Correlations of lncRNAs with cervical lymph node metastasis and prognosis of papillary thyroid carcinoma

Multi-omics analysis unveils dysregulation of the tumor immune microenvironment and development of a machine learning-based multi-gene classifier for predicting lateral lymph node metastasis in papillary thyroid carcinoma

PURPOSE

Lateral lymph node metastasis (LNM) critically influences surgical decision-making in papillary thyroid carcinoma (PTC). However, the sensitivity of preoperative imageological examination in detecting LNM remains suboptimal, necessitating the development of more accurate diagnostic and predictive tools. This study aims to identify multi-omics biomarkers and construct a predictive model for LNM.

METHODS

We performed a comprehensive multi-omics analysis of 50 PTCs presenting with (LNM group) or without lateral lymph node metastases (LNN group) using whole exome sequencing and whole transcriptome sequencing.

RESULTS

Younger age, larger tumor size, and lymphovascular invasion were associated with increased risk of LNM, while invasive follicular subtype was associated with lower risk of LNM. Genomic landscape analysis identified 23 LNM group specific driver mutations and 15 protective variants in the LNN group. Transcriptome analysis identified 444 differentially expressed genes associated with LNM. Weighted gene co-expression network analysis revealed a module that correlated negatively with LNM, with key genes significantly enriched in Notch signaling pathway and Apelin signaling pathway. Notably, elevated neutrophils in tumor immune microenvironment was strongly associated with high LNM risk, suggesting neutrophils as potential early predictors of lateral lymph node metastasis in PTC. A machine learning-based multi-gene classifier was developed to predict LNM, achieving excellent performance with an area under the curve (AUC) of 0.98 in the training set and 0.892 in the test set.

CONCLUSIONS

This study provides novel insights into the molecular characteristics of PTC associated with lateral lymph node metastasis, highlighting tumor-infiltrating neutrophils as an independent LNM predictor. The multi-gene classifier developed in this study demonstrates promising clinical utility for improving the accuracy of LNM prediction and guiding personalized treatment strategies in PTC.

Development of a clinical-molecular prediction model for central lymph node metastasis in cN0 stage papillary thyroid microcarcinoma: a retrospective study

BACKGROUND

Identifying occult central lymph node metastasis (CLNM) is essential for guiding prophylactic lymph node dissection (PLND) in patients with cN0 stage papillary thyroid microcarcinoma (PTMC). This study aimed to identify molecular prognostic biomarkers associated with PTMC and develop a clinical-molecular prediction model for CLNM.

METHODS

Differentially expressed genes (DEGs) in PTMC were identified through bioinformatics analysis of the TCGA database. Prognostic DEGs were selected using Cox and LASSO regression analyses, and a risk-scoring model was constructed based on these genes. The prognostic value of the model was validated using Kaplan-Meier survival analysis and ROC curves. DEG expression levels were compared between patients with CLNM and those without (NCLNM). Clinical data and surgical specimens were collected from 404 patients with cN0 stage PTMC treated at the First Affiliated Hospital of Ningbo University in 2022. The cohort was randomly divided into a derivation cohort (n = 323) and a validation cohort (n = 81). DEG expression was quantified using RT-qPCR. Univariate and multivariate logistic regression analyses were conducted in the derivation cohort to identify predictors of CLNM and develop a predictive model. The model's performance was evaluated using the Hosmer-Lemeshow test, ROC curves, calibration curves, and decision curve analysis (DCA).

RESULTS

In the TCGA database, FN1, MT-1 F, and TFF3 were identified as prognostic biomarkers. Risk scores based on these genes achieved AUCs of 0.789 (5 years) and 0.674 (10 years) for predicting disease-free survival. Furthermore, FN1, MT-1 F, and TFF3 expression levels were significantly higher in the CLNM group compared to the NCLNM group. Among the 404 PTMC patients, the incidence of CLNM was 42.6% (n = 172). RT-qPCR analysis demonstrated significantly elevated expression of FN1 in both PTMC tissues compared to normal tissues and in the CLNM group relative to the NCLNM group, while MT-1 F and TFF3 exhibited markedly reduced expression levels. In the derivation cohort, FN1, MT-1 F, TFF3, tumor size ≥5 mm, calcification, multifocality, and extrathyroidal extension were independent predictors of CLNM. The prediction model based on these factors showed AUCs of 0.736 (derivation cohort) and 0.813 (validation cohort). Moreover, calibration curves, the Hosmer-Lemeshow test (χ² = 2.411, P = 0.966), and DCA confirmed the model's robust performance and clinical utility.

