Single-Cell Transcriptome Analysis Reveals Inter-Tumor Heterogeneity in Bilateral Papillary Thyroid Carcinoma

Increased Thyroid DPP4 Expression Is Associated With Inflammatory Process in Patients With Hashimoto Thyroiditis

CONTEXT

Dipeptidyl peptidase-4 (DPP4) is originally described as a surface protein in lymphocytes. Lymphocyte infiltration and subsequent destruction of thyroid tissue have been considered as the central pathological mechanism in Hashimoto thyroiditis (HT).

OBJECTIVE

The present study aimed to investigate DPP4 expression in peripheral blood and thyroid tissue in HT patients, and explore the role of DPP4 in the pathophysiological process of HT.

METHODS

This case-control study recruited 40 drug-naive HT patients and 81 control individuals. Peripheral blood and thyroid specimens were collected for assessing the expression and activity of DPP4. Moreover, single-cell RNA sequencing (scRNA-seq) analysis of 6 "para-tumor tissues" samples from scRNA-seq data set GSE184362 and in vitro cell experiments were also conducted.

RESULTS

The HT patients had similar DPP4 serum concentration and activity as the controls. However, the expression and activity of DPP4 was significantly increased in the thyroid of the HT group than in the control group. The scRNA-seq analysis showed that DPP4 expression was significantly increased in the HT group, and mainly expressed in T cells. Further in vitro studies showed that inhibition of lymphocyte DPP4 activity with sitagliptin downregulated the production of inflammatory factors in co-cultured thyroid cells.

CONCLUSION

DPP4 expression was significantly increased in the thyroid of the HT group compared with the control group, and was mainly localized in the lymphocytes. Inhibition of lymphocyte DPP4 activity reduced the production of inflammatory factors in co-cultured thyroid cells. Therefore, inhibition of DPP4 may have a beneficial effect by alleviating inflammatory reactions in HT patients.

TIM3 activates the ERK1/2 pathway to promote invasion and migration of thyroid tumors

BACKGROUND

This study aims to study the possible action mechanism of T-cell immunoglobulin and mucin domain 3 (TIM3) on the migratory and invasive abilities of thyroid carcinoma (TC) cells.

METHODS

GSE104005 and GSE138198 datasets were downloaded from the GEO database for identifying differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) analysis were performed on the common DEGs in GSE104005 and GSE138198 datasets. Subsequently, in order to understand the effect of a common DEG (TIM3) on TC cells, we performed in vitro experiments using FRO cells. The migratory and invasive abilities of FRO cells were detected by wound scratch assay and Transwell assay. Proteins expression levels of the phosphorylated (p)-extracellular signal-regulated kinase (ERK)1/2, matrix metalloproteinase-2 (MMP-2) and MMP-9 were determined via Western blotting after ERK1/2 inhibition in TIM3-NC group and TIM3-mimic group.

RESULTS

316 common DEGs were identified in GSE104005 and GSE138198 datasets. These DEGs were involved in the biological process of ERK1 and ERK2 cascade. TIM3 was significantly up-regulated in TC. In vitro cell experiments showed that TIM3 could promote migration and invasion of TC cells. Moreover, TIM3 may affect the migration, invasive abilities of TC cells by activating the ERK1/2 pathway.

CONCLUSION

The above results indicate that TIM3 may affect the migratory and invasive of TC cells by activating the ERK1/2 pathway.

Research progress of immunotherapy against anaplastic thyroid cancer

Anaplastic thyroid cancer (ATC) is the most aggressive type of thyroid cancer. While ATC is rare, its mortality is high. Standard treatments, such as surgery, radiotherapy, and chemotherapy, have demonstrated limited efficacy in managing ATC. However, the advent of immunotherapy has significantly improved the prognosis for patients with ATC. Immunotherapy effectively targets and eliminates tumor cells by using the power of the body's immune cells. The neoantigen is an atypical protein generated by somatic mutation, is exclusively observed in neoplastic cells, and is devoid of central tolerance. Neoantigens exhibit enhanced specificity towards tumor cells and display robust immunogenic properties. Currently, neoantigen therapy is primarily applied in immune checkpoint inhibitors and cellular immunotherapy, encompassing adoptive immunotherapy and tumor vaccines. This study discusses the mechanism, tumor microenvironment, clinical trials, adverse events, limitations and future directions associated with ATC immunotherapy.

