The specific regulatory networks between different variants in papillary thyroid carcinoma: A transcriptomics study

doi:10.21203/rs.3.rs-3126566/v1

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The specific regulatory networks between different variants in papillary thyroid carcinoma: A transcriptomics study

https://doi.org/10.21203/rs.3.rs-3126566/v1

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Objective: To analyze the molecular pathogenesis or characteristics based on transcriptomics techniques to diagnose and treat papillary thyroid carcinoma (PTC) caused by mutations.

Methods: We conducted transcriptome sequencing to identify differentially expressed genes (DEGs) in PTC-derived cell lines TPC-1, BCPAP, IHH4, and CVPTC. Additionally, gene expression profiling was performed using microarray in GEO database to screen out representative dataset chips and analyze DEGs of PTC tissues in the clinic. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on DEGs were performed using DAVID database. We established the protein-protein interaction (PPI) network using STRING database and built the transcription factor (TF) regulation network based on module analysis for identifying master regulators along with gene modules.

Results: We identified 4,353 and 3,250 DEGs among cell line (CVPTC/BCPAP/IHH4 or TPC-1/BCPAP/IHH4) and normal thyroid cells independently. After screening GSE27155 dataset, we identified 1,075 DEGs among PTC tissue samples (classic/RET_PTC mutation/high cellular variant) and normal tissue samples. The DEGs in the three groups were enriched in different pathways like cell proliferation, signal dysregulation, immune dysregulation, angiogenesis and cancer pathways. By constructing the PPI network and performing the module analysis, we determined top 10 key genes in the three groups separately. The results showed that 22 and 61 important TFs were significantly enriched in PPI of the two cell groups respectively and only one important TF was significantly enriched in PPI of PTC tissues.

Conclusion: We identified the transcriptomic features of PTC cell lines and clinical PTC tissues associated with invasive behavior.

papillary thyroid carcinoma

variation

transcriptomics

specific targets

regulatory networks

transcription factors

Papillary thyroid carcinoma (PTC) is the most prevalent thyroid cancer (TC) and is responsible for about 95% of the cases of TC. It is an inert and heterogeneous tumor ^{1, 2}. Although PTC usually shows a high level of differentiation and low local recurrence, invasion, and metastasis rates, a small proportion of tumors exhibit intratumor heterogeneity and high aggressiveness ³. Several histological PTC variants have been identified in the clinic, including hypercellular variant, shoe stud variant, solid variant, columnar cell variant, diffuse sclerotic variant, etc. Although studies have analyzed the molecular characteristics of differentiated thyroid cancer ⁴, the underlying mechanism by which differentiated thyroid cancer transforms into different variants has not been fully elucidated. Some studies have shown that multiple mutated loci in tumor suppressor genes or proto-oncogenes are related to the occurrence of TC ⁵. However, these studies are limited to genome-level variant analysis with relatively little information on the transcriptome and proteome.

In this study, we selected several variants of PTC cell lines, including ^N/ACVPTC (CVPTC), ^{CCDC6–RET fusi}°ⁿTPC-1 (TPC-1), ^BRAFp.V600EBCPAP (BCPAP), ^{BRAF p.V600E}IHH4 (IHH4), and ^N/ANthy-ori 3–1 ⁶. We then selected gene expression data in the GSE27155 dataset, including 18 classic PTC, 9 RET/PTC mutation PTC, 10 tall-cell variant PTC, and four healthy thyroid tissues. Next, the comparison results were intersected to identify differentially expressed genes (DEGs) among the cell lines and tissues. The PTC cells/tissues were compared to the healthy thyroid cells/tissues to identify PTC-specific malignant markers. We also used gene set enrichment analysis (GSEA) for annotating biological activities enriched by PTC-specific DEGs. Then, we constructed a transcription factor (TF) regulatory network for identifying heterogeneities in the master regulators of PTC cell lines and tissues.

