Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma.
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ABSTRACT: Background
In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.Methods
Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.Results
LNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype.Conclusions
We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.
SUBMITTER: Berg HF
PROVIDER: S-EPMC7109044 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:This study aimed to develop a prediction model for lymph node metastasis using a gene expression signature in patients with endometrioid-type endometrial cancer. METHODS:Newly diagnosed endometrioid-type endometrial cancer cases in which the patients had undergone lymphadenectomy during a surgical staging procedure were identified from a national dataset (N = 330). Clinical and pathologic data were extracted from patient medical records, and gene expression datasets of their tumors were used to create a 12-gene predictive model for lymph node metastasis. We used principal components analysis on a training set (n = 110) to develop multivariate logistic models to predict low-risk patients having a probability of lymph node metastasis of less than 4%. The model with the highest prediction performance was selected for an evaluation set (n = 112), which, in turn, was validated in an independent validation set (n = 108). RESULTS:The model applied to the evaluation set showed 100% sensitivity (90% confidence interval [CI], 74%-100%) and 42% specificity (90% CI, 34%-51%), which resulted in 100% negative predictive value (90% CI, 89%-100%). In the validation set, we confirmed that the model consistently showed 100% sensitivity (90% CI, 88%-100%), 42% specificity (90% CI, 32%-50%), and 100% negative predictive value (90% CI, 88%-100%). CONCLUSIONS:Our 12-gene signature model is a useful tool for the identification of patients with endometrioid-type endometrial cancer at low risk of lymph node metastasis, particularly given that it can be used to analyze histologic tissue before surgery and used to tailor surgical options.
| S-EPMC5780243 | biostudies-other
Project description:BackgroundLymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. Anomalous microRNAs (miRNAs) are associated with cell functions and are becoming a powerful tool to characterize malignant transformation and metastasis. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC).MethodCandidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. Samples were randomly divided into the training and validation cohorts. A miRNA signature was built using a logistic regression model and was evaluated by the area under the curve (AUC) of receiver operating characteristic curve (ROC) and decision curve analysis (DCA). We also conducted pathway enrichment analysis and miRNA-gene regulatory network to look for potential genes and pathways engaged in LNM progression. Survival analysis was performed, and the miRNAs were tested whether they expressed differently in another independent GEO database.ResultThirty-one candidate miRNAs were screened and a final 15-miRNA signature was constructed by logistic regression. The model showed good calibration in the training and validation cohorts, with AUC of 0.824 (95% CI, 0.739-0.912) and 0.821 (95% CI, 0.691-0.925), respectively. The DCA demonstrated the miRNA signature was clinically useful. Hub miRNAs in signature seemed to contribute to EEC progression via mitotic cell cycle, cellular protein modification process, and molecular function. MiR-34c was statistically significant in survival that a higher expression of miR-34c indicated a higher survival time. MiR-34c-3p, miR-34c-5p, and miR-34b-5p were expressed differentially in GSE75968.ConclusionThe miRNA signature could work as a noninvasive method to detect LNM in EEC with a high prediction accuracy. In addition, miR-34c cluster may be a key biomarker referring LNM in endometrial cancer.
| S-EPMC8082502 | biostudies-literature
Project description:ObjectiveWe report the incidence of occult nodal metastasis in patients who underwent primary surgical staging for apparent early endometrioid or serous endometrial cancer with bilateral SLN mapping and enhanced pathology. Occult ovarian metastasis rates were also reported.MethodsPatients with clinical stage I serous or endometrioid endometrial cancer who underwent primary staging surgery with successful bilateral SLN mapping from 1/2005-12/2018 were retrospectively evaluated. Rates of isolated tumor cells (ITCs), micro- and macrometastatic nodal disease, and occult ovarian involvement were reported.ResultsOf 1044 patients, 959 had endometrioid and 85 serous carcinoma. There were no positive SLNs among 510 patients with noninvasive FIGO grade 1/2 endometrioid carcinoma and < 1%ITCs. Grade 1: 4.5%(9/202) with inner-half and 10%(6/62) with outer-half myoinvasion had positive SLNs. Grade 2: rates were 4%(3/76) and 20%(8/41), respectively. Grade 3: 5%(1/20) with noninvasive, 3%(1/31) with inner-half, and 24%(4/17) with outer-half myoinvasion had positive SLNs. ITC incidence increased with depth of myoinvasion-25% of deeply invasive grade 1/2 and 18% of deeply invasive grade 3 tumors. Four (10%) of 41 patients with noninvasive serous endometrial carcinoma had ITCs or positive SLNs. There were no occult ovarian metastases with grades 1/2 disease, 2/68 (3%) with grade 3 disease, and 2/85 (2%) with serous endometrial carcinoma.ConclusionUltrastaging SLNs may be unwarranted in low-grade noninvasive endometrioid cancer but valuable in noninvasive serous carcinoma. Occult ovarian metastasis is uncommon in early endometrial carcinoma and occurs in 2-3% of high-risk histologies. Further research is needed to determine ITC significance, particularly with regard to adjuvant treatment.