Clinical value of ultrasound parameters PI, TTP, and MTT in assessing cervical lymph node metastasis of papillary thyroid carcinoma

OBJECTIVE

To determine the clinical value of ultrasound in assessing cervical lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC).

METHODS

The medical records of 179 PTC patients treated in Shandong Provincial Qianfoshan Hospital between March 2016 and March 2019 were collected. The patients were assigned to a transfer group (54 cases) and a non-transfer group (125 cases) according to their pathologic results. The ultrasound parameters (peak intensity (PI), time to peak (TTP), and mean transit time (MTT)) of the two groups were compared. Then, multivariate logistic regression was used to analysis the results, and receiver operating characteristic (ROC) curves were plotted to evaluate the value of risk factors in predicting CLNM.

RESULTS

The transfer group showed notably lower PI, TTP and MTT than the non-transfer group (P<0.001), and focus diameter, microcalcification, multiple foci, PI, TTP, and MTT were identified as independent risk factors for LNM in patients (P<0.05). According to the ROC curve, the areas under the curves (AUCs) of microcalcification, multiple foci, and PI were all smaller than 0.7; the AUCs of focus diameter and MTT were smaller than 0.8, and the AUC of TTP was 0.855.

CONCLUSION

PI, TTP, and MTT all decrease in PTC patients with CLNM, and TTP has a strong predictor for CLNM in them, with an AUC of 0.855.

TIPARP as a prognostic biomarker and potential immunotherapeutic target in male papillary thyroid carcinoma

BACKGROUND

Male patients with papillary thyroid carcinoma (PTC) tend to have poorer prognosis compared to females, partially attributable to a higher rate of lymph node metastasis (LNM). Developing a precise predictive model for LNM occurrence in male PTC patients is imperative. While preliminary predictive models exist, there is room to improve accuracy. Further research is needed to create optimized prognostic models specific to LNM prediction in male PTC cases.

METHODS

We conducted a comprehensive search of publicly available microarray datasets to identify candidate genes continuously upregulated or downregulated during PTC progression in male patients only. Univariate Cox analysis and lasso regression were utilized to construct an 11-gene signature predictive of LNM. TIPARP emerged as a key candidate gene, which we validated at the protein level using immunohistochemical staining. A prognostic nomogram incorporating the signature and clinical factors was developed based on the TCGA cohort.

RESULTS

The 11-gene signature demonstrated good discriminative performance for LNM prediction in training and validation datasets. High TIPARP expression associated with advanced stage, high T stage, and presence of LNM. A prognostic nomogram integrating the signature and clinical variables reliably stratified male PTC patients into high and low recurrence risk groups.

CONCLUSIONS

We identified a robust 11-gene signature and prognostic nomogram for predicting LNM occurrence in male PTC patients. We propose TIPARP as a potential contributor to inferior outcomes in males, warranting further exploration as a prognostic biomarker and immunotherapeutic target. Our study provides insights into the molecular basis for gender disparities in PTC.

Development and Validation of Potential Molecular Subtypes and Signatures of Thyroid Carcinoma Based on Aging-related Gene Analysis

BACKGROUND/AIM

Thyroid carcinoma (THCA) is a cancer of the endocrine system that most commonly affects women. Aging-associated genes play a critical role in various cancers. Therefore, we aimed to gain insight into the molecular subtypes of thyroid cancer and whether senescence-related genes can predict the overall prognosis of THCA patients.

MATERIALS AND METHODS

Thyroid carcinoma (THCA) transcriptome-related expression profiles were obtained from The Cancer Genome Atlas (TCGA) database. These profiles were randomly divided into training and validation subsets at a ratio of 1:1. Unsupervised clustering algorithms were used to compare differences between the two subtypes; prognosis-related senescence genes were used to further construct our prognostic models by univariate and multivariate Cox analyses and construct a nomogram to predict the 1-, 3-, and 5-year overall survival probability of THCA patients. In addition, we performed gene set enrichment analysis (GSEA) to predict the immune microenvironment and somatic mutations between the different risk groups. Finally, real-time PCR was used to verify the expression levels of key model genes.

