Lymph node metastasis and tumor-educated immune tolerance: Potential therapeutic targets against distant metastasis

Construction of a nomogram for predicting HNSCC distant metastasis and identification of EIF5A as a hub gene

Patients with distant metastasis of head and neck squamous cell carcinoma (HNSCC) often have a poor prognosis. However, early diagnosis of distant metastasis is challenging in clinical practice, and distant metastasis is often only detected in the late stages of tumor metastasis through imaging techniques. In this study, we utilized data from HNSCC patients collected from the TCGA database. Patients were divided into distant metastasis and nonmetastasis groups based on the tumor-node-metastasis (TNM) stage. We analyzed the differentially expressed genes between the two groups (DM/non-M DEGs) and their associated lncRNAs and generated a predictive model based on 23 lncRNAs that were significantly associated with the occurrence of distant metastasis in HNSCC patients. On this basis, we built a nomogram to predict the distant metastasis of HNSCC patients. Moreover, through WGCNA and Cytoscape software analysis of DM/non-M DEGs, we identified the gene most closely related to HNSCC distant metastasis: EIF5A. Our findings were validated using GEO data; EIF5A expression was significantly increased in the tumor tissues of HNSCC patients with distant metastasis. We then predicted miRNAs that can directly bind to EIF5A via the TargetScan and miRWalk websites, intersected them with differentially expressed miRNAs in the two groups from the TCGA cohort, and identified the only overlapping miRNA, miR-424; we predicted the direct binding site of EIF5A and miR-424 via the miRWalk website. Immunohistochemistry further revealed high expression of EIF5A in the primary tumor tissue of HNSCC patients with distant metastasis. These results provide a new perspective for the early diagnosis of distant metastasis in HNSCC patients and the study of the mechanisms underlying HNSCC distant metastasis.

Differential diagnosis of cervical lymphadenopathy: Integration of postvascular phase of contrast-enhanced ultrasound and predictive nomogram model

BACKGROUND

Distinguishing benign from malignant cervical lymph nodes is critical yet challenging. This study evaluates the postvascular phase of contrast-enhanced ultrasound (CEUS) and develops a user-friendly nomogram integrating demographic, conventional ultrasound, and CEUS features for accurate differentiation.

METHODS

We retrospectively analyzed 395 cervical lymph nodes from 395 patients between January 2020 and December 2022. The cohort was divided into training and validation sets using stratified random sampling. A predictive model, based on demographic, ultrasound, and CEUS features, was created and internally validated.

RESULTS

The training set included 280 patients (130 benign, 150 malignant nodes) and the validation set 115 patients (46 benign, 69 malignant). Relative hypoenhancement in the postvascular phase emerged as a promising indicator for MLN, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 96.7 %,52.3 %, 70.0 %, 93.2 %, and 76.1 %, respectively in the training set and 95.7 %, 52.2 %, 75.0 %, 88.9 %, and 74.8 % in the validation set. Age over 50 years, history of malignancy, short-axis diameter greater than 1.00 cm, focal hyperechogenicity, ill-defined borders, and centripetal perfusion were also identified as independent MLN indicators. The nomogram prediction model showed outstanding accuracy, with an area under the curve (AUC) of 0.922 (95 % CI: 0.892-0.953) in the training set and 0.914 (95 % CI: 0.864-0.963) in the validation set.

CONCLUSION

Relative hypoenhancement in the postvascular phase of CEUS, combined with demographics and ultrasound features, is effective for identifying MLNs. The developed prediction model, with a user-friendly nomogram, can facilitate clinical decision-making.