2.1 Identification of DEGs and annotation in PTC

A flowchart of this study is presented in Fig. 1. In total, 4,353 DEGs were screened in the PTC cell lines (CVPTC, BCPAP, and IHH4) relative to healthy thyroid cells (Fig. 2A). Additionally, 3,250 DEGs were screened in other PTC cells (TPC-1, BCPAP, and IHH4) relative to healthy thyroid cells (Fig. 2B). A total of 1,075 DEGs were screened in the PTC tissues (classic, RET/PTC mutation, and high cell variant) compared to adjacent normal tissues (Fig. 2C). However, there were no common DEGs between thyroid papillary carcinoma cell lines and thyroid papillary carcinoma tissues (Fig. 2D).

2.2 GSEA of PTC-specific DEGs

To determine the effects of PTC-specific DEGs, GO and KEGG analyses were conducted using the PTC cell lines and tissues for interpreting gene levels. The results showed 168 significantly enriched gene functions and 17 KEGG pathways compared to the PTC-derived cell lines (CVPTC, BCPAP, and IHH4) and 96 significantly enriched gene functions and eight KEGG pathways compared to other PTC-derived cell lines (TPC-1, BCPAP, and IHH4) (P < 0.0001). We also found 113 significantly enriched gene functions and 6 KEGG pathways compared to PTC tissues (classic, RET/PTC mutation, and high cell variant) (P < 0.0001).

The cell line genes were enriched mainly in extracellular matrix tissue, cell adhesion, angiogenesis, plasma membrane, and axon guidance. The genes in the PTC tissues (classic, RET/PTC mutation, and high cell variant) were mainly enriched in response to drugs, response to exogenous stimuli, signal transduction, angiogenesis, apoptosis, actin cytoskeleton, response to cytokines, and positive regulation of apoptosis. The cell component enrichment analysis indicated that two groups of PTC cell lines mainly correlated with the plasma membrane, extracellular matrix, cell surface, components of the plasma membrane, basement membrane, extracellular region, and receptor complex. The genes in the PTC tissues (classic, RET/PTC mutation, and high cell variant) were mainly enriched in extracellular bodies, cytoplasm, membrane, adhesion spots, cell surface, plasma membrane, glutamatergic synapses, cytoskeleton, and receptor complexes. Regarding molecular function, two groups of PTC cell line genes were mainly associated with protein binding, integrin binding, receptor binding, and transcription factor activity (Fig. 3A-B). The genes in the PTC tissues (classic, RET/PTC mutation, and high cell variant) were mostly associated with protein binding, protein homodimerization activity, actin binding, actin filament binding, cadherin binding, ligand-activated sequence-specific DNA binding, RNA polymerase II transcription factor activity, protein domain specific binding, and transcription factor binding (Fig. 3C).

The pathway analysis showed that the genes in the PTC-derived cell lines (CVPTC, BCPAP, and IHH4) were related to focal adhesion, the ECM receptor interaction signaling pathway, the MAPK pathway, the cancer pathway, the PI3K Akt pathway, the calcium pathway, hypertrophic cardiomyopathy, Cushing's syndrome, cell adhesion molecules, bladder cancer, human papillomavirus infection, amoebiasis, and the gastric acid secretion signaling pathway (Fig. 4A). The genes in the PTC-derived cell lines (TPC-1, BCPAP, and IHH4) were significantly enriched in pathways of cancer, dilated cardiomyopathy, axonal guidance, the MAPK signaling pathway, focal adhesion, hypertrophic cardiomyopathy, the Rap1 pathway, the cell adhesion molecule pathway, and ECM receptor interaction (Fig. 4B). The genes in the PTC tissues (classic, RET/PTC mutation, and high cell variant) were closely associated with tight junction, proteoglycans in cancer, the p53 pathway, transcriptional disorders in cancer, apoptosis, and the signaling pathways in cancer (Fig. 4C).

