Identification and external validation of a novel miRNA signature for lymph node metastasis prediction in submucosal-invasive gastric cancer patients

Lymph node metastasis determined miRNAs in esophageal squamous cell carcinoma

PURPOSE

There is no golden noninvasive and effective technique to diagnose lymph node metastasis (LNM) for esophageal squamous cell carcinoma (ESCC) patients. Here, a classifier was proposed consisting of miRNAs to screen ESCC patients with LNM from the ones without LNM.

METHODS

miRNA expression and clinical data files of 93 ESCC samples were downloaded from TCGA as the discovery set and 119 ESCC samples with similar dataset GSE43732 as the validation set. Differentially expressed miRNAs (DE-miRNAs) were analyzed between patients with LNM and without LNM. LASSO regression was performed for selecting the DE-miRNA pair to consist the classifier. To validate the accuracy and reliability of the classifier, the SVM and AdaBoost algorithms were applied. The CCK-8 and wound healing assay were used to evaluate the role of the miRNA in ESCC cells.

RESULT

There were 43 DE miRNAs between the LNM+ group and LNM- group. Among them, miR-224-5p, miR-99a-5p, miR-100-5p, miR-34c-5p, miR-503-5p, and miR-452-5p were identified by LASSO to establish the classifier. SVM and AdaBoost showed that the model could classify the ESCC patients with LNM from the ones without LNM precisely and reliably in 2 data sets. miR-224-5p in the classifier as the top contributor to discriminate the two groups of patients based on AdaBoost, promoted ESCC cell proliferation and migration in vitro.

CONCLUSION

The classifier based on these 6 miRNAs could classify the ESCC patients with LNM from the ones without LNM successfully.

Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer

BACKGROUND

The status of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) is particularly important for the formulation of clinical treatment. The purpose of this study was to construct a nomogram to predict the risk of LNM in EGC before operation.

METHODS

Univariate analysis and logistic regression analysis were used to determine the independent risk factors for LNM. The independent risk factors were included in the nomogram, and the prediction accuracy, discriminant ability and clinical practicability of the nomogram were evaluated by the receiver operating characteristic curve (ROC), calibration curve and clinical decision curve (DCA), and 100 times ten-fold cross-validation was used for internal validation.

RESULTS

33 (11.3%) cases of AGC were pathologically confirmed as LNM. In multivariate analysis, T stage, presence of enlarged lymph nodes on CT examination, carbohydrate antigen 199 (CA199), undifferentiated histological type and systemic inflammatory response index (SIRI) were risk factors for LNM. The area under the ROC curve of the nomogram was 0.86, the average area under the ROC curve of the 100-fold ten-fold cross-validation was 0.85, and the P value of the Hosmer-Lemeshow test was 0.60. In addition, the clinical decision curve, net reclassification index (NRI) and Integrated Discriminant Improvement Index (IDI) showed that the nomogram had good clinical utility.

CONCLUSIONS

We found that SIRI is a novel biomarker for preoperative prediction of LNM in EGC, and constructed a nomogram for preoperative prediction of the risk of LNM in EGC, which is helpful for the formulation of the clinical treatment strategies.

A CT-based Radiomics Model for Prediction of Lymph Node Metastasis in Early Stage Gastric Cancer

RATIONALE AND OBJECTIVES

To develop and validate a CT-based radiomics model for preoperative prediction of lymph node metastasis (LNM) in early stage gastric cancer (EGC).

MATERIALS AND METHODS

Four hundred and sixty-three consecutive EGC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. The predictive performance of radiomics signature was tested in the training and testing cohorts. Multivariate logistic regression analysis was conducted to build a radiomics-based model combined radiomics signature and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics signature comprised six robust features showed significant association with LNM in both cohorts. A radiomics model that incorporated radiomics signature and CT-reported lymph node status showed good calibration and discrimination in the training cohort (AUC = 0.91) and testing cohort (AUC = 0.89). Decision curve analysis confirmed the clinical utility of this model.

CONCLUSION

The CT-based radiomics model showed favorable accuracy for prediction of LNM in EGC and may help to improve clinical decision-making.

miR-940 is a new biomarker with tumor diagnostic and prognostic value

miR-940 is a microRNA located on chromosome 16p13.3, which has varying degrees of expression imbalance in many diseases. It binds to the 3′ untranslated region (UTR) and affects the transcription or post-transcriptional regulation of target protein-coding genes. For a diversity of cellular processes, including cell proliferation, migration, invasion, apoptosis, epithelial-to-mesenchymal transition (EMT), cell cycle, and osteogenic differentiation, miR-940 can affect them not only by regulating protein-coding genes but also long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in pathways. Intriguingly, miR-940 participates in four pathways that affect cancer development, including the Wnt/β-catenin pathway, mitogen-activated protein kinase (MAPK) pathway, PD-1 pathway, and phosphatidylinositol 3-kinase (PI3K)-Akt pathway. Importantly, the expression of miR-940 is intimately correlated with the diagnosis and prognosis of tumor patients, as well as to the efficacy of tumor chemotherapy drugs. In conclusion, our main purpose is to outline the expression of miR-940 in various diseases and the molecular biological and cytological functions of target genes in order to reveal its potential diagnostic and prognostic value as well as its predictive value of drug efficacy.

Establishment and verification of a nomogram for predicting the risk of lymph node metastasis in early gastric cancer

BACKGROUND

endoscopic submucosal dissection (ESD) has been widely recognized by patients and doctors due to its advantages in early gastric cancer (EGC). The accurate prediction of the risk of lymph node metastasis (LNM) in EGC is important to select suitable treatments with this procedure for patients. Unfortunately, the accuracy of endoscopic ultrasound and computed tomography in the diagnosis of EGC lymph node status is extremely limited. The purpose of the present study was to establish an LNM nomogram risk model of early gastric cancer patients based on clinical data, to guide treatment for clinicians.

