Dataset Information

A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma.

ABSTRACT: Objective:To develop and validate a novel RNA-seq-based nomogram for preoperative prediction of lymph node metastasis (LNM) for patients with oral squamous cell carcinoma (OSCC). Methods:RNA-seq data for 276 OSCC patients (including 157 samples with LNM and 119 without LNM) were downloaded from TCGA database. Differential expression analysis was performed between LNM and non-LNM of OSCC. These samples were divided into a training set and a test set by a ratio of 9?:?1 while the relative proportion of the non-LNM and LNM groups was kept balanced within each dataset. Based on clinical features and seven candidate RNAs, we established a prediction model of LNM for OSCC using logistic regression analysis. Tenfold crossvalidation was utilized to examine the accuracy of the nomogram. Decision curve analysis was performed to evaluate the clinical utility of the nomogram. Results:A total of 139 differentially expressed RNAs were identified between LNM and non-LNM of OSCC. Seven candidate RNAs were screened based on FPKM values, including NEURL1, AL162581.1 (miscRNA), AP002336.2 (lncRNA), CCBE1, CORO6, RDH12, and AC129492.6 (pseudogene). Logistic regression analysis revealed that the clinical N stage (p < 0.001) was an important factor to predict LNM. Moreover, three RNAs including RDH12 (p value < 0.05), CCBE1 (p value < 0.01), and AL162581.1 (p value < 0.05) could be predictive biomarkers for LNM in OSCC patients. The average accuracy rate of the model was 0.7661, indicating a good performance of the model. Conclusion:Our findings constructed an RNA-seq-based nomogram combined with clinicopathology, which could potentially provide clinicians with a useful tool for preoperative prediction of LNM and be tailored for individualized therapy in patients with OSCC.

SUBMITTER: Qiao B

PROVIDER: S-EPMC7479460 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma.

Qiao Bo B Zhao Min M Wu Jing J Wu Huan H Zhao Yiming Y Meng Fanhao F Tian Yu Y Wang Situo S Shi Jinlong J Zhang Haizhong H

BioMed research international 20200831

<h4>Objective</h4>To develop and validate a novel RNA-seq-based nomogram for preoperative prediction of lymph node metastasis (LNM) for patients with oral squamous cell carcinoma (OSCC).<h4>Methods</h4>RNA-seq data for 276 OSCC patients (including 157 samples with LNM and 119 without LNM) were downloaded from TCGA database. Differential expression analysis was performed between LNM and non-LNM of OSCC. These samples were divided into a training set and a test set by a ratio of 9 : 1 while the re ...[more]

PMID: 32934959

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Project description:ObjectiveMET positivity is independently associated with survival in oral squamous cell carcinoma (OSCC). Since MET is a known orchestrator of invasive tumor growth, we investigated its association with LNM in early oral tongue squamous cell carcinoma (OTSCC). As it is recommended by the NCCN to use tumor depth of invasion (DOI) in making decisions on elective neck dissection (END), the results obtained for MET positivity were aligned with those for DOI > 4 mm. The cutoff value used in our institution.MethodsTumor samples from patients who underwent primary tumor resection and neck dissection between 1995 and 2013, were collected from the archives of the Leiden and Erasmus University Medical Center. Immunohistochemistry with D1C2 was performed to identify MET negative (< 10% uniform positivity) and MET positive (≥ 10% uniform positivity) cancers. ROC curve analysis and the Chi-squared test were used to investigate the association of MET positivity with LNM (pN+ and occult). Binary logistic regression was used to investigate the association of MET positivity with LNM.ResultsForty-five (44.1%) of the 102 cancers were MET positive. Ninety were cN0 of which 20 were pN+ (occult metastasis). The remaining 12 cancers were cN+, of which 10 were proven pN+ and 2 were pN0. MET positivity was associated with LNM with a positive predictive value (PPV) of 44.4% and a negative predictive value (NPV) of 82.5% for pN+. For the occult group, the PPV was 36.8% and the NPV was 88.5%. Regression analysis showed that MET positivity is associated with pN+ and occult LNM (p-value < 0.05).ConclusionMET positivity is significantly associated with LNM in early OTSCC, outperforming DOI. The added value of MET positivity could be in the preoperative setting when END is being considered during the initial surgery. For cases with DOI ≤ 4 mm, MET positivity could aid in the clinical decision whether regular follow-up, watchful waiting, or END is more appropriate. Realizing that these preliminary results need to be independently validated in a larger patient cohort, we believe that MET positivity could be of added value in the decision making on END in early OTSCC.

Project description:Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm2, which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm2) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Dataset Information

A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma.

Publications

A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma.

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