2.3 Construction of the PPI network

For establishing interactions among the selected DEGs in the PTC, the STRING database was used for constructing the PPI network. Then, the 10 most significant hub genes were selected in different groups in the whole network separately using the plug-in cytoHubba by the MCC method in Cytoscape (Figs. 5A, 5C, and 5E). They were TNF, IL6, EGFA, MMP9, EGF, TGFB1, CXCR4, PECAM1, IL1B, and FN1 in the PTC-derived cell lines (CVPTC, BCPAP, and IHH4). Also, VEGFA, EGFR, TNF, CXCL12, TGFB1, EGF, PECAM1, CXCR4, CD44, and SPP1 were the top 10 hub genes in the PTC-derived cell lines (TPC-1, BCPAP, and IHH4). The top 10 hub genes in the PTC tissues (classical PTC, RET/PTC mutated PTC, and high cell variant PTC) were JUN, VEGFA, CCND1, SRC, ESR1, HIF1A, BCL2L1, CD44, KIT, and FN1.

We also used the DEGs to perform a pathway analysis using the plug-in ClueGO in Cytoscape (Figs. 5B, 5D, and 5F). The significant vital MCC module showed functions, including proteoglycans in cancer, fluid shear stress, rheumatoid arthritis, AGE-RAGE pathway diabetes complications, leukocyte cross endothelial migration, atherosclerosis, and pathways in cancer in the PTC-derived cell lines. Additionally, the significant vital MCC module showed functions in PTC tissues, including proteoglycans in cancer, bladder cancer, breast cancer, Kaposi's sarcoma-associated herpesvirus infection, pathways in cancer, and mitotic phagocytosis.

2.4 Key transcription factors and network prediction

To elucidate the possible regulatory factors that target the genes in the critical MCC modules of our as-constructed PPI network, the plug-in iRegulon in Cytoscape was used for predicting targets and regulatory factors (Table 1). We identified 22 TFs to be the master regulators in the PTC-derived cell lines (CVPTC, BCPAP, and IHH4), including STAT6, CEBPB, EBF1, MYB, PDX1, CEBPA, CEBPE, CEBPG, CEBPD, KIAA0907, SPIC, SPIB, SPI1, EHF, ELF5, NFATC3, ELF3, ETV6, DAB2, STAT3, and AVEN (NES > 5.0) that targeted 30 genes. In total, 66 TFs were closely associated with the protein networks of the PTC-derived cell lines (TPC-1, BCPAP, and IHH4) with 48 targets. The top 22 transcription factors included TFAP2C, GTF2IRD1, PLAG1, AVEN, PARP1, E2F1, EGR1, EGR2, EGR3, EGR4, FOXD3, FOXC2, FOXC1, FOXD2, HLTF, FOXA1, FOXA2, FOXB1, FOXO4, FOXA3, SRF, and FOXL1 (NES > 5.0). Only one TF was estimated, and TCF12 was the most significant regulatory factor targeting seven genes in the PTC tissues (classic, RET/PTC mutation, and high cell variant).

