MicroRNA profiles for lymph node metastasis in T1-stage colorectal cancers.
ABSTRACT: If we can more accurately predict the likelihood of regional lymph node metastasis (LNM) for endoscopically resected T1-stage colorectal cancers (CRC), the number of unnecessary additional surgeries can be reduced. We aimed of identify miRNA markers that can predict LNM-positive tumors among T1-stage CRCs and we also developed a miRNA classifier set for facilitating the accuracy and applicability. Overall design: Tumor cell rich areas (>70% of tumor cells) were selected and microdissected under a microscope. Total RNA was extracted using the RecoverAll™ Total Nucleic Acid Isolation Kit for FFPE. Then, we examined global miRNA expression profiles from 16 T1-stage CRCs (7 LNM-positive and 9 LNM- negative tumors) using miRNA-array analysis.
INSTRUMENT(S): Agilent-031181 Unrestricted_Human_miRNA_V16.0_Microarray 030840 (Feature Number version)
Project description:BACKGROUND:It is critical for determining the optimum therapeutic solutions for T1-2 colorectal cancer (CRC) to accurately predict lymph node metastasis (LNM) status. The purpose of the present study is to establish and verify a nomogram to predict LNM status in T1-2 CRCs. METHODS:A total of 16 600 T1-2 CRC patients were enrolled and classified into the training, internal validation, and external validation cohorts. The independent predictive parameters were determined by univariate and multivariate analyses to develop a nomogram to predict the probability of LNM status. The calibration curve, the area under the receiver operating characteristic curve (AUROC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram, and an external verification cohort was to verify the applicability of the nomogram. RESULTS:Seven independent predictors of LNM in T1-2 CRC were identified in the multivariable analysis, including age, tumor site, tumor grade, perineural invasion, preoperative carcinoembryonic antigen, clinical assessment of LNM, and T stage. A nomogram incorporating the seven predictors was constructed. The nomogram yielded good discrimination and calibration, with AUROCs of 0.72 (95% confidence interval [CI]: 0.70-0.75), 0.70 (95% CI: 0.67-0.74), and 0.74 (95% CI: 0.71-0.79) in the training, internal validation, and external validation cohorts, respectively. DCA showed that the predictive scoring system had high clinical application value. CONCLUSIONS:We proposed a novel predictive model for LNM in T1-2 CRC patients to assist physicians in making treatment decisions. The nomogram is advantageous for tailoring therapy in T1-2 CRC patients.
Project description:Background:Endoscopic resection is increasingly used to treat pathological T1 (pT1) esophageal cancer (EC) patients. However, the procedures are limited by lymph node metastasis (LNM) and remain controversial. We aimed to construct a nomogram to predict the risk of LNM in patients with pT1 esophageal squamous cell carcinoma (ESCC). Methods:A total of 243 patients with pT1 ESCC who underwent esophagectomy and lymph node dissection at two different institutes between February 2013 and June 2019 were analyzed retrospectively. Patients were categorized into the negative group and the positive group according to whether there was LNM. Risk factors for LNM were evaluated by univariate and multivariate analyses. The nomogram was used to estimate the individual risk of LNM. Results:Forty-six (18.9%) of the 243 patients with pT1 ESCC exhibited LNM. The LNM rate in patients with stage T1a disease was 5.7% (5/88), and the rate in patients with stage T1b disease was 26.5% (41/155). Multivariable logistic regression analysis showed that tumor differentiation [odds ratio (OR) =1.942, 95% confidence interval (CI): 1.067-3.536, P=0.030], the T1 sub-stage (OR =4.750, 95% CI: 1.658-13.611, P=0.004), the preoperative alanine aminotransferase/aspartate aminotransferase ratio (LSR) (OR =5.371, 95% CI: 1.676-17.210, P=0.005), and the high-density lipoprotein cholesterol (HDL-C) level (OR =5.894, 95% CI: 1.917-18.124, P=0.002) were independent risk factors for LNM. The nomogram had relatively high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.803 (95% CI: 0.732-0.873). The calibration curve showed that the predicted probability of LNM was in good agreement with the actual probability. Conclusions:Clinicopathological and hematological parameters of tumor differentiation, the T1 sub-stage, the preoperative LSR, and the HDL-C level may predict the risk of LNM in T1 ESCC. The risk of LNM can be predicted by the nomogram.
