RNA sequencing identifies crucial genes in papillary thyroid carcinoma (PTC) progression

Integrated transcriptome sequencing and weighted gene co-expression network analysis reveals key genes of papillary thyroid carcinomas

Objective

Papillary thyroid carcinoma (PTC) accounts for the majority of thyroid cancers and has a high recurrence rate. We aimed to screen key genes involved in PTC to provide novel insights into the mechanisms of PTC.

Methods

Seven microarray datasets of PTC were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) between PTC and normal samples were screened in the merged dataset. Then, protein-protein interaction (PPIs) functional modules analysis and weighted gene co-expression network analysis (WGCNA) were utilized to identify PTC-associated key genes. The identified key genes were then characterized from various aspects, including gene set enrichment analysis (GSEA) and the associations with immune infiltration, methylation levels and prognosis.

Results

A large numbers of DEGs were identified, and these DEGs are involved in several cancer pathways. Nine key genes (including down-regulated genes GNA14, AVPR1A, and WFS1, and up-regulated genes LAMB3, PLAU, MET, MFGE8, PRSS23, and SERPINA1) were identified. Patients in the AVPR1A and GNA14 high expression groups had better disease-free survival (DFS) than those in the low expression group. Key genes were mainly involved in P53 pathway, estrogen response, apoptosis, glycolysis, NOTCH signaling, epithelial mesenchymal transition, WNT_beta catenin signaling, and inflammatory response. The expression of key genes was associated with immune cell infiltration and corresponding methylation levels. The verification results of key gene proteins and mRNA expression levels using external validation datasets were consistent with our expectations, implying the involvements of key genes in PTC.

Conclusion

The key genes may serve as potential therapeutic targets for PTC. This study provides novel insights into the mechanisms underlying PTC development.

PIWIL1 Promotes Malignant Progression of Papillary Thyroid Carcinoma by Inducing EVA1A Expression

INTRODUCTION

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer. Previous studies have reported on the ectopic expression of P-element-induced wimpy testis ligand 1 (PIWIL1) in various human cancers, but its role in PTC progression has not been investigated.

METHODS

In this study, we measured the expression levels of PIWIL1 and Eva-1 homolog A (EVA1A) in PTC using qPCR and WB. We performed a viability assay to evaluate PTC cell proliferation and used flow cytometry to investigate apoptosis. Moreover, we conducted a Transwell invasion assay to quantify cell invasion and assessed PTC growth in vivo using xenograft tumor models.

RESULTS

Our findings showed PIWIL1 to be highly expressed in PTC and promote cell proliferation, cell cycle activity, and cell invasion, while suppressing apoptosis. Additionally, PIWIL1 accelerated tumor growth in PTC xenografts by modulating the EVA1A expression.

CONCLUSION

Our study suggests that PIWIL1 contributes to the progression of PTC through EVA1A signaling, indicating its potential role as a therapeutic target for PTC. These results provide valuable insights into PIWIL1 function and may lead to more effective treatments for PTC.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein-protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

Exploration of the Potential Biomarkers of Papillary Thyroid Cancer (PTC) Based on RT2 Profiler PCR Arrays and Bioinformatics Analysis

Background

Papillary thyroid carcinoma (PTC) has increased rapidly over recent years, and radiation, hormone effects, gene mutations, and others were viewed as closely related. However, the molecular mechanisms of PTC have not been cleared. Therefore, we intended to screen more accurate key genes and pathways of PTC by combining RT² profiler PCR arrays and bioinformatics methods in this study.

Materials and Methods

RT² profiler PCR arrays were firstly analyzed to identify differential expression genes (DEGs) in PTC. RT-qPCR were performed to verify the most significant differential expression genes. The TCGA database was used to further verify for expanded data. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was analyzed. To construct the protein–protein interaction (PPI) network, we used STRING and Cytoscape to make module analysis of these DEGs.

Results

Sixteen differentially expressed genes were presented in RT² profiler PCR arrays, including 13 down-regulated DEGs (DEGs) and three up-regulated DEGs (DEGs), while 13 stable DEGs were eventually verified. A total of 155 DEGs were presented in the TCGA database, including 82 up-regulated DEGs (DEGs) and 73 down-regulated DEGs (dDEGs). A total of 29 important genes were extracted after integrating these two results, GO and KEGG analyses were used to observe the possible mechanisms of action of these DEGs. The PPI network was constructed to observe hub genes. Prognostic analysis further demonstrated the involvement of these genes in the biological processes of PTC.

