Screening and validation of lymph node metastasis risk-factor genes in papillary thyroid carcinoma

IPCEF1: Expression Patterns, Clinical Correlates and New Target of Papillary Thyroid Carcinoma

Introduction: Despite the generally favorable prognosis of PTC (Papillary Thyroid Carcinoma), it can still exhibit aggressive behavior and lead to patient mortality. IPCEF1 (interaction protein for cytohesin exchange factors 1) has emerged as a critical player in cell signaling related to proliferation and migration in cancer progression. Objective: Our research aimed to determine whether IPCEF1 is a key gene in PTC, elucidate its possible molecular mechanisms and ultimately search for new targets. Methods: This research utilized four gene expression array datasets and TCGA database to examine the role of IPCEF1 in PTC. Differential gene expression analysis, survival analysis, KEGG and GO enrichment and immune cell infiltration correlations were realized by bioinformatic methods. The expressions of IPCEF1 in PTC tissues were examined by IHC and the proliferation, migration, cell cycles of PTC cells were examined by CCK8, transwell and flow cytometry. Results: IPCEF1 had lower expression in PTC tumor tissues and its lower expression might lead to worse T/N stage and DFS/ PFS, which is perhaps related to its regulation of the JAK/STAT signaling pathway and immune microenvironment (macrophage and Tregs). IPCEF1 reduced the proliferation and migration ability of PTC cells, which is consistent with our clinical observations. Besides, we also found that high expression level of IPCEF1 lead to cell cycle arrest in the S or G2 phase, which ultimately reduced cell growth and proliferation. Conclusion: IPCEF1 is a cancer suppressor gene in the progression of PTC, influencing patient survival and prognosis through modulation of immune infiltration and signaling pathways.

[Analysis of risk factors for lymph node metastasis in patients with CN0 papillary thyroid carcinoma]

Objective:To explore the risk factors of lymph node metastasis and multiple lymph node metastasis in patients with stage CN0 papillary thyroid carcinoma. Methods:The clinical case data of 3 099 patients with CN0 papillary thyroid cancer who underwent lymph node dissection at Xijing Hospital of Air Force Medical University from January 2013 to December 2022 were retrospectively analyzed, univariate and multivariate logistic regression were used to analyze the risk factors of lymph node metastasis and multiple lymph node metastasis. Results:Male gender, age<55 years, multifocal lesions, and lesion size ≥2 cm were independent risk factors for lymph node metastasis in CN0 patients（P<0.05）, while diabetes was an independent protective factor for lymph node metastasis（P<0.05）.Age<55 years, capsular invasion, and multifocal lesions were independent risk factors for the presence of ≥3 lymph nodes with metastasis （P<0.05）. Conclusion:In CN0 stage PTC patients, special attention should be given to the possibility of lymph node metastasis when they are male, aged <55 years, have multifocal lesions, or have lesion size >2 cm.

Risk factors analysis of lateral cervical lymph node metastasis in papillary thyroid carcinoma: a retrospective study of 830 patients

OBJECTIVE

The aim of this study is to investigate the risk factors for lateral cervical lymph node metastasis in papillary thyroid carcinoma (PTC).

METHODS

Clinicopathological data (age, gender, Hashimoto's thyroiditis, preoperative circulating tumor cells (CTCs), multifocal, maximum lesion diameter, invaded capsule, T stage, and lymph node metastasis) of 830 PTC patients diagnosed and treated in Meizhou People's Hospital from June 2021 to April 2023 were collected. The related factors of lateral cervical lymph node metastasis were analyzed.

RESULTS

There were 334 (40.2%), and 103 (12.4%) PTC patients with central lymph node metastasis, and lateral cervical lymph node metastasis, respectively. Compared with patients without lateral cervical lymph node metastasis, PTC patients with lateral cervical lymph node metastasis had a higher proportion of multifocal, maximum lesion diameter > 1 cm, invaded capsule, T3-T4 stage. Regression logistic analysis showed that male (odds ratio (OR): 2.196, 95% confidence interval (CI): 1.279-3.769, p = 0.004), age < 55 years old (OR: 2.057, 95% CI: 1.062-3.988, p = 0.033), multifocal (OR: 2.759, 95% CI: 1.708-4.458, p < 0.001), maximum lesion diameter > 1 cm (OR: 5.408, 95% CI: 3.233-9.046, p < 0.001), T3-T4 stage (OR: 2.396, 95% CI: 1.241-4.626, p = 0.009), and invaded capsule (OR: 2.051, 95% CI: 1.208-3.480, p = 0.008) were associated with lateral cervical lymph node metastasis.

CONCLUSIONS

Male, age < 55 years old, multifocal, maximum lesion diameter > 1 cm, T3-T4 stage, and invaded capsule were independent risk factors for lateral cervical lymph node metastasis in PTC.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning

Background

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.

Materials and methods

LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.

Results

Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.

Conclusion

In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.