| S-EPMC7293586 | biostudies-literature
Project description:BackgroundLymph node metastasis (LNM) is a critical unfavorable prognostic factor in endometrial cancer (EC). At present, models involving molecular indicators that accurately predict LNM are still uncommon. We addressed this gap by developing nomograms to individualize the risk of LNM in EC and to identify a low-risk group for LNM.MethodsIn all, 776 patients who underwent comprehensive surgical staging with pelvic lymphadenectomy at the First Affiliated Hospital of Chongqing Medical University were divided into a training cohort (used for building the model) and a validation cohort (used for validating the model) according to a predefined ratio of 7:3. Logistics regression analysis was used in the training cohort to screen out predictors related to LNM, after which a nomogram was developed to predict LNM in patients with EC. A calibration curve and consistency index (C-index) were used to estimate the performance of the model. A receiver operating characteristic (ROC) curve and Youden index were used to determine the optimal threshold of the risk probability of LNM predicted by the model proposed in this study. Then, the prediction performance of different models and their discrimination abilities for identifying low-risk patients were compared.ResultLNM occurred in 87 and 42 patients in the training and validation cohorts, respectively. Multivariate logistic regression analysis showed that histological grade (P=0.022), myometrial invasion (P=0.002), lymphovascular space invasion (LVSI) (P=0.001), serum CA125 (P=0.008), Ki67 (P=0.012), estrogen receptor (ER) (0.009), and P53 (P=0.003) were associated with LNM; a nomogram was then successfully established on this basis. The internal and external calibration curves showed that the model fits well, and the C-index showed that the prediction accuracy of the model proposed in this study was better than that of the other models (the C-index of the training and validation cohorts was 0.90 and 0.91, respectively). The optimal threshold of the risk probability of LNM predicted by the model was 0.18. Based on this threshold, the model showed good discrimination for identifying low-risk patients.ConclusionCombining molecular indicators based on classical clinical parameters can predict LNM of patients with EC more accurately. The nomogram proposed in this study showed good discrimination for identifying low-risk patients with LNM.
| S-EPMC8372407 | biostudies-literature
Project description:BackgroundThis study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it.MethodsClinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM.ResultsThe overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834-25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849-0.982) and 0.873(95%CI:0.776-0.970), respectively.ConclusionsWe developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.
| S-EPMC8243766 | biostudies-literature
Project description:We developed an efficient microRNA (miRNA) model that could predict the risk of lymph node metastasis (LNM) in hepatocellular carcinoma (HCC). We first evaluated a training cohort of 192 HCC patients after hepatectomy and found five LNM associated predictive factors: vascular invasion, Barcelona Clinic Liver Cancer stage, miR-145, miR-31, and miR-92a. The five statistically independent factors were used to develop a predictive model. The predictive value of the miRNA-based model was confirmed in a validation cohort of 209 consecutive HCC patients. The prediction model was scored for LNM risk from 0 to 8. The cutoff value 4 was used to distinguish high-risk and low-risk groups. The model sensitivity and specificity was 69.6 and 80.2%, respectively, during 5 years in the validation cohort. And the area under the curve (AUC) for the miRNA-based prognostic model was 0.860. The 5-year positive and negative predictive values of the model in the validation cohort were 30.3 and 95.5%, respectively. Cox regression analysis revealed that the LNM hazard ratio of the high-risk versus low-risk groups was 11.751 (95% CI, 5.110-27.021; P < 0.001) in the validation cohort. In conclusion, the miRNA-based model is reliable and accurate for the early prediction of LNM in patients with HCC.