Conclusion

This study identified some potential molecular targets and signal pathways, which might help us raise our awareness of the mechanisms of PTC.

Clinicopathological value and underlying molecular mechanism of annexin A2 in 992 cases of thyroid carcinoma

BACKGROUND

Thyroid carcinoma (THCA) is one of the most frequent endocrine cancers and has increasing morbidity. Annexin A2 (ANXA2) has been found to be highly expressed in various cancers; however, its expression level and potential mechanism in THCA remain unknown. This study investigated the clinicopathological value and primary molecular machinery of ANXA2 in THCA.

MATERIAL AND METHODS

Public RNA-sequencing and microarray data were obtained and analyzed with ANXA2 expression in THCA and corresponding non-cancerous thyroid tissue. A Pearson correlation coefficient calculation was used for the acquisition of ANXA2 coexpressed genes, while edgR, limma, and Robust Rank Aggregation were employed for differentially expressed gene (DEG) in THCA. The probable mechanism of ANXA2 in THCA was predicted by gene ontology and pathway enrichment. A dual-luciferase reporter assay was employed to confirm the targeting relationships between ANXA2 and its predicted microRNA (miRNA).

RESULTS

Expression of ANXA2 was significantly upregulated in THCA tissues with a summarized standardized mean difference of 1.09 (P < 0.0001) based on 992 THCA cases and 589 cases of normal thyroid tissue. Expression of ANXA2 was related to pathologic stage. Subsequently, 1442 genes were obtained when overlapping 4542 ANXA2 coexpressed genes with 2248 DEGs in THCA; these genes were mostly enriched in pathways of extracellular matrix-receptor interaction, cell adhesion molecules, and complement and coagulation cascades. MiR-23b-3p was confirmed to target ANXA2 by dual-luciferase reporter assay.

CONCLUSIONS

Upregulated expression of ANXA2 may promote the malignant biological behavior of THCA by affecting the involving pathways or being targeted by miR-23b-3p.

Integrated bioinformatics analysis of the association between apolipoprotein E expression and patient prognosis in papillary thyroid carcinoma

The prognosis of most patients with papillary thyroid carcinoma (PTC) is excellent despite some cases of tumor progression or relapse. The present study was designed to reveal possible prognostic risk indicators for PTC. Differentially expressed genes (DEGs) extracted from 4 Gene Expression Omnibus (GEO) cohorts were subjected to functional enrichment analyses by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. A dataset from The Cancer Genome Atlas (TCGA) was obtained to filter and validate significant genes using cytoHubba, followed by analysis of their association with clinicopathological characteristics and prognosis. In total, 240 DEGs were identified after data preprocessing. These DEGs were enriched in ‘intracellular redox equilibrium’, ‘release of exosome’, ‘cell adhesion’, ‘regulation of extracellular matrix’, ‘collagen binding’ and ‘energy metabolism’ based on GO analysis which including cellular component, molecular function and biological process. KEGG pathway analysis revealed that the DEGs were enriched in thyroid hormone synthesis, pathways in cancer, focal adhesion, metabolic pathways, apoptosis, PPAR signaling pathway and PI3K/AKT signaling pathway. Using cytoHubba, the following hub genes were identified: Apolipoprotein E (APOE); hemoglobin subunit α1 (HBA1); angiotensin II receptor 1 (AGTR1); collagen I α1 (COL1A1); galectin 3 (LGALS3) and TIMP metallopeptidase inhibitor 1 (TIMP1). The expression of these genes was found to be consistent in TCGA datasets. Kaplan-Meier analysis revealed that APOE was significantly associated with overall survival (P=0.00067) and disease free survival (P=0.00220). Additionally, low expression of APOE was significantly associated with older age (P<0.001) and higher TNM stage (P<0.001) compared with the high expression group. Therefore, APOE may function as a predictive risk indicator for progression as well as prognosis of PTC.

miR-650 promotes motility of anaplastic thyroid cancer cells by targeting PPP2CA

PURPOSE

Aberrant expression of miRNAs is crucial in several tissues tumorigenesis including thyroid. Recent studies demonstrated that miR-650 plays different role depending on the cancer type. Herein, we investigated the role of miR-650 in thyroid carcinoma.

METHODS

The expression of miR-650 was analyzed in human thyroid tissues by q-RT-PCR. Anaplastic (8505C, CAL62, SW1736) and papillary (TPC-1) thyroid cancer cell lines were used to dissect the role of miR-650 on malignant hallmarks of transformation. Label-free proteomic analysis was exploited to unravel the targets of miR-650, while luciferase reporter assay and functional experiments were performed to confirm a selected target. Spearman's rank correlation test was used to assess the association between miR-650 and its target in human thyroid cancer tissues.