Table 1

The prediction results of the major transcription factors in PTC-derived cell lines and PTC tissues
NES	Transcription factors	Target genes
CVPTC/BCPAP/IHH4 vs. normal thyroid cell
6.9256	STAT6, CEBPB, EBF1, MYB, PDX1, CEBPA, CEBPE, CEBPG, CEBPD	FN1, VEGFA, PECAM1, IL6, CXCR4, MMP9
5.41786	KIAA0907	FN1, CXCR4, VEGFA, PECAM1, EGF
5.31388	SPIC, SPIB, SPI1, EHF, ELF5, NFATC3, ELF3, ETV6	MMP9, VEGFA, FN1
5.25496	DAB2	FN1, CXCR4, EGF, PECAM1, VEGFA, MMP9
5.25149	STAT3	VEGFA, FN1, MMP9
5.05046	AVEN	CXCR4, PECAM1, VEGFA, FN1, MMP9, IL1B, IL6, EGF
4.95688	EXOSC3	FN1, MMP9, VEGFA, CXCR4, IL1B, PECAM1, EGF
TPC-1/BCPAP/IHH4 vs. normal thyroid cell
6.93086	TFAP2C, GTF2IRD1	CXCR4, CD44, EGF
6.44082	PLAG1	EGFR, CXCR4, CXCL12
6.41632	AVEN	CXCR4, PECAM1, CD44
6.38202	PARP1	CXCR4, CD44, EGF
5.78907	E2F1	TGFB1, CXCR4
5.73027	EGR1, EGR2, EGR3, EGR4	CXCR4, CXCL12, TGFB1
5.54405	FOXD3, FOXC2, FOXC1, FOXD2, HLTF, FOXA1, FOXA2, FOXB1, FOXO4, FOXA3, SRF	TNF, CXCL12
5.51955		CXCL12, CXCR4, EGFR, TNF
5.46565		CD44, CXCR4
5.44114	FOXC2, FOXL1, FOXD3, FOXD2, FOXO3, FOXC1, FOXJ2, FOXO1, SRF, FOXK1	CXCL12, TNF
5.43134	SALL4, SALL3, SALL1, SALL2, ZNF236	TGFB1, CD44
5.34314	LTF, IRF1, IRF8, IRF7, IRF6, IRF2, IRF4, IRF3, IRF5, PRDM1, MYB	CXCR4, EGF, SPP1
5.25493		CXCL12, SPP1, EGFR, PECAM1, EGF
5.22553	PRDM1, IRF1, ZNF683	CXCL12, CD44, CXCR4
5.21083		CXCR4, CXCL12
5.08832	TCF3, HAND1, HAND2, NOBOX	CXCR4, EGFR
5.07852	NR3C1,NR2F2,NR2F1,HSF1,MAF,CHURC1,IKZF2,RFX1,SPZ1,OLIG2,PGR, ZFP42, YY1, MYB	CXCL12, EGFR
5.00991	FOXL1, FOXC2, FOXD2, FOXJ2, FOXF1, FOXJ1, FOXF2, FOXO1, FOXO4, FOXP3, FOXA1, HLTF, FOXO6, FOXJ3, FOXL2	TNF, CXCL12
classic /RET_PTC mutation /tall cell variant vs. normal thyroid tissue
5.97176	TCF12	BCL2L1, ESR1, CCND1, VEGFA, ESR1, BCL2L1, CCND1
4.80603	RBBP9	BCL2L1, ESR1, CCND1, ESR1, BCL2L1, CCND1
4.28996	CEBPD	VEGFA, FN1, BCL2L1
4.25049	FOXD2	CCND1, BCL2L1, VEGFA
4.16853	ESR1	ESR1, VEGFA, BCL2L1, CCND1

3.1 Cells and culture

Human BCPAP and IHH4 PTC-derived cells along with human normal thyroid cells (Nthy-ori3-1) were obtained from Genio Bio Ltd. The human TPC-1 cell line was obtained from Beijing Bena Biological Co., and a cell line of the classic papillary thyroid carcinoma cell (CVPTC) was maintained in our laboratory. All PTC cell lines were treated with DMEM (Hyclone, USA) containing 10% fetal bovine serum (FBS, Gibco; USA) and a complete culture medium consisting of 1% penicillin/streptomycin (PS, Gibco; USA). Then, the cells were incubated in an incubator (Thermo Scientific, USA) at 37°C and 5% CO₂ in a humidified environment. The Nthy-ori3–1 cells were then incubated in the RPMI-1640 medium (Hyclone, USA) containing 10% FBS. All cells were passaged under the same conditions to the seventh generation.

