guangdong, PR China.
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