Project description:Purpose:Minute T1 colorectal cancer (CRC) lesions (?5 mm) are rare; however, little is known about their characteristics and aggressiveness. In this study, we evaluated the characteristics of minute T1 CRC in relevance to pathology and treatment. Methods:This retrospective study included 849 patients with T1 CRC endoscopically or surgically treated between January 2001 and December 2016. The patients were stratified into 4 groups according to tumor size; minute group (?5 mm), small group (6-10 mm), medium group (11-20 mm), and large group (?21 mm). Clinicopathological variables were evaluated with respect to tumor size. Results:The incidence of the minute T1 CRC was 2.4% (20 of 849). Minute T1 CRC was significantly associated with flat type (minute, 25%; small, 12.6%; medium, 8.8%; large, 12.6%; P = 0.016), right-sided cancer (30%, 15.4%, 15.4%, 15.1%, P = 0.002) and the absence of background adenoma (BGA) (50%, 40.7%, 32.8%, 18.1%, P < 0.001). In patients who underwent surgery, lymph node metastasis (LNM) was significantly higher in the minute group (36.4%, 15.9%, 15.7%, 9.2%, P = 0.029). Conclusion:Minute T1 CRC is significantly associated with flat type, right-sided cancers, as well as with the absence of BGA and LNM. These results suggested the minute T1 CRC lesions are often aggressive and are likely to be missed during colonoscopy.
Project description: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.
Project description: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.
Project description:PURPOSE:Although some studies have reported differences in clinicopathological features between left- and right-sided advanced colorectal cancer (CRC), there are few reports regarding early-stage disease. In this study, we aimed to compare the clinicopathological features of left- and right-sided T1 CRC. METHODS:Subjects were 1142 cases with T1 CRC undergoing surgical or endoscopic resection between 2001 and 2018 at Showa University Northern Yokohama Hospital. Of these, 776 cases were left-sided (descending colon to rectum) and 366 cases were right-sided (cecum to transverse colon). We compared clinical (patients age, sex, tumor size, morphology, initial treatment) and pathological features (invasion depth, histological grade, lymphatic invasion, vascular invasion, tumor budding) including lymph node metastasis (LNM). RESULTS:Left-sided T1 CRC showed significantly higher rates of LNM (left-sided 12.0% vs. right-sided 5.4%, P <?0.05) and lymphatic invasion (left-sided 32.7% vs. right-sided 23.2%, P <?0.05). Especially, the sigmoid colon and rectum showed higher rates of LNM (12.4% and 12.1%, respectively) than other locations. Patients with left-sided T1 CRC were younger than those with right-sided T1 CRC (64.9 years ±11.5 years vs. 68.7?±?11.6 years, P <?0.05), as well as significantly lower rates of poorly differentiated carcinoma/mucinous carcinoma than right-sided T1 CRC (11.6% vs. 16.1%, P <?0.05). CONCLUSION:Left-sided T1 CRC, especially in the sigmoid colon and rectum, exhibited higher rates of LNM than right-sided T1 CRC, followed by higher rates of lymphatic invasion. These results suggest that tumor location should be considered in decisions regarding additional surgery after endoscopic resection. TRIAL REGISTRATION:This study was registered with the University Hospital Medical Network Clinical Trials Registry ( UMIN 000032733 ).