CONCLUSION

FN1, MT-1 F, and TFF3 are valuable prognostic biomarkers for PTMC. The clinical-molecular prediction model incorporating these genes provides a basis for personalized PLND decision-making in cN0 stage PTMC patients.

TRIAL REGISTRATION NUMBER

Not applicable.

Progress in long non-coding RNAs as prognostic factors of papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is generally recognized as a slow-growing tumor. However, a small subset of patients may still experience relapse or metastasis shortly after therapy, leading to a poor prognosis and raising concerns about excessive medical treatment. One major challenge lies in the inadequacy of effective biomarkers for accurate risk stratification. Long non-coding RNAs (lncRNAs), which are closely related to malignant characteristics and poor prognosis, play a significant role in the genesis and development of PTC through various pathways. The objective of this review is to provide a comprehensive summary of the biological functions of lncRNAs in PTC, identify prognosis-relevant lncRNAs, and explore their potential mechanisms in drug resistance to BRAF kinase inhibitors, tumor dedifferentiation, and lymph node metastasis. By doing so, this review aims to offer valuable references for both basic research and the prediction of PTC prognosis.

Correlations of m6A Methylation-Related lncRNAs with the Prognosis of Papillary Thyroid Carcinoma

Purpose

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer. Recurrence makes the prognosis for some patients with PTC worse. Increasing evidence have suggested that N6-methyladenosine (m6A) RNA methylation plays an important role in tumorigenesis. However, the significance of m6A-related lncRNAs in the malignant progression of PTC remains unknown. In this study, we explored the significance of M6A-related lncrnas in the malignant progression of PTC.

Patients and Methods

Transcriptome and clinical data of PTC were achieved and integrated from The Cancer Genome Atlas (TCGA). Firstly, a Spearman correlation analysis was performed to obtain m6A RNA methylation-associated lncRNAs. Next, We constructed a prognostic signature and assessed the accuracy of the signature by receiver operating characteristic (ROC) curve and Kaplan Meier survival analyses. Furthermore, functional enrichment analysis was performed on the high- and low-risk groups. Finally, we determined prognostic gene expression in clinical samples using quantitative reverse transcription polymerase chain reaction (RT-qPCR).

Results

We identified 56 differentially expressed lncRNAs associated with m6A RNA methylation. Univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses showed that the survival-related lncRNAs associated with m6A RNA methylation were detected, which showed superior calibration and discrimination. Moreover, the biological processes related to energy metabolism were significantly activated in the high-risk group. Finally, the co-expressed genes of lncRNAs in the risk model were significantly enriched in biological processes related to copper ion response. Finally, we validated the expression levels of three prognostic genes in clinical samples using RT-qPCR.

Conclusion

Our study revealed m6A RNA methylation-associated lncRNAs were significantly associated with disease-free survival in patients with papillary thyroid cancer, which would improve our understanding of the relationship between m6A RNA methylation-associated lncRNAs and PTC.

Matrix Metalloproteinase 9/microRNA-145 Ratio: Bridging Genomic and Immunological Variabilities in Thyroid Cancer

