Identification of Key Pathways and Genes in Anaplastic Thyroid Carcinoma via Integrated Bioinformatics Analysis

Gene-expression profile analysis to disclose diagnostics and therapeutics biomarkers for thyroid carcinoma

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA.

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.

FN1 Promotes Thyroid Carcinoma Cell Proliferation and Metastasis by Activating the NF-Κb Pathway

BACKGROUND

Thyroid cancer (THCA) is a common endocrine tumor. This study aims to identify the THCA-related key gene Fibronectin 1 (FN1) by bioinformatics methods and explore its function and regulatory mechanism.

METHODS

Gene Expression Omnibus database (GSE3678, GSE33630, and GSE53157 datasets) was searched for the analysis of differentially expressed genes (DEGs) in THCA tissues v.s. (normal tissues). The enrichment of DEGs was investigated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways using the DAVID database. Screening the hub gene was performed with the STRING database and Cytoscape software. The expression and survival analyses of these hub genes in THCA were studied with the Gene Expression Profiling Interactive Analysis database. LinkedOmics database was searched for the related signaling pathways regulated by FN1 in THCA. Real-time quantitative reverse transcriptase polymerase chain reaction was adopted to detect the mRNA expression of Fibromodulin, microfibril-associated protein 4, Osteoglycin, and FN1. The cell viability, growth, migration and aggressiveness were examined by Cell counting kit-8, 5-Ethynyl-2 '- deoxyuridine assay, scratch assay, and Transwell assay. The expression levels of NF-κB signaling pathway-related proteins (p-IκB-α, p-IKK-β, NF-κB p65) were detected by Western blot.

RESULTS

FN1 mRNA was up-regulated in THCA tissues and cell lines (MDA-T85 and MDA-T41). The high expression of FN1 is relevant to larger tumor diameters and lymph node metastasis in sufferers with THCA. Functional experiments showed that overexpression of FN1 in the MDA-T85 cell line promoted growth, migration and aggressiveness; knockdown of FN1 in MDA-T41 cells inhibited these malignant behaviors. In mechanism, FN1 promoted the expression levels of proteins related with NF-κB signaling pathway and activated NF-κB signaling pathway.

CONCLUSION

FN1 is up-regulated in THCA and facilitates cell growth, migration and invasion by activating the NF-κB signaling pathway. FN1 will be a promising biomarker of THCA and may become a molecular target for THCA treatment.

GANT61 suppresses cell survival, invasion and epithelial-mesenchymal transition through inactivating AKT/mTOR and JAK/STAT3 pathways in anaplastic thyroid carcinoma

Glioma-associated oncogene (Gli) antagonist-61 (GANT61) not only suppresses the malignant behavior of several cancers but also presents synergistic effects with other anticancer agents on suppressing the progression of cancers, while relevant information is rare in anaplastic thyroid carcinoma (ATC). This study aimed to explore the therapeutic effect of GANT61 in ATC and its molecular mechanism. ATC cells (8505C and CAL-62) were treated with GANT61, followed by detection of cell proliferation, apoptosis, invasion and epithelial-mesenchymal transition (EMT) markers. Subsequently, RNA sequencing was performed to explore the potential downstream pathway. Following that, rescue experiments were conducted by SC79 (AKT activator) or colivelin (STAT3 activator) monotreatment or combined with GANT61 in ATC cells. GANT61 reduced Gli1 expression, suppressed proliferation at several time settings, promoted apoptosis, inhibited invasion and increased E-cadherin while decreased Vimentin and Snail expressions (EMT markers) in ATC cells. The subsequent RNA sequence identified 85 upregulated differentially expressed genes (DEGs) and 71 downregulated DEGs in GANT61-treated ATC cells, which were mainly enriched in PI3K/AKT, JAK/STAT, Hedgehog and mTOR pathways. Next, the inactivation of AKT/mTOR and JAK/STAT3 pathways by GANT61 treatment was verified by western blot. The following rescue experiments showed that SC79 or colivelin treatment promoted the malignant behaviors of ATC cells. More importantly, SC79 or colivelin treatment compensated the effect of GANT61 treatment on cell proliferation at several time settings and apoptosis, invasion, and part of that on EMT in ATC cells. GANT61 suppresses cell survival, invasion and EMT through inactivating AKT/mTOR or JAK/STAT3 pathways in ATC.

Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Methods

For determining pathways and key genes that have relation with development of ATC, differentially expressed genes (DEGs) from GSE33630 as well as GSE65144 expression microarray were screened. Furthermore, we also worked on carrying out the task of constructing a protein-protein interaction (PPI) network and the work of weighing gene coexpression network (WGCNA). DAVID was utilized for the performance of the Gene Ontology (GO) as well as KEGG pathway enrichment analyses for DEGs. We used TCGA THCA data and GSE53072 to further verify the hub gene and hub pathway.

Results

We came to the conclusion of the recognition of a total of 1063 genes as DEGs. Analysis regarding functional and pathway enrichment showed that there existed a notable enrichment of upregulated DEGs in the organization of extracellular structure and matrix organization, as well as in organelle fission and nuclear division. The downregulated DEG was markedly gathered in the thyroid hormone metabolic process and generation, as well as in the metabolic process of cellular modified amino acid. We identified 10 hub genes (CXCL8, CDH1, AURKA, CCNA2, FN1, CDK1, ITGAM, CDC20, MMP9, and KIF11) through the PPI network, which might be strongly linked to the carcinogenesis and the development of ATC. In the coexpression network, 6 modules that were relevant to ATC were recognized. The modules were related to the interaction of signaling pathway of p53, Hippo, PI3K/Akt, and ECM-receptor. This hub genes and hub pathway were further successfully validated as a potential biomarker for carcinogenesis and prediction in another database GSE53072.

Conclusion

To summarize, this research displayed an illustration of hub genes and pathways that had relation with ATC development, which suggested that DEGs and hub genes, recognized on the basis of bioinformatics analyses, were valuable in the diagnosis for patients with ATC.

CDCA8 Contributes to the Development and Progression of Thyroid Cancer through Regulating CDK1

Background: This study aims to reveal regulatory role of cell division cycle associated 8 (CDCA8) in thyroid cancer progression and metastasis.

Methods: A series of experiments in vivo and in vitro were performed to explore the function of CDCA8 in thyroid cancer.

Results: Immunohistochemical analysis showed that CDCA8 expression levels were upregulated in thyroid cancer tissues compared with normal tissues, and were statistically correlated with tumor stage. Results of in vitro loss-of-function assay showed that downregulation of endogenous expression of CDCA8 could significantly inhibit cell proliferation, colony formation, cell migration, and promote apoptosis. Thyroid cancer cells lacking CDCA8 expression also had reduced tumorigenicity in vivo. Further, results of preliminary mechanistic exploration showed that CDK1 may be a potential downstream molecule of CDCA8 in regulating thyroid cancer progression. We subsequently confirmed that CDK1 itself exerted a significant regulatory function in thyroid cancer by loss- and gain-of-function experiments. Moreover, overexpression of CDK1 could weaken the tumor suppressive effect caused by CDCA8 knockdown.

Conclusions: CDCA8 functions as an oncogene in thyroid cancer, and CDCA8 knockdown suppresses cancer development in vitro and in vivo. Additionally, CDK1 was further identified as a potential target of CDCA8 in thyroid cancer.

Aberrant Expression of BUB1B Contributes to the Progression of Thyroid Carcinoma and Predicts Poor Outcomes for Patients

Objective: This study aimed to clarify the function and potential mechanism of BUB1B in THCA.

Methods: Expression of BUB1B in THCA was firstly determined, and its important prognostic value was then demonstrated. The potential mechanism was initially predicted by KEGG analysis. To explore the specific function of BUB1B in THCA, we used lentivirus infection to knock down the BUB1B, and then performed flow cytometry, colony formation, transwell, and wound-healing assays. Related protein expression was detected through western blotting. Additionally, we predicted the BUB1B-regulated pathways involved in THCA by GSEA analysis.

Results: BUB1B expression was highly increased in THCA tissues relative to normal controls. We further found that BUB1B was essential for tumor cell proliferation, and BUB1B high expression predicted a shorter PFS time of THCA patients. More importantly, Cox regression determined the BUB1B as an independent prognostic factor for PFS in THCA. BUB1B was initially found to participate in the cell cycle pathway from KEGG analysis. Unexpectedly, we did not detect the disturbing effect on the cell cycle distribution of THCA cells with BUB1B knockdown. But, BUB1B knockdown inhibited the proliferation, invasion, and migration of THCA cells, as well as increased apoptotic cells, and the results were further confirmed by western blotting. Through GSEA analysis, we predicted a positive correlation between BUB1B and metastasis-related pathways such as mTOR and NF-kappa B signaling pathways.