RESULTS

miR-650 is over-expressed in anaplastic (ATC) thyroid carcinoma where it enhances cell migration and invasion. Proteomic label-free and bioinformatics analysis revealed that the serine-threonine protein phosphatase 2 catalytic subunit alpha (PPP2CA) is a target of miR-650; these finding were confirmed by luciferase assay. Restoration of PPP2CA mRNA, deprived of its 3'UTR, is able to revert the malignant phenotype induced by miR-650 in HEK-293 cells. Importantly, PPP2CA is down-regulated in ATC tissues and is inversely correlated with miR-650.

CONCLUSIONS

miR-650 displayed oncogenic activity in ATC cells through targeting PPP2CA phosphatase. These results suggest that miR-650/PPP2CA axis could be modulated to interfere with motile ability of thyroid carcinoma cells.

Analysis of lncRNA and mRNA Transcriptomes Expression in Thyroid Cancer Tissues Among Patients With Exposure of Medical Occupational Radiation

Background:

Occupational exposure of radiation among medical radiation workers contributes to the subsequent increased risk of thyroid cancer. Long noncoding RNAs (lncRNAs) are emerging as important regulators of cancer biology. However, little is known about lncRNA expression in thyroid cancer tissues from patients who are exposed to medical occupational radiation. The purpose of this study is to reveal the transcriptomes difference between thyroid cancer tissues and adjacent nonneoplastic thyroid tissues.

Methods:

Microarray technology was used in this study. Quantitative reverse transcription polymerase chain reaction was adopted to verify 6 differentially expressed lncRNAs. Gene ontology and pathway analyses were performed using standard enrichment computational methods. Potential target genes of the differentially expressed lncRNAs were predicted with 2 independent algorithms.

Results:

A total of 23 lncRNA and messenger RNA transcripts were found differentially expressed in the thyroid cancer tissues (fold change ≥2.0, P < .05). This differential lncRNA expression may affect many pathways, including those involved in cysteine and methionine metabolism, Huntington disease, propanoate metabolism, and carcinogenesis.

Conclusions:

Our study provides a transcriptome-wide screening and analysis of the lncRNA expression profile in thyroid cancer tissues from patients with medical occupational radiation exposure and lays the foundation for further investigation of lncRNAs related to thyroid cancer development and carcinogenic risk of medical occupational radiation exposure.

Overexpression of COL5A1 promotes tumor progression and metastasis and correlates with poor survival of patients with clear cell renal cell carcinoma

Background and aims

COL5A1 has been identified to be involved in metastasis of clear cell renal cell carcinoma (ccRCC) by bioinformatic analysis. This study aimed to investigate COL5A1 expression and its clinical significance in ccRCC. The function of COL5A1 in ccRCC was further investigated.

Methods

COL5A1 expression was examined in 256 ccRCC tissues and paired adjacent normal renal tissues by immunohistochemistry and real-time quantitative PCR. The clinical significance of COL5A1 expression was evaluated. Downregulation of COL5A1 was achieved using siRNA. The effects of COL5A1 silencing on cell proliferation, apoptosis, migration, invasion in vitro, and tumor growth in vivo were investigated.

Results

COL5A1 expression was upregulated in the majority of the ccRCC tissues at both protein and mRNA levels. COL5A1 expression was significantly correlated with tumor diameter, tumor stage, tumor grade, distant metastasis, recurrence, necrosis, and sarcomatoid (all P<0.05). COL5A1 expression was also significantly associated with overall survival of ccRCC patients (HR 1.876; P=0.027) and recurrence-free survival of localized ccRCC patients (HR 4.751; P<0.001). The accuracy of TNM, University of California Los Angeles Integrated Staging System, and Mayo clinic stage, size, grade, and necrosis prognostic models was improved when COL5A1 expression was added.

Conclusion

COL5A1 knockdown significantly inhibited cell proliferation, induced cell apoptosis, inhibited cell migration and invasion in vitro, and inhibited tumor growth in vivo. Therefore, COL5A1 may be a novel prognostic biomarker and a promising therapeutic target for ccRCC.

Network Analyses of Integrated Differentially Expressed Genes in Papillary Thyroid Carcinoma to Identify Characteristic Genes

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC.