RESULTS

The 'ConsensusClusterPlus' R package was used to cluster thyroid cancer into two categories (Cluster1 and Cluster2) on the basis of 46 differentially expressed aging-related genes (DE-ARGs); patients in Cluster1 demonstrated a better prognosis than those in Cluster2. Cox analysis was used to screen six prognosis-related DE-ARGs. Finally, our real-time PCR results confirmed our hypothesis.

CONCLUSION

Differences exist between the two subtypes of thyroid cancer that help guide treatment decisions. The six DE-ARG genes have a high predictive value for risk stratifying THCA patients.

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.

A novel prognostic signature and immune microenvironment characteristics associated with disulfidptosis in papillary thyroid carcinoma based on single-cell RNA sequencing

Background: Disulfidptosis is a newly discovered form of regulated cell death. The research on disulfidptosis and tumor progression remains unclear. Our research aims to explore the relationship between disulfidptosis-related genes (DRGs) and the clinical outcomes of papillary thyroid carcinoma (PTC), and its interaction on the tumor microenvironment. Methods: The single-cell RNA seq data of PTC was collected from GEO dataset GSE191288. We illustrated the expression patterns of disulfidptosis-related genes in different cellular components in thyroid cancer. LASSO analyses were performed to construct a disulfidptosis associated risk model in TCGA-THCA database. GO and KEGG analyses were used for functional analyses. CIBERSORT and ESTIMATE algorithm helped with the immune infiltration estimation. qRT‒PCR and flow cytometry was performed to validate the hub gene expression and immune infiltration in clinical samples. Results: We clustered PTC scRNA seq data into 8 annotated cell types. With further DRGs based scoring analyses, we found endothelial cells exhibited the most relationship with disulfidptosis. A 4-gene risk model was established based on the expression pattern of DRGs related endothelial cell subset. The risk model showed good independent prognostic value in both training and validation dataset. Functional enrichment and genomic feature analysis exhibited the significant correlation between tumor immune infiltration and the signature. The results of flow cytometry and immune infiltration estimation showed the higher risk scores was related to immuno-suppressive tumor microenvironment in PTC. Conclusion: Our study exhibited the role of disulfidptosis based signature in the regulation of tumor immune microenvironment and the survival of PTC patients. A 4-gene prognostic signature (including SNAI1, STC1, PKHD1L1 and ANKRD37) was built on the basis of disulfidptosis related endothelial cells. The significance of clinical outcome and immune infiltration pattern was validated robustly.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

New Insights into Immune Cells and Immunotherapy for Thyroid Cancer

Thyroid cancer (TC) is the most common endocrine malignancy worldwide, and the incidence of TC has gradually increased in recent decades. Differentiated thyroid cancer (DTC) is the most common subtype and has a good prognosis. However, advanced DTC patients with recurrence, metastasis and iodine refractoriness, as well as more aggressive subtypes such as poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC), still pose a great challenge for clinical management. Therefore, it is necessary to continue to explore the inherent molecular heterogeneity of different TC subtypes and the global landscape of the tumor immune microenvironment (TIME) to find new potential therapeutic targets. Immunotherapy is a promising therapeutic strategy that can be used alone or in combination with drugs targeting tumor-driven genes. This article focuses on the genomic characteristics, tumor-associated immune cell infiltration and immune checkpoint expression of different subtypes of TC patients to provide guidance for immunotherapy.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME). Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME. Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties. Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer

Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.

Reveal the potential molecular mechanism of circRNA regulating immune-related mRNA through sponge miRNA in the occurrence and immune regulation of papillary thyroid cancer

BACKGROUND

Papillary thyroid cancer (PTC) is the most common endocrine malignant tumour. The purpose of this study was to explore the potential molecular mechanism of circRNA regulating immune-related mRNA through sponge miRNA in the occurrence and immune regulation of PTC.