METHODS

a retrospective examination of the records of EGC patients undergoing radical gastrectomy from August 2012 to August 2019 in the Gastrointestinal Center of Subei People's Hospital was performed. The clinicopathological data were classified into a training set and validation set according to the time. Univariate and multivariate analyses were performed to identify risk factors related to LNM. A risk model for predicting the occurrence of LNM in EGC was established and validated.

RESULTS

of the 503 EGC patients, 78 (15.5 %) had lymph node metastasis. Logistic stepwise regression analysis showed that the predictive factors included sex, tumor location, tumor diameter, differentiation, ulcer and lymphatic vascular invasion. The discrimination of the LNM prediction model was satisfactory with an AUC of 0.8033 (internal validation) and 0.7353 (external validation). The correction effect of the calibration was satisfactory and the DCA decision curve analysis showed a strong clinical practicability.

CONCLUSION

the nomogram risk prediction model of LNM has been established for EGC patients to assist in formulating personalized treatment plans.

A radiomics-based model for prediction of lymph node metastasis in gastric cancer

PURPOSE

To develop and validate a radiomics-based model for preoperative prediction of lymph node metastasis (LNM) in gastric cancer (GC).

METHOD

A total of 768 GC patients were enrolled in this retrospective study. Radiomics features were extracted from portal venous phase computed tomography (CT) scans. A radiomics signature was built with highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method in the training cohort (n = 486). The signature was further validated in internal validation (n = 240) and external testing cohorts (n = 42). Multivariate logistic regression analysis was conducted to build a model that combined radiomics signature, serum biomarkers, and lymph node status according to CT. Performance of the model was determined by its discrimination, calibration, and clinical usefulness. The predictive value of the model was also evaluated in early stage GC (EGC) subgroup.

RESULTS

The radiomics signature comprised 7 robust features showed favorable prediction efficacy in all cohorts. A radiomics-based model that incorporated radiomics signature, serum CA72-4, and CT-reported lymph node status had good calibration and discrimination in training cohort [AUC, 0.92; 95% confidence interval (CI), 0.89-0.95] and validation cohort (AUC 0.86; 95% CI, 0.81-0.91). The model also showed a favorable predictive performance for EGC patients with an AUC of 0.85 (95% CI, 0.76-0.94). Decision curve analysis confirmed the clinical utility of this model.

CONCLUSIONS

The radiomics-based model showed favorable accuracy for prediction of LNM in GC. The model may also serve as a noninvasive tool for preoperative evaluation of LNM in EGC.

Risk factors of tumor invasion and node metastasis in early gastric cancer with undifferentiated component: a multicenter retrospective study on biopsy specimens and clinical data

Background

Early gastric cancer (EGC) with undifferentiated component (UDC) is a more aggressive entity, where the significance of preoperative data to tumor invasion and lymph node metastasis (LNM) remains unclarified.

Methods

A total of 5,020 GC patients undergoing radical gastrectomy in three centers were reviewed, of which, EGC with UDC in preoperative biopsy specimens were enrolled. The histology of biopsy and surgical specimens was graded according to the proportion of UDC and signet ring cells (SRCs). Risk factors of tumor invasion and LNM were evaluated with histological, clinical and demographic data.

Results

Lower body mass index (BMI), melena and larger tumor size were the independent preoperative risk factors of both LNM and LVI, while ulcerative lesion (UL) and the lower third stomach were only correlated with LNM. No relevance was found between the histological features of biopsy specimens and LNM, but SRC or >50% UDC lowered the risk of lymphovascular invasion (LVI) and/or submucosal (SM) invasion. When surgical data (depth of invasion and LVI included) were added, lower BMI, melena and the lower third stomach were still the independent preoperative risk factors of LNM, and LVI, SRC and SM invasion also showed relevance to LNM. The performance of predictive models using pre- or postoperative histological data was comparable.

Conclusions

The preoperative data were significantly relevant to tumor invasion and LNM, showing comparable risk strength with surgical specimens in histology.

Identification and external validation of a novel miRNA signature for lymph node metastasis prediction in submucosal-invasive gastric cancer patients

Endoscopic resection (ER) has been increasingly performed in the treatment of early gastric cancer (GC). However, lymph node metastasis (LNM) can cause treatment failure with ER, especially in T1b patients. Here, we attempted to develop a miRNA-based classifier to detect LNM in T1b patients. Based on high-throughput data from The Cancer Genome Atlas, we identified 20 miRNAs whose expression significantly changed in T1-2 GC with LNM vs T1-2 GC without LNM. We then developed a miRNA signature to predict LNM of T1b GC using the LASSO model and backward step wise elimination approach in a training cohort. Furthermore, the predictive accuracy of this classifier was validated in both an internal testing group of 63 patients and an external independent group of 114 patients. This systematic and comprehensive in silico study identified a 7-miRNA signature with an area under the receiver operating characteristic curve (AUROC) value of 0.843 in T1-2 GC and 0.911 in T1 EGC. The backward elimination was further used to develop a 4-miRNA (miR-153-3p, miR-708, miR-940 and miR-375) risk-stratification model in the training cohort with an AUROC value of 0.898 in cohort 2. When pathologic results were used as a reference, the risk model yielded AUROC values of 0.829 and 0.792 in two cohorts of endoscopic biopsy specimens. This novel miRNA-LNM classifier works better than the currently used pathologic criteria of ER in T1b EGC. This classifier could individualize the management of T1b patients and facilitate treatment decisions.