Identification of key genes and miRNAs markers of papillary thyroid cancer

OBJECTIVE

In this study, crucial genes and microRNAs (miRNAs) associated with the progression, staging, and prognosis of papillary thyroid cancer (PTC) were identified.

METHODS

Four PTC datasets, including our own mRNA-sequencing (mRNA-seq) dataset and three public datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas, were used to analyze differentially expressed genes (DEGs) and miRNAs (DEMs) between PTC tumor tissues and paired normal tissues (control). Gene ontology (GO) terms and pathways associated with these DEGs were identified, and protein-protein interactions (PPIs) were analyzed. Additionally, an miRNA-mRNA regulatory network was constructed and the functions of DEMs were explored. Finally, miRNAs/mRNAs associated with tumor staging and prognosis were identified. The expression levels of several key genes and miRNAs were validated by qRT-PCR.

RESULTS

Numerous DEGs and DEMs were identified between tumor and control groups in four datasets. The DEGs were significantly enriched in cell adhesion and cancer-related GO terms and pathways. In the constructed PPI network, ITGA2, FN1, ICAM1, TIMP1 and CDH2 were hub proteins. In the miRNA-mRNA negative regulatory networks, miR-204-5p regulated the largest number of target genes, such as TNFRSF12A. miR-146b, miR-204, miR-7-2, and FN1 were associated with tumor stage in PTC, and TNFRSF12A and CLDN1 were related to prognosis.

CONCLUSIONS

Our results suggested the important roles of ITGA2, FN1, ICAM1, TIMP1 and CDH2 in the progression of PTC. miR-204-5p, miR-7-2, and miR-146b are potential biomarkers for PTC staging and FN1, CLDN1, and TNFRSF12A may serve as markers of prognosis in PTC.

Genetic expression profile-based screening of genes and pathways associated with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer; however, the specific genes and signaling pathways involved in this cancer remain largely unclear. The present study analyzed three profile datasets, GSE6004, GSE29265 and GSE60542, which were comprised of 47 PTC and 41 normal thyroid tissue samples, to identify key genes and pathways associated with PTC. Initially, differentially-expressed genes (DEGs) between PTC and normal thyroid tissue were screened using R 3.4.0 (2017-04-21, R Foundation, Vienna, Austria, http://www.R-project.org/). These DEGs were then clustered by gene ontology functional terms and representative signaling pathways. Additionally, specific key gene nodes were filtered out from a constructed protein-protein interaction (PPI) network. The results identified a total of 423 shared DEGs associated with PTC, including 211 upregulated and 212 downregulated genes. These 423 genes were primarily enriched in glycosaminoglycan binding, sulfur compound binding, heparin binding, enzyme activator activity, peptidase activator activity and hsa04512: Extracellular matrix (ECM)-receptor interaction. A total of 21 central node genes were identified as key genes in the PTC disease process including complement factor D (CFD), Collagen Type I α 1 Chain (COL1A1), Extracellular Matrix Protein 1 (ECM1) and Fibronectin 1 (FN1). These genes are involved in protease binding, G-protein coupled receptor binding, extracellular matrix structural constituent and peptidase regulator activity. To conclude, using bioinformatics analysis, the present study identified candidate DEGs and critical pathways in PTC that may improve the current understanding regarding the underlying mechanisms of PTC. These genes and pathways may be used as potential therapeutic targets of PTC in the future.

Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma

AIM

Anaplastic thyroid carcinoma (ATC) is the most lethal thyroid malignancy. Identification of novel drug targets is urgently needed.

MATERIALS & METHODS

We re-analyzed several GEO datasets by systematic retrieval and data merging. Differentially expressed genes (DEGs) were filtered out. We also performed pathway enrichment analysis to interpret the data. We predicted key genes based on protein-protein interaction networks, weighted gene co-expression network analysis and genes' cancer/testis expression pattern. We also further characterized these genes using data from the Cancer Genome Atlas (TCGA) project and gene ontology annotation.

RESULTS

Cell cycle-related pathways were significantly enriched in upregulated genes in ATC. We identified TRIP13, DLGAP5, HJURP, CDKN3, NEK2, KIF15, TTK, KIF2C, AURKA and TPX2 as cell cycle-related key genes with cancer/testis expression pattern. We further uncovered that most of these putative key genes were critical components during chromosome segregation.

CONCLUSION

We predicted several key genes harboring potential therapeutic value in ATC. Cell cycle-related processes, especially chromosome segregation, may be the key to tumorigenesis and treatment of ATC.

Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co-expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co-expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co-expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC-related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes.