Project description:The occurrence of lymph node metastases (LNM) after endoscopic submucosal dissection (ESD) in patients with gastric cancer (GC) leads to poor prognosis. However, few biomarkers are available to predict LNM in GC patients. Thus, we measured expression of 6 cancer-related miRNAs using real-time RT-PCR in 102 GC samples that were randomized into a training set and a testing set (each, 51 cases). Using logistic regression, we identified 4-miRNA (miR-27b, miR-128, miR-100 and miR-214) signatures for predicting LNM in GC patients. Patients with high-risk scores for the 4-miRNA signature tended to have higher LNM than those with low-risk scores. Meanwhile, the ROC curve of the 4-miRNA signature was better for predicting LNM in GC patients. In addition, Cox regression analysis indicated that a 2-miRNA signature (miR-27b and miR-214) or a miR-214/N stage signature was predictive of survival for GC patients. This work describes a previously unrecognized 4-miRNA signature involved in LNM and a 2-miRNA signature or miR-214/N stage signature related to GC patients' survival.
Project description:BACKGROUND AND AIMS:Endoscopic resection (ER) for submucosal invasive colorectal cancer (T1 CRC) can be grouped as curative ER (C-ER) and non-curative ER (NC-ER). Little is known about the long-term outcomes of patients in these two groups. Therefore, we have evaluated the long-term outcomes in endoscopically resected T1 CRC patients in C-ER and NC-ER groups. METHODS:We conducted a retrospective study on 220 patients with T1 CRC treated with ER from January 2007 to December 2017. First, we investigated the long-term outcomes (5-year overall survival [OS] and recurrence-free survival [RFS]) in the C-ER group (n = 49). In the NC-ER group (n = 171), we compared long-term outcomes between patients who underwent additional surgical resection (ASR) (n = 117) and those who did not (surveillance-only, n = 54). RESULTS:T1 CRC patients in the C-ER and NC-ER groups had a median follow-up of 44 (interquartile range 32-69) months. There was no risk of tumor recurrence and cancer-related deaths in patients with C-ER. In the NC-ER group, the 5-year OS rates were 75.3% and 92.6% in the surveillance-only and ASR subgroups, respectively. The hazard ratio (HR) for ASR in NC-ER vs. surveillance-only in NC-ER was statistically insignificant. However, RFS rates were significantly different between the ASR (97.2%) and surveillance-only (84.0%) subgroups. Multivariate analysis indicated a submucosal invasion depth (SID) of >2500 µm and margin positivity to be associated with recurrence. CONCLUSIONS:The surveillance-only approach can be considered as an alternative surgical option for T1 CRCs in selected patients undergoing NC-ER.
Project description:To evaluate the pattern of lymph node metastasis (LNM) according to primary tumor location in T1 and T2 stage non-small cell lung cancer (NSCLC) patients.The data of 1916 NSCLC patients with LNM who underwent surgery with systematic nodal resection between November 2008 to December 2014 were included in the study. Analyses of tumor location, pathological T stage, and nodal metastasis were performed.In T1a stage patients, superior mediastinum, aortopulmonary, and inferior mediastinum lymph node metastases were observed in primary tumors present in the right upper lobe (RUL), left upper lobe (LUL) and right middle lobe (RML), respectively. In T1b-stage patients, superior mediastinum, aortopulmonary, and inferior mediastinum lymph node metastases were observed in the RML, LUL, and right lower lobe (RLL), respectively. In patients with T2a-stage, superior mediastinum, aortopulmonary and inferior mediastinum lymph node metastases were observed in the RUL, LUL, and RLL, respectively. However, in T2b-stage patients, RUL, LUL and RML locations were associated with superior mediastinum, aortopulmonary, and inferior mediastinum lymph node metastases, respectively. Multivariable logistic regression showed that T stage was significantly associated with mediastinal and intrapulmonary lymph node metastases. In addition, tumor location was significantly associated with N2 station LNM.LNM varied according to tumor location and T-stage, which are independent factors influencing N2 station LNM.
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.