METHODS

All data were downloaded from public databases, such as GEO, Immport and TCGA. Differentially expressed (DE) mRNAs (DEmRNAs), DEmiRNAs and DEcircRNAs were identified using metaMA and limma packages. Subsequently, immune-related DEmRNAs were screened, and circRNA-miRNA-mRNA (ceRNA) regulatory network was constructed. In addition, functional annotation, protein-protein interaction (PPI) network construction, immune cell infiltration analysis and Pearson correlation analysis were performed. Finally, qRT-PCR validation and cell experiments were also performed.

RESULTS

In total, 2962 DEmRNAs, 78 DEmiRNAs and 51 DEcircRNAs were obtained. Subsequently, 195 immune-related DEmRNAs were obtained based on Immport database. Cytokine-cytokine receptor interaction was the only signalling pathway obtained in KEGG analysis. Then, 8 hub immune-related DEmRNAs were identified based on PPI network and CytoHubba plug-in. Subsequently, ceRNA sub-network containing hub immune-related DEmRNAs was extracted from ceRNA regulatory network. In ceRNA sub-network, hsa_circ_0082182-hsa-miR-18b-5p-FGF1/PDGFC, hsa_circ_0016404-hsa-miR-1275-FGF1/CTSB/IL13RA1, hsa_circ_0070100-hsa-miR-27a-3p/hsa-miR-27b-3p-TGFBR3, hsa_circ_0060055/hsa_circ_0038718-hsa-miR-150-3p-CXCL14, hsa_circ_0030427/hsa_circ_0002917-hsa-miR-22-3p-BMP7 and hsa_circ_0030427/hsa_circ_0002917-hsa-miR-125a-5p-LIFR axes were identified. Moreover, FGF1, PDGFC, CTSB, IL13RA1, TGFBR3, CXCL14, BMP7, LIFR, hsa-miR-125a-5p, hsa-miR-1275, hsa-miR-150-3p, hsa-miR-18b-5p and hsa-miR-27b-3p were also found to have good diagnostic accuracy and may be potential novel diagnostic markers for PTC. XCell analysis showed that the levels of immune cell infiltration (including Tregs, HSC, DC and Monocytes) were significantly different between the PTC and the control groups. Knockdown of the expression of hsa_circ_0082182 significantly inhibits the activity, proliferation, migration and invasion of TPC-1 cells.

CONCLUSION

Several circRNA-miRNA-mRNA axes identified in this study may be related to the occurrence, progression and survival of PTC. This lays a theoretical foundation for further understanding the molecular mechanism of PTC, and also contributes to clinical management and research.

The Immune Landscape of Papillary Thyroid Cancer in the Context of Autoimmune Thyroiditis

Simple Summary

The association between papillary thyroid cancer and Hashimoto’s thyroiditis went through a long-standing human debate recently elucidated by the establishment of a novel mouse model. Papillary thyroid carcinoma is an excellent model for studying the tumor immune microenvironment because it is naturally accompanied by immune cells, making it a good candidate for the treatment with immune checkpoint inhibitors.

Abstract

Papillary thyroid cancer (PTC) often co-occurs with Hashimoto’s thyroiditis, an association that has long been reported in clinical studies, remaining controversial. Experimental evidence has recently shown that pre-existing thyroiditis has a beneficial effect on PTC growth and progression by a distinctive expansion of effector memory CD8 T cells. Although the link between inflammation and PTC might involve different components of the immune system, a deep characterization of them which includes T cells, B cells and tertiary lymphoid structures, Mye-loid cells, Neutrophils, NK cells and dendritic cells will be desirable. The present review article considers the role of the adaptive and innate immune response surrounding PTC in the context of Hashimoto’s thyroiditis. This review will focus on the current knowledge by in vivo and in vitro studies specifically performed on animals’ models; thyroid cancer cells and human samples including (i) the dual role of tumor-infiltrating lymphocytes; (ii) the emerging role of B cells and tertiary lymphoid structures; (iii) the role of myeloid cells, dendritic cells, and natural killer cells; (iv) the current knowledge of the molecular biomarkers implicated in the complex link between thyroiditis and PTC and the potential implication of cancer immunotherapy in PTC patients in the context of thyroiditis.