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Wu H, Feng H, Miao X, Ma J, Liu C, Zhang L, Yang L. Construction and validation of a prognostic model based on 11 lymph node metastasis-related genes for overall survival in endometrial cancer. Cancer Med 2022; 11:4641-4655. [PMID: 35778922 PMCID: PMC9741985 DOI: 10.1002/cam4.4844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/28/2021] [Accepted: 12/23/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Endometrial cancer (EC) is one of the most common malignant tumors in female reproductive system. The incidence of lymph node metastasis (LNM) is only about 10% in clinically suspected early-stage EC patients. Discovering prognostic models and effective biomarkers for early diagnosis is important to reduce the mortality rate. METHODS A least absolute shrinkage and selection operator (LASSO) regression was conducted to identify the characteristic dimension decrease and distinguish porgnostic LNM related genes signature. Subsequently, a novel prognosis-related nomogram was constructed to predict overall survival (OS). Survival analysis was carried out to explore the individual prognostic significance of the risk model and key gene was validated in vitro. RESULTS In total, 89 lymph node related genes (LRGs) were identified. Based on the LASSO Cox regression, 11 genes were selected for the development of a risk evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better OS (p = 3.583e-08). The area under the ROC curve (AUC) of this model was 0.718 at 5 years of OS. Then, we developed an OS-associated nomogram that included the risk score and clinicopathological features. The concordance index of the nomogram was 0.769. The survival verification performed in three subgroups from the nomogram demonstrated the validity of the model. The AUC of the nomogram was 0.787 at 5 years OS. Proliferation and metastasis of HMGB3 were explored in EC cell line. External validation with 30 patients in our hospital showed that patients with low-risk scores had a longer OS (p-value = 0.03). Finally, we revealed that the most frequently mutated genes in the low-risk and high-risk groups are PTEN and TP53, respectively. CONCLUSIONS Our results suggest that LNM plays an important role in the prognosis, and HMGB3 was potential as a biomarker for EC patients.
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Affiliation(s)
- Hong Wu
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
| | - Haiqin Feng
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
| | - Xiaoli Miao
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
| | - Jiancai Ma
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
| | - Cairu Liu
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
| | - Lina Zhang
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
| | - Liping Yang
- Department of Obstetrics and GynecologyHandan Central HospitalHandanChina
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Huang CY, Liao KW, Chou CH, Shrestha S, Yang CD, Chiew MY, Huang HT, Hong HC, Huang SH, Chang TH, Huang HD. Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer. Front Oncol 2020; 9:1508. [PMID: 32039004 PMCID: PMC6985442 DOI: 10.3389/fonc.2019.01508] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 12/16/2019] [Indexed: 12/27/2022] Open
Abstract
Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC. Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established. Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively. Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.
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Affiliation(s)
- Chia-Yen Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Department of Obstetrics and Gynecology, Gynecologic Cancer Center, Cathay General Hospital, Taipei, Taiwan.,School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Kuang-Wen Liao
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, Taiwan
| | - Chih-Hung Chou
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.,Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - Sirjana Shrestha
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Chi-Dung Yang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, China.,Warshel Institute for Computational Biology, Chinese University of Hong Kong, Shenzhen, China
| | - Men-Yee Chiew
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Hsin-Tzu Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
| | - Hsiao-Chin Hong
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.,Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan.,School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, China.,Warshel Institute for Computational Biology, Chinese University of Hong Kong, Shenzhen, China
| | - Shih-Hung Huang
- Department of Pathology, Cathay General Hospital, Taipei, Taiwan
| | - Tzu-Hao Chang
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.,Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Hsien-Da Huang
- School of Life and Health Sciences, Chinese University of Hong Kong, Shenzhen, China.,Warshel Institute for Computational Biology, Chinese University of Hong Kong, Shenzhen, China
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Preoperative Prediction of Lymph Nodal Metastases in Endometrial Carcinoma: Is it Possible?: A Literature Review. Int J Gynecol Cancer 2019; 28:394-400. [PMID: 29303927 DOI: 10.1097/igc.0000000000001163] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Lymph node status is one of the most important prognostic factors in endometrial cancer and crucial for deciding adjuvant therapy. OBJECTIVE The aim of the study was to assess the different models used to predict lymphatic nodal disease. SEARCH STRATEGY A literature search was conducted to detect the relevant studies. INCLUSION CRITERIA Relevant papers comparing the preoperative modality with the final histopathological results including randomized clinical trials, case-control studies, and any publications with a minimum of 50 patients in the report. RESULTS Molecular-based predictors are still far from a practical application. Preoperative radiological scans (positron emission tomography, computed tomography, magnetic resonance imaging, and ultrasound) have shown the best predictor of lymphatic dissemination. However, there is currently no ideal model available, which can be used within standard clinical care. CONCLUSIONS Surgical staging still remains the criterion standard in the determination of lymph node status in endometrial cancer.