Periostin as a novel biomarker for postoperative recurrence of chronic rhinosinitis with nasal polyps

We previously reported that chronic rhinosinusitis with nasal polyps (CRSwNP) was subdivided into four chronic rhinosinusitis (CRS) subtypes using the JESREC scoring system. We sought to identify the gene expression profile and biomarkers related with CRSwNP by RNA-sequence. RNA-sequencing was performed to identify differentially expressed genes between nasal polyps (NPs) and inferior turbinate mucosa from 6 patients with CRSwNP, and subsequently, quantitative real-time PCR was performed to verify the results. ELISA was performed to identify possible biomarkers for postoperative recurrence. In the RNA-sequencing results, periostin (POSTN) expression was the highest in NP. We focused on POSTN and investigated the protein level of POSTN by immunohistochemistry and ELISA. POSTN was diffusely expressed in moderate and severe eosinophilic CRS using immunohistochemistry, and its staining pattern was associated with the severity of the phenotype of the CRSwNP (P < 0.05). There was a significant difference between the POSTN high/low groups for postoperative recurrence when the cutoff point was set at 115.5 ng/ml (P = 0.0072). Our data suggests that the protein expression level of POSTN was associated with the severity of CRSwNP, and serum POSTN can be a novel biomarker for postoperative recurrence of CRSwNP.

Identification of Biomarkers Based on Differentially Expressed Genes in Papillary Thyroid Carcinoma

The incidence of papillary thyroid carcinoma (PTC) is increasing rapidly throughout the world. Hence, there is an urgent need for identifying more specific and sensitive biomarkers to explorate the pathogenesis of PTC. In this study, three pairs of stage I PTC tissues and matched normal adjacent tissues were sequenced by RNA-Seq, and 719 differentially expressed genes (DEGs) were screened. KEGG pathway enrichment analyses indicated that the DEGs were significantly enriched in 28 pathways. A total of 18 nodes consisting of 20 DEGs were identified in the top 10% of KEGG integrated networks. The functions of DEGs were further analysed by GO. The 13 selected genes were confirmed by qRT-PCR in 16 stage I PTC patients and by The Cancer Genome Atlas (TCGA) database. The relationship interactions between DEGs were analysed by protein-protein interaction networks and chromosome localizations. Finally, four newly discovered genes, COMP, COL3A1, ZAP70, and CD247, were found to be related with PTC clinical phenotypes, and were confirmed by Spearman’s correlation analyses in TCGA database. These four DEGs might be promising biomarkers for early-stage PTC, and provide an experimental foundation for further exploration of the pathogenesis of early-stage PTC.

Overexpression of collagen type V α1 chain in human breast invasive ductal carcinoma is mediated by TGF-β1

Collagen type V α1 chain (COL5A1) is a minor fibrillar collagen in mammals that co-polymerizes with type I collagen to adjust the diameter of collagen molecules. However, the function of COL5A1 in invasive ductal carcinoma (IDC) of the human breast remains unknown. In the present study, our group examined the expression of COL5A1 in IDC compared with its adjacent normal tissue and fibroadenoma of the breast. COL5A1 was revealed to be overexpressed in IDC compared with benign tumor and adjacent normal control tissues, and was associated with the expression of estrogen receptor and progesterone receptor. No association between COL5A1 expression and tumor size, lymph node metastasis, clinical stage, age, or Her2 expression was identified. High expression of COL5A1 mRNA was associated with distant metastasis free survival in patients with breast cancer. Knockdown of COL5A1 led to a decrease of cell viability, as detected by the WST-1 assay, and an inhibition of migration and invasion, as detected by wound healing and Transwell assays, respectively, in the breast cancer cell line MCF-7. The expression of COL5A1 in MCF-7 cells was downregulated by transforming growth factor (TGF)‑β1, which was abolished in the presence of SB-431542, an inhibitor of TGF-β type I receptor. In conclusion, these data indicated that COL5A1 is overexpressed in IDC and regulated by TGF-β1, suggesting that an increase of COL5A1 reflects tumor progression and may serve as a novel biomarker and therapeutic target for the treatment of breast IDC.

Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

RNAs may act as competing endogenous RNAs (ceRNAs), a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA) to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA)-messenger RNA (mRNA)long non-coding RNA (lncRNA) networks from miRcode database and weighted correlation network analysis (WGCNA), based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB). We applied the pipeline into The Cancer Genome Atlas (TCGA) thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs) were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA-mRNA-lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients' survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

Comprehensive characterization of differentially expressed genes in thyroid cancer

AIM

To explore the patterns of gene expression and functionally characterize the differentially expressed genes (DEGs) in thyroid cancer.