3.2 Transcriptome sequencing and differential gene screening of PTC cells

Transcriptome sequencing of PTC cells (CVPTC, TPC-1, BCPAP, IHH4, and Nthy-ori 3–1) was performed by Beijing Novozyme Technology Co. Three replicates were performed for each cell type. The cell lines were screened for differential genes, and two-by-two comparisons were performed for selecting DEGs based on the thresholds of P < 0.05 and |log₂(FC)| ≥ 1.

3.3 Microarray data of PTC tissues

We obtained the representative microarray dataset GSE27155 comprising 18 classic PTC, nine RET/PTC mutation PTC, 10 tall-cell variant PTC, and four healthy thyroid tissues from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). The dataset was analyzed using the GEO2R online tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/). The DEGs were selected based on the thresholds of P < 0.05 and | log₂(FC)| ≥ 1. The consistent DEGs among the three kinds of PTC compared to the non-carcinoma thyroid samples were considered to be PTC-specific genes.

3.4 Functional annotation and pathway analysis of the DEGs

The DAVID bioinformatics resource database (https://david.ncifcrf.gov/, 2021 updated version) was used for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of the DEGs in the PTC cells or tissues. The threshold of significant difference was P < 0.05.

3.5 Construction of the protein-protein interaction (PPI) network and module analysis

The search tool for the retrieval of interacting genes (STRING, http://string-db.org/) database is extensively used for retrieving and constructing PPI networks. We used the STRING database for constructing a PPI network based on DEGs and the Cytoscape software (Version 3.7.2) for data visualization. Then, we used Maximal Clique Centrality (MCC) to identify critical modules from the obtained PPI network. For diverse modules, pathway interaction networks were built using ClueGO.

3.6 Establishing a regulatory network and identifying master regulators

To identify the master regulators of PTC-specific modules, we used iRegulon, a plug-in in Cytoscape, for constructing the regulatory network. This plug-in was described by Janky et al. ⁷, and it depends on a characteristic ranking-and-recovery algorithm. To predict the TFs, default parameters were used. The TFs whose normalized enrichment score (NES) was over 5 were selected for establishing the regulatory network.

The accurate diagnosis and effective treatment of PTC with a good prognosis or a PTC variant with a poor prognosis are necessary. Many studies have investigated genomic or transcriptome changes in PTC cells with different mutation types using different techniques. However, studies on PTC in vivo and in vitro have led to different findings in different articles. In this study, one GEO database-derived dataset was used, and datasets from various cell lines were also obtained to jointly analyze heterogeneities in regulatory networks and important transcription factors among PTC cells and tissues.

The invasiveness of PTC is mediated by molecular evolution. Identifying PTC-specific DEGs can help to comprehensively analyze their characteristic changes and invasiveness. Analyzing these three comparison results together helped to identify genes with unique expression in PTC among the three kinds of variants/cell lines relative to healthy tissues/thyroid cells. As shown by GSEA, the cell cycle, cell proliferation and apoptosis, signal imbalance, and immune regulation were significantly correlated with PTC-derived cell lines. Gene functional annotation and module MCC analyses revealed its relationship with the cell cycle and cell proliferation. The genes associated with the cell cycle are highly expressed in the PTC cells ^{8, 9}. We built a TF regulatory network, where EHF, the ELF family genes, PARP1, E2F1, HLTF, AVEN, and the SALL family genes were identified as the possible master regulators of cell cycle and cell proliferation in PTC cells. We also constructed a TF regulatory network with the EGR family genes, the FOX family genes, the CEBP family genes, the SPI family genes, DAB2, the IRF family genes, and STAT as potential master regulators of angiogenesis, carcinogenesis, and immune regulation. The E2F1 pathway is essential for controlling the expression of genes in the G1/S phase ^{10, 11}. We also found that E2F1 is an important specific factor in PTC, which was similar to the findings of a study by Yang et al. ¹². We also identified E2F1 as a possible transcriptional target, which might be associated with an imbalance in the mechanism of the cell cycle in cancer cells. The TFs PARP1 and HLTF coordinate homologous recombination and regulate DNA damage in cells, while assisted E2F gene to promote and modulate the cell cycle ^{13, 14}. Aberrant levels of FOX family genes are associated with several cancers, including thyroid cancers ^15–17. The genes in the CEBP family and the SPI family are specific transcription factors of the lymphoid system and hematopoietic cells. They regulate lymphocyte development and control the self-renewal and differentiation of cells ^{18, 19}. The results of our analysis showed that the genes in the CEBP family and the SPI family are important transcription factors in thyroid cancer. However, their role in different types of PTC remains unclear. Most deaths from cancer occur due to metastasis to other organs, and cancer cell invasion might be a prerequisite for tumor development. Some studies have shown that EGR family genes are important transcription factors in cancer angiogenesis ²⁰. The SALL family genes regulate cell migration ²¹, which influences important nodes of cancer malignancy. We found that transcriptionally regulating EGR and SALL family genes is necessary for controlling cell migration and the development of malignant characteristics in PTC; thus, regulating these genes can activate specific potential targets of PTC. DAB2 is an oncoprotein that is associated with mutations in signaling proteins of the Ras-MAPK pathway ^{22, 23}. However, studies on DAB2 in PTC are limited, and the role of DAB2 in PTC needs further investigation.