Matrix metalloproteinase 9 (MMP9) and microRNA-145 (miR-145) have emerged as essential biomarkers in thyroid cancer progression and metastasis. However, their combined evaluation and clinical utility as a unified prognostic marker across diverse thyroid cancer subgroups remain unexplored. We investigated the diagnostic and prognostic value of the MMP9/miR-145 ratio in thyroid cancer, hypothesizing it may overcome inter-patient heterogeneity and serve as a versatile biomarker regardless of genetic mutations or autoimmune status. MMP9 and miR-145 expressions were analyzed in 175 paired papillary thyroid cancer (PTC) and normal tissues. Plasma levels were assessed perioperatively and longitudinally over 12-18 months in 86 matched PTC patients. The associations with clinicopathological parameters and patient outcomes were evaluated. MMP9 was upregulated, and miR-145 downregulated in cancer tissues, with a median MMP9/miR-145 ratio 17.6-fold higher versus controls. The tissue ratio accurately diagnosed thyroid malignancy regardless of BRAF mutation or Hashimoto's thyroiditis status, overcoming genetic and autoimmune heterogeneity. A high preoperative circulating ratio predicted aggressive disease features, including lymph node metastasis, extrathyroidal extension, progression/relapse, and recurrence. Although the preoperative plasma ratio was elevated in patients with unfavorable outcomes, it had limited utility for post-surgical monitoring. In conclusion, the MMP9/miR-145 ratio is a promising biomarker in PTC that bridges genetic and immunological variabilities, enhancing preoperative diagnosis and prognostication across diverse patient subgroups. It accurately stratifies heterogenous cases by aggressiveness. The longitudinal trends indicate decreasing applicability for post-thyroidectomy surveillance. Further large-scale validation and protocol standardization can facilitate clinical translation of the MMP9/miR-145 ratio to guide personalized thyroid cancer management.

Clinical significance of matrix metalloproteinase-9 expression in papillary thyroid carcinoma: a meta-analysis

OBJECTIVE

The purpose of this study was to investigate the relationship between the expression of matrix metalloproteinase-9 (MMP-9) and pathological indexes in papillary thyroid carcinoma (PTC).

EVIDENCE OBTAINED

The database was searched in PubMed, Embase, CNKI, and Web of Science databases for relevant clinical trials. The odds ratio (OR) and 95% confidence interval (CI) show the effect of MMP-9 expression and age, tumour size, gender, lymph node metastasis (LNM), and TNM (tumour, lymph node, metastasis) stage. Statistical analysis of the data was performed using Stata 17.0.

EVIDENCE SYNTHESIS

A total of 1433 patients with PTC were included in this meta-analysis. MMP-9 expression was significantly correlated with LNM (OR = 3.92, 95% CI = 2.71-5.65, P = 0.000), tumour size (OR = 1.69, 95% CI = 1.13-2.52, P = 0.011), and TNM stage (OR = 2.95, 95% CI = 2.10-4.13, P = 0.000), but not with gender (OR = 0.90, 95% CI = 0.66-1.22, P = 0.487) and age (OR = 1.36, 95% CI = 0.93-1.98, P = 0.115).

CONCLUSIONS

Our meta-analysis showed that MMP-9 was significantly associated with LNM, tumour size, and TNM stage; therefore, MMP-9 may be a reliable prognostic biomarker for patients with PTC. However, more high-quality studies are needed to support these findings further.

The Role of MMP-9 and MMP-9 Inhibition in Different Types of Thyroid Carcinoma

Matrix metalloproteinase-9 (MMP-9), one of the most investigated and studied biomarkers of the MMPs family, is a zinc-dependent proteolytic metalloenzyme whose primary function is degrading the extracellular matrix (ECM). It has been proved that MMP-9 expression elevates in multiple pathological conditions, including thyroid carcinoma. MMP-9 has a detectable higher level in malignant or metastatic thyroid tumor tissues than in normal or benign tissues and acts as an additional marker to distinguish different tumor stages because of its close correlations with clinical features, such as lymph node metastasis, TNM stage, tumor size and so on. Natural and non-natural MMP-9 inhibitors suppress its expression, block the progression of diseases, and play a role in therapy consequently. MMP-9 inhibitory molecules also assist in treating thyroid tumors by suppressing the proliferation, invasion, migration, metastasis, viability, adhesion, motility, epithelial-mesenchymal transition (EMT), and other risk factors of different thyroid cancer cells. In a word, discovering and designing MMP-9 inhibitors provide great therapeutic effects and promising clinical values in various types of thyroid carcinoma.

Emerging role of oncogenic long noncoding RNA as cancer biomarkers

The view of long noncoding RNAs as nonfunctional "garbage" has been definitely outdated by the large body of evidence indicating this class of ncRNAs as "golden junk", especially in precision oncology. Indeed, in light of their oncogenic role and the higher expression in multiple cancer types compared with paired adjacent tissues, the clinical interest for lncRNAs as diagnostic and/or prognostic biomarkers has been rapidly increasing. The emergence of large-scale sequencing technologies, their subsequent diffusion even in small research and clinical centers, the technological advances for the detection of low-copy lncRNAs in body fluids, coupled to the huge reduction of operating costs, have nowadays made possible to rapidly and comprehensively profile them in multiple tumors and large cohorts. In this review, we first summarize some relevant data about the oncogenic role of well-studied lncRNAs having a clinical relevance. Then, we focus on the description of their potential use as diagnostic/prognostic biomarkers, including an updated overview about licensed patents or clinical trials on lncRNAs in oncology.