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Günakan E, Atan S, Haberal AN, Küçükyıldız İA, Gökçe E, Ayhan A. A novel prediction method for lymph node involvement in endometrial cancer: machine learning. Int J Gynecol Cancer 2018; 29:320-324. [PMID: 30718313 DOI: 10.1136/ijgc-2018-000033] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 07/28/2018] [Accepted: 08/30/2018] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. In this study we constructed prediction models for EC patients using the Naïve Bayes machine learning algorithm for LNI prediction. METHODS The study assessed 762 patients with EC. Algorithm models were based on the following histopathological factors: V1: final histology; V2: presence of lymphovascular space invasion (LVSI); V3: grade; V4: tumor diameter; V5: depth of myometrial invasion (MI); V6: cervical glandular stromal invasion (CGSI); V7: tubal or ovarian involvement; and V8: pelvic LNI. Logistic regression analysis was also used to evaluate the independent factors affecting LNI. RESULTS The mean age of patients was 59.1 years. LNI was detected in 102 (13.4%) patients. Para-aortic LNI (PaLNI) was detected in 54 (7.1%) patients, of which four patients had isolated PaLNI. The accuracy rate of the algorithm models was found to be between 84.2% and 88.9% and 85.0% and 97.6% for LNI and PaLNI, respectively. In multivariate analysis, the histologic type, LVSI, depth of MI, and CGSI were independently and significantly associated with LNI (p<0.001 for all). CONCLUSIONS Machine learning may have a place in the decision tree for the management of EC. This is a preliminary report about the use of a new statistical technique. Larger studies with the addition of sentinel lymph node status, laboratory findings, or imaging results with machine learning algorithms may herald a new era in the management of EC.
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Affiliation(s)
- Emre Günakan
- Department of Obstetrics and Gynecology, University of Medical Sciences, Keçioren Training and Research Hospital, Ankara, Turkey
| | | | | | | | - Ehad Gökçe
- Department of Obstetrics and Gynecology, Başkent University, School of Medicine, Ankara, Turkey
| | - Ali Ayhan
- Department of Obstetrics and Gynecology, Başkent University, School of Medicine, Ankara, Turkey
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PIK3CA exon9 mutations associate with reduced survival, and are highly concordant between matching primary tumors and metastases in endometrial cancer. Sci Rep 2017; 7:10240. [PMID: 28860563 PMCID: PMC5578954 DOI: 10.1038/s41598-017-10717-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 08/14/2017] [Indexed: 01/10/2023] Open
Abstract
Mutations of the phosphoinositide-3-kinase (PI3K) catalytic subunit alpha gene (PIK3CA) are frequent in endometrial cancer. We sequenced exon9 and exon20 of PIK3CA in 280 primary endometrial cancers to assess the relationship with clinicopathologic variables, patient survival and associations with PIK3CA mRNA and phospho-AKT1 by gene expression and protein data, respectively. While PIK3CA mutations generally had no impact on survival, and were not associated with clinicopathological variables, patients with exon9 charge-changing mutations, providing a positive charge at the substituted amino acid residue, were associated with poor survival (p = 0.018). Furthermore, we characterized PIK3CA mutations in the metastatic setting, including 32 patients with matched primary tumors and metastases, and found a high level of concordance (85.7%; 6 out of 7 patients), suggesting limited heterogeneity. PIK3CA mRNA levels were increased in metastases compared to the primary tumors (p = 0.031), independent of PIK3CA mutation status, which rather associated with reduced PIK3CA mRNA expression. PIK3CA mutated tumors expressed higher p-AKT/AKT protein levels, both within primary (p < 0.001) and metastatic lesion (p = 0.010). Our results support the notion that the PI3K signaling pathway might be activated, both dependent- and independently of PIK3CA mutations, an aspect that should be considered when designing PIK3 pathway targeting strategies in endometrial cancer.
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Zeng JH, Liang L, He RQ, Tang RX, Cai XY, Chen JQ, Luo DZ, Chen G. Comprehensive investigation of a novel differentially expressed lncRNA expression profile signature to assess the survival of patients with colorectal adenocarcinoma. Oncotarget 2017; 8:16811-16828. [PMID: 28187432 PMCID: PMC5370003 DOI: 10.18632/oncotarget.15161] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 01/24/2017] [Indexed: 02/06/2023] Open
Abstract
Growing evidence has shown that long non-coding RNAs (lncRNAs) can serve as prospective markers for survival in patients with colorectal adenocarcinoma. However, most studies have explored a limited number of lncRNAs in a small number of cases. The objective of this study is to identify a panel of lncRNA signature that could evaluate the prognosis in colorectal adenocarcinoma based on the data from The Cancer Genome Atlas (TCGA). Altogether, 371 colon adenocarcinoma (COAD) patients with complete clinical data were included in our study as the test cohort. A total of 578 differentially expressed lncRNAs (DELs) were observed, among which 20 lncRNAs closely related to overall survival (OS) in COAD patients were identified using a Cox proportional regression model. A risk score formula was developed to assess the prognostic value of the lncRNA signature in COAD with four lncRNAs (LINC01555, RP11-610P16.1, RP11-108K3.1 and LINC01207), which were identified to possess the most remarkable correlation with OS in COAD patients. COAD patients with a high-risk score had poorer OS than those with a low-risk score. The multivariate Cox regression analyses confirmed that the four-lncRNA signature could function as an independent prognostic indicator for COAD patients, which was largely mirrored in the validating cohort with rectal adenocarcinoma (READ) containing 158 cases. In addition, the correlative genes of LINC01555 and LINC01207 were enriched in the cAMP signaling and mucin type O-Glycan biosynthesis pathways. With further validation in the future, our study indicates that the four-lncRNA signature could serve as an independent biomarker for survival of colorectal adenocarcinoma.