METHODS

DEGs were determined between 57 paired thyroid cancer and noncancerous tissues using DESeq2. Subsequently, the main functions of the DEGs were studied by a variety of analyses.

RESULTS

We identified a cohort of 752 upregulated and 309 downregulated DEGs in thyroid cancer. Several hub DEGs were found in the protein-protein interaction networks. We also revealed a set of DEGs that were dysmethylated, involved in copy number variations and associated with clinical features in thyroid cancer.

CONCLUSION

These results provide some novel findings on DEGs in thyroid cancer, which will be useful to guide further investigation and target therapy for this disease. [Formula: see text].

Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray

Osteosarcoma is a common and highly malignant tumour in children and teenagers that is characterized by drug resistance and high metastatic potential. Patients often develop pulmonary metastasis and have a low survival rate. However, the mechanistic basis for pulmonary metastasis remains unclear. To identify key gene and pathways associated with pulmonary metastasis of osteosarcoma, the authors downloaded the gene expression dataset GSE85537 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in primary tumours and lung metastases. Subsequently, the authors performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein-protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In total, 2,493 genes were identified as DEGs. Of these, 485 genes (19.45%) were upregulated, and the remaining 2,008 genes (80.55%) were downregulated. The authors identified the predominant GO categories and KEGG pathways that were significantly over-represented in the metastatic OS samples compared with the non-metastatic OS samples. A PPI network was constructed, and the results indicated that ALB, EGFR, INS, IL6, CDH1, FYN, ERBB2, IL8, CXCL12 and RAC2 were the top 10 core genes. The enrichment analyses of the genes involved in the top three significant modules demonstrated that the DEGs were principally related to neuroactive ligand-receptor interaction, the Rap1 signaling pathway, and protein digestion and absorption. Together, these data elucidated the molecular mechanisms of OS patients with pulmonary metastasis and provide potential therapeutic targets. However, further experimental studies are needed to confirm these results.

Cell Cycle M-Phase Genes Are Highly Upregulated in Anaplastic Thyroid Carcinoma

BACKGROUND

Anaplastic thyroid carcinoma (ATC) accounts for only 3% of thyroid cancers, yet strikingly, it accounts for almost 40% of thyroid cancer deaths. Currently, no effective therapies exist. In an effort to identify ATC-specific therapeutic targets, we analyzed global gene expression data from multiple studies to identify ATC-specific dysregulated genes.

METHODS

The National Center for Biotechnology Information Gene Expression Omnibus database was searched for high-throughput gene expression microarray studies from human ATC tissue along with normal thyroid and/or papillary thyroid cancer (PTC) tissue. Gene expression levels in ATC were compared with normal thyroid or PTC using seven separate comparisons, and an ATC-specific gene set common in all seven comparisons was identified. We investigated these genes for their biological functions and pathways.

RESULTS

There were three studies meeting inclusion criteria, (including 32 ATC patients, 69 PTC, and 75 normal). There were 259 upregulated genes and 286 downregulated genes in ATC with at least two-fold change in all seven comparisons. Using a five-fold filter, 36 genes were upregulated in ATC, while 40 genes were downregulated. Of the 10 top globally upregulated genes in ATC, 4/10 (MMP1, ANLN, CEP55, and TFPI2) are known to play a role in ATC progression; however, 6/10 genes (TMEM158, CXCL5, E2F7, DLGAP5, MME, and ASPM) had not been specifically implicated in ATC. Similarly, 3/10 (SFTA3, LMO3, and C2orf40) of the most globally downregulated genes were novel in this context, while 7/10 genes (SLC26A7, TG, TSHR, DUOX2, CDH1, PDE8B, and FOXE1) have been previously identified in ATC. We experimentally validated a significant correlation for seven transcription factors (KLF16, SP3, ETV6, FOXC1, SP1, EGFR1, and MAFK) with the ATC-specific genes using microarray analysis of ATC cell lines. Ontology clustering of globally altered genes revealed that "mitotic cell cycle" is highly enriched in the globally upregulated gene set (44% of top upregulated genes, p-value <10^-30).

CONCLUSIONS

By focusing on globally altered genes, we have identified a set of consistently altered biological processes and pathways in ATC. Our data are consistent with an important role for M-phase cell cycle genes in ATC, and may provide direction for future studies to identify novel therapeutic targets for this disease.