Super-Resolution Ultrasound Localization Microscopy of Microvascular Structure and Flow for Distinguishing Metastatic Lymph Nodes - An Initial Human Study

PURPOSE

Detecting and distinguishing metastatic lymph nodes (LNs) from those with benign lymphadenopathy are crucial for cancer diagnosis and prognosis but remain a clinical challenge. A recent advance in super-resolution ultrasound (SRUS) through localizing individual microbubbles has broken the diffraction limit and tracking enabled in vivo noninvasive imaging of vascular morphology and flow dynamics at a microscopic level. In this study we hypothesize that SRUS enables quantitative markers to distinguish metastatic LNs from benign ones in patients with lymphadenopathy.

MATERIALS AND METHODS

Clinical contrast-enhanced ultrasound image sequences of LNs from 6 patients with lymph node metastasis and 4 with benign lymphadenopathy were acquired and motion-corrected. These were then used to generate super-resolution microvascular images and super-resolved velocity maps. From these SRUS images, morphological and functional measures were obtained including micro-vessel density, fractal dimension, mean flow speed, and Local Flow Direction Irregularity (LFDI) measuring the variance in local flow direction. These measures were compared between pathologically proven reactive and metastasis LNs.

RESULTS

Our initial results indicate that the difference in the indicator of flow irregularity (LFDI) derived from the SRUS images is statistically significant between the two groups. The LFDI is 60% higher in metastatic LNs compared with reactive nodes.

CONCLUSION

This pilot study demonstrates the feasibility of super-resolution ultrasound for clinical imaging of lymph nodes and the potential of using the irregularity of local blood flow directions afforded by SRUS for the characterization of LNs.

Prevalence and predictor for malignancy of contralateral thyroid nodules in patients with unilateral PTMC: a systematic review and meta-analysis

BACKGROUND

The presence of clinically negative nodules on the contralateral lobe is common in patients with unilateral papillary thyroid microcarcinoma (PTMC). The appropriate operational strategies of contralateral thyroid nodules remain controversial. In this study, we analyzed clinical features that could be predictors for malignancy of contralateral thyroid nodules coexisting with diagnosed unilateral PTMC.

METHODS

The literatures published from January 2000 to December 2019 were searched in PubMed, Cochrane Library, Embase, Web of Science, CNKI, and Wan Fang database. Odds ratio (OR) with 95% CI was used to describe categorical variables. Heterogeneity among studies was examined by the Q test and I2 test; potential publication bias was detected by Harbord test and 'trim and fill' method.

RESULTS

In this meta-analysis, 2541 studies were searched and 8 studies were finally included. The results showed that the rate of carcinoma in contralateral nodules was 23% (OR = 0.23, 95% CI = 0.18-0.29). The pooled data indicated that contralateral malignancy was not associated with age, gender, primary lesion size, ipsilateral central lymph node metastasis and multifocality of contralateral lesion. The following variables have correlations with an increased risk of contralateral malignancy: multifocality of primary carcinomas (OR = 3.93, 95% CI = 2.70-5.73, P < 0.0001), capsular invasion (OR = 1.61, 95% CI = 1.10-2.36, P = 0.01), and Hashimoto's thyroiditis (OR = 1.57, 95% CI = 1.13-2.20, P = 0.008).

CONCLUSIONS

Based on our meta-analysis, the rate at which contralateral malignancies are preoperatively misdiagnosed as benign is 23%. The risk factors for contralateral malignancy in unilateral PTMC patients with contralateral clinical negative nodules include multifocality of primary carcinomas, capsular invasion, and Hashimoto's thyroiditis.