| S-EPMC4823129 | biostudies-literature
Project description:BackgroundThe amplification or mutation of oncogenes and escape from immune surveillance systems promote tumor metastasis. However, subtle changes in the immune microenvironment and signaling pathways are poorly understood during the formation of lymphovascular space involvement (LVSI) and lymph node (LN) metastasis of endometrioid endometrial adenocarcinoma (EEA).Patients and methodsWe detected tumor immunology-related signaling pathways and immunocyte subtypes according to the mRNA levels of 750 oncogenes and genes relating to the tumor microenvironment and immune response using the Nanostring PanCancer IO 360 Panel in 24 paraffin-embedded tissues of EEAs and benign gynecological diseases. Internal reference genes were used for data normalization.ResultsAngiogenesis and immune cell adhesion signaling pathways were activated during LVSI formation of EEA progression. However, during the development of LVSI to LN metastasis, immune system signaling pathways were significantly inhibited, including antigen presentation, cytotoxicity, lymphoid compartment, interferon signaling, and costimulatory signaling pathways. Immune-related genes (CD69, HLA-DOA, ATF3, GBP1, AP2, DTX3L, EGR1, GBP4, TAP1, EIF2AK2, MX1, ISG15, STAT1, and HLA-DRA) were significantly downregulated in EEA with LN metastasis compared to those in EEA with LVSI. Instead, hypoxia, metabolic stress, epigenetic regulation, matrix remodeling, and metastasis signaling pathways were continuously activated in LN metastasis. We also found that neutrophils, macrophages, and mast cells might be involved in LVSI formation and LN metastasis in EEA.ConclusionsEEA with metastatic LNs showed significant immunosuppressive effects. Some oncogenes, matrix remodeling- and hypoxia-related genes, and neutrophil signatures showed higher expression, suggesting their potential as therapeutic targets and offering new immunotherapy strategies in EEA during LN metastasis.
| S-EPMC7756104 | biostudies-literature
Project description:Objective: To compare the prognostic predictive performance of six lymph node (LN) staging schemes: American Joint Committee on Cancer (AJCC) N stage, number of retrieved lymph nodes (NRLN), number of positive lymph nodes (NPLN), number of negative lymph nodes (NNLN), lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS) among node-positive endometrioid endometrial cancer (EEC) patients. Methods: A total of 3,533 patients diagnosed with node-positive EEC between 2010 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively analyzed. We applied X-tile software to identify the optimal cutoff value for different staging schemes. Univariate and multivariate Cox regression models were used to assess the relationships between different LN schemes and survival outcomes [disease-specific survival (DSS) and overall survival (OS)]. Moreover, Akaike information criterion (AIC) and Harrell concordance index (C-index) were used to evaluate the predictive performance of each scheme in both continuous and categorical patterns. Results: N stage (N1/N2) was not an independent prognostic factor for node-positive EEC patients based on multivariate analysis (DSS: p = 0.235; OS: p = 0.145). Multivariate model incorporating LNR demonstrated the most superior goodness of fit regardless of continuous or categorical pattern. Regarding discrimination power of the models, LNR outperformed other models in categorical pattern (OS: C-index = 0.735; DSS: C-index = 0.737); however, LODDS obtained the highest C-index in continuous pattern (OS: 0.736; DSS: 0.739). Conclusions: N stage (N1/N2) was unable to differentiate the prognosis for node-positive EEC patients in our study. However, LNR and LODDS schemes seemed to have a better predictive performance for these patients than other number-based LN schemes whether in DSS or OS, which revealed that LNR and LODDS should be more helpful in prognosis assessment for node-positive EEC patients than AJCC N stage.
| S-EPMC8298894 | biostudies-literature
Project description:PurposeTo evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC).Materials and methods108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared.ResultsMRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy.ConclusionMRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.
| S-EPMC10493896 | biostudies-literature
Project description:Non-invasive assessment of the risk of lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC) is of great value for the treatment option selection. The purpose of this paper is to develop a transfer learning radiomics (TLR) model for preoperative prediction of LNM in PTC patients in a multicenter, cross-machine, multi-operator scenario. Here we report the TLR model produces a stable LNM prediction. In the experiments of cross-validation and independent testing of the main cohort according to diagnostic time, machine, and operator, the TLR achieves an average area under the curve (AUC) of 0.90. In the other two independent cohorts, TLR also achieves 0.93 AUC, and this performance is statistically better than the other three methods according to Delong test. Decision curve analysis also proves that the TLR model brings more benefit to PTC patients than other methods.
| S-EPMC7511309 | biostudies-literature