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Affiliation(s)
- Jiang-Hui Zeng
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Liang Liang
- Department of General Surgery, First Affiliated Hospital of Guangxi Medical University (West Branch), Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Rong-Quan He
- Department of Medical Oncology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Rui-Xue Tang
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Xiao-Yong Cai
- Department of General Surgery, First Affiliated Hospital of Guangxi Medical University (West Branch), Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Jun-Qiang Chen
- Department of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Dian-Zhong Luo
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
| | - Gang Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China
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Mittal P, Klingler-Hoffmann M, Arentz G, Winderbaum L, Kaur G, Anderson L, Scurry J, Leung Y, Stewart CJ, Carter J, Hoffmann P, Oehler MK. Annexin A2 and alpha actinin 4 expression correlates with metastatic potential of primary endometrial cancer. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:846-857. [PMID: 27784647 DOI: 10.1016/j.bbapap.2016.10.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 09/30/2016] [Accepted: 10/16/2016] [Indexed: 12/12/2022]
Abstract
The prediction of lymph node metastasis using clinic-pathological data and molecular information from endometrial cancers lacks accuracy and is therefore currently not routinely used in patient management. Consequently, although only a small percentage of patients with endometrial cancers suffer from metastasis, the majority undergo radical surgery including removal of pelvic lymph nodes. Upon analysis of publically available data and published research, we compiled a list of 60 proteins having the potential to display differential abundance between primary endometrial cancers with versus those without lymph node metastasis. Using data dependent acquisition LC-ESI-MS/MS we were able to detect 23 of these proteins in endometrial cancers, and using data independent LC-ESI-MS/MS the differential abundance of five of those proteins was observed. The localization of the differentially expressed proteins, was visualized using peptide MALDI MSI in whole tissue sections as well as tissue microarrays of 43 patients. The proteins identified were further validated by immunohistochemistry. Our data indicate that annexin A2 protein level is upregulated, whereas annexin A1 and α actinin 4 expression are downregulated in tumours with lymph node metastasis compared to those without lymphatic spread. Moreover, our analysis confirmed the potential of these markers, to be included in a statistical model for prediction of lymph node metastasis. The predictive model using highly ranked m/z values identified by MALDI MSI showed significantly higher predictive accuracy than the model using immunohistochemistry data. In summary, using publicly available data and complementary proteomics approaches, we were able to improve the prediction model for lymph node metastasis in EC.
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Affiliation(s)
- Parul Mittal
- Adelaide Proteomics Centre, School of Biological Sciences, The University of Adelaide, SA 5005; Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, SA 5005
| | - Manuela Klingler-Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, The University of Adelaide, SA 5005; Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, SA 5005
| | - Georgia Arentz
- Adelaide Proteomics Centre, School of Biological Sciences, The University of Adelaide, SA 5005; Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, SA 5005
| | - Lyron Winderbaum
- Adelaide Proteomics Centre, School of Biological Sciences, The University of Adelaide, SA 5005; Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, SA 5005
| | - Gurjeet Kaur
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, 11800 Minden, Pulau Pinang, Malaysia
| | - Lyndal Anderson
- Department of Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - James Scurry
- Faculty of Health and Medicine, University of New South Wales, Callaghan, New South Wales, Australia
| | - Yee Leung
- School of Women's and Infants' Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Colin Jr Stewart
- School of Women's and Infants' Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Jonathan Carter
- Department of Gynaecological Oncology, Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
| | - Peter Hoffmann
- Adelaide Proteomics Centre, School of Biological Sciences, The University of Adelaide, SA 5005; Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, SA 5005.
| | - Martin K Oehler
- Discipline of Obstetrics and Gynaecology, School of Paediatrics and Reproductive Health, Research Centre for Reproductive Health, Robinson Institute, The University of Adelaide, SA 5005; Department of Gynaecological Oncology, Royal Adelaide Hospital, Adelaide, SA 5005, Australia.
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