Regulatory role of long non-coding RNA UCA1 in signaling pathways and its clinical applications

Long non-coding RNA metastasis-associated urothelial carcinoma associated 1 (UCA1) plays a pivotal role in various human diseases. Its gene expression is regulated by several factors, including transcription factors, chromatin remodeling and epigenetic modification. UCA1 is involved in the regulation of the PI3K/AKT, Wnt/β-catenin, MAPK, NF-κB and JAK/STAT signaling pathways, affecting a series of cellular biological functions, such as cell proliferation, apoptosis, migration, invasion and tumor drug resistance. Furthermore, UCA1 is used as a novel potential biomarker for disease diagnosis and prognosis, as well as a target for clinical gene therapy. The present review systematically summarizes and elucidates the mechanisms of upstream transcriptional regulation of UCA1, the regulatory role of UCA1 in multiple signaling pathways in the occurrence and development of several diseases, and its potential applications in clinical treatment.

Prediction of Lymph-Node Metastasis in Cancers Using Differentially Expressed mRNA and Non-coding RNA Signatures

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96-92.19%), 81.97% (70.83-95.24%), and 80.78% (69.61-90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

Comprehensive analysis of ceRNA network related to lincRNA in glioblastoma and prediction of clinical prognosis

BACKGROUND

Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM).

METHODS

We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model.

RESULTS

A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM.

CONCLUSION

We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

Synergism of Proneurogenic miRNAs Provides a More Effective Strategy to Target Glioma Stem Cells

Simple Summary

miRNAs function as critical regulators of gene expression and have been defined as contributors of cancer phenotypes by acting as oncogenes or tumor suppressors. Based on these findings, miRNA-based therapies have been explored in the treatment of many different malignancies. The use of single miRNAs has faced some challenges and showed limited success. miRNAs cooperate to regulate distinct biological processes and pathways and, therefore, combination of related miRNAs could amplify the repression of oncogenic factors and the effect on cancer relevant pathways. We established that the combination of tumor suppressor miRNAs miR-124, miR-128, and miR-137 is much more effective than single miRNAs in disrupting proliferation and survival of glioma stem cells and neuroblastoma lines and promoting differentiation and response to radiation. Subsequent genomic analyses showed that other combinations of tumor suppressor miRNAs could be equally effective, and its use could provide new routes to target in special cancer-initiating cell populations.

Abstract

Tumor suppressor microRNAs (miRNAs) have been explored as agents to target cancer stem cells. Most strategies use a single miRNA mimic and present many disadvantages, such as the amount of reagent required and the diluted effect on target genes. miRNAs work in a cooperative fashion to regulate distinct biological processes and pathways. Therefore, we propose that miRNA combinations could provide more efficient ways to target cancer stem cells. We have previously shown that miR-124, miR-128, and miR-137 function synergistically to regulate neurogenesis. We used a combination of these three miRNAs to treat glioma stem cells and showed that this treatment was much more effective than single miRNAs in disrupting cell proliferation and survival and promoting differentiation and response to radiation. Transcriptomic analyses indicated that transcription regulation, angiogenesis, metabolism, and neuronal differentiation are among the main biological processes affected by transfection of this miRNA combination. In conclusion, we demonstrated the value of using combinations of neurogenic miRNAs to disrupt cancer phenotypes and glioma stem cell growth. The synergistic effect of these three miRNA amplified the repression of oncogenic factors and the effect on cancer relevant pathways. Future therapeutic approaches would benefit from utilizing miRNA combinations, especially when targeting cancer-initiating cell populations.

UCA1 long non-coding RNA: An update on its roles in malignant behavior of cancers

The lncRNA urothelial carcinoma-associated 1 (UCA1) is a 1.4 kb long transcript which has been firstly recognized in human bladder cancer cell line. Subsequent studies revealed its over-expression in a wide array of human cancer cell lines and patients' samples. In addition to conferring malignant phenotype to cells, it enhances resistance to conventional anti-cancer drugs. Moreover, transcript levels of this lncRNA have been regarded as diagnostic markers in several cancer types including gastric, bladder and liver cancers. The underlying mechanism of its participation in carcinogenesis has been identified in some cancer types. Sponging tumor suppressor miRNAs, interacting with cancer-promoting signaling pathways and enhancing cell cycle progression are among these mechanisms. Although few studies have shown anti-carcinogenic properties for this lncRNA, the bulk of evidence supports its oncogenic roles. In the current study, we have reviewed the current literature on the role of UCA1 in the carcinogenic process based on the results of in vitro studies, investigations in animal models and assessment of UCA1 expression in clinical samples.