Additionally, as demonstrated by PTC tissue variants of GSEA, cell proliferation, and apoptosis, the dysregulation of signaling transcription showed a close relationship with the PTC tissues. According to annotation and module MCC analyses, TCF12 is an important transcription factor in PTC. It not only helps to maintain the balance between stem cell differentiation and reconstitution ²⁴ but is also an important factor that leads to a poor prognosis of cancer ²⁵. Therefore, the poor prognosis, recurrence, and intratumor heterogeneity of PTC occasionally seen in clinical patients might be associated with the TCF12 transcription factor. However, the effect of TCF12 on PTC is not known and needs to be investigated. The selected master regulators along with the corresponding candidate targets can lead to complications in the cell cycle and promote tumor development gene regulatory networks. They are also associated with a poor treatment effect, easy recurrence, and rapid progression of PTC.

Overall, in this study, we comprehensively analyzed the malignant hallmarks and predicted the role of key genes and transcription factors in variants of PTC. Some TFs were identified as master regulators of the PTC-specific DEGs in this regulatory network. These TFs might provide greater insights into the mechanism of PTC. However, certain factors require further investigation, for example, the sample asymmetry in the dataset and the small size of the cell line samples in data analysis. Notwithstanding, this study can be in support of the point about important significance of PTC variants associated key regulators in future.

Acknowledgments

This work was supported by the [National Natural Science Foundation of China] under Grant [Number: 81860313] and Special key project of Kunming Medical Association [Number: 201801PH00061].

Conflicts of interest

The authors report there are no competing interests to declare.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article.

Author contributions

Gao-hong Zhu was in charge of study conception and design. Li-jun Xie was responsible for writing an article. Shi-ci Yang , Rui He ,Ting-ting Wang and Zhi-yu Yang was responsible for gathering and analyzing data . Shu Song and Jiao-jiao Feng was responsible for interpretation table or figure preparation. All authors reviewed this manuscript.

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The specific regulatory networks between different variants in papillary thyroid carcinoma: A transcriptomics study

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Abstract

Figures

1. Introduction

2. Results

2.1 Identification of DEGs and annotation in PTC

2.2 GSEA of PTC-specific DEGs

2.3 Construction of the PPI network

2.4 Key transcription factors and network prediction

3. Methods

3.1 Cells and culture

3.2 Transcriptome sequencing and differential gene screening of PTC cells

3.3 Microarray data of PTC tissues

3.4 Functional annotation and pathway analysis of the DEGs

3.5 Construction of the protein-protein interaction (PPI) network and module analysis

3.6 Establishing a regulatory network and identifying master regulators

4. Discussion

Declarations

References

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