Conclusions: Present study identified BUB1B as a promising clinical prognostic factor in THCA, as well as a potential novel therapeutic target for cancer treatment.

Expression of colorectal neoplasia differentially expressed in anaplastic thyroid carcinoma and its effect on cancer cell proliferation

Background

The incidence of anaplastic thyroid cancer (ATC) is high among human cancers. Colorectal neoplasia differentially expressed (CRNDE) is highly expressed in common tumors, and is therefore a potential molecular target for anti-tumor therapy. However, the function of CRNDE in ATC remains elusive.

Methods

The Gene Expression Omnibus (GEO) database was used to screen the differential expression of long-noncoding RNA (lncRNA) in ATC tissues. The Cancer Genome Atlas (TCGA) database was used to analyze the expression of CRNDE in thyroid cancer (THCA) tissues and its impact on patient prognosis. Quantitative real-time PCR (qRT-PCR) was used to determine the expression level of CRNDE in tumor and control tissues. The biological function of CRNDE in THCA was explored using TCGA RNA sequencing (RNA-seq) data analysis. ATC cell lines with low and high CRNDE expression were selected for CRNDE siRNA transfection, and the proliferation of cells was detected in each group.

Results

The GEO and TCGA databases analysis results showed that CRNDE was highly expressed in ATC tissues, which is related to the poor prognosis of THCA patients. Also, the expression of CRNDE in the ATC cell line, ARO (human thyroid cancer cell line), was relatively high, while the expression in sw579 is relatively low. Therefore, ARO and sw579 were chosen for CRNDE small interfering RNA (siRNA) transfection. Compared with negative control (si-NC), the expression of CRNDE in si-CRNDE-1, si-CRNDE-2, and si-CRNDE-3 was reduced, indicating that the inhibitory effect was significantly enhanced and the cell proliferation ability was reduced, and the cell cycle is arrested in the G0/G1 phase. Finally, it was found that the wnt3a, β-catenin, and cyclinD1 protein expressions of si-CRNDE-1 and si-CRNDE-2 were significantly reduced.

Conclusions

The high expression of CRNDE in ATC tissues may promote the proliferation of ATC cells by regulating the Wnt/β-catenin signaling pathway. CRNDE may be a potential molecular target for the treatment of ATC.

Artificial intelligence perspective in the future of endocrine diseases

In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.

Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma

BACKGROUND

Ferroptosis is an iron-dependent and regulated cell death that has been widely reported in a variety of malignancies. The overall survival of papillary thyroid cancer (PTC) is excellent, but the identification of patients with poor prognosis still faces challenges. Nevertheless, whether ferroptosis-related genes (FRGs) can be used to screen high-risk patients is not clear.

METHODS

We obtained the clinical data of patients with PTC and FRGs from the UCSC Xena platform and the FerrDb respectively. Differentially expressed genes (DEGs) of FRGs were obtained from the entire The Cancer Genome Atlas (TCGA). Subsequently, the entire TCGA dataset was randomly split into two subsets: training and test datasets. Based on DEGs, we constructed a predictive model which was tested in the test dataset and the entire TCGA dataset to predict progression-free survival (PFS). Patients were categorized into high- or low-risk groups based on their median risk score. We analyzed differences in some aspects, including pathway enrichment analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), tumor microenvironment (TME), human leukocyte antigen (HLA) genes, and tumor mutation burden (TMB) analyses, between high-risk and low-risk groups.

RESULTS

A predictive model with three FRGs (HSPA5, AURKA, and TSC22D3) was constructed. Patients in the high-risk group had worse PFS compared with patients in the low-risk group. Functional analysis results revealed that ssGSEA, immune cell infiltration, TME, HLA, and TMB were closely associated with ferroptosis.

CONCLUSION

The prognostic model constructed in this study can effectively predict PFS for patients with PTC.

Prognostic Value and Significant Pathway Exploration Associated with TOP2A Involved in Papillary Thyroid Cancer

Background

Topoisomerase 2-alpha (TOP2A) has been identified as a hub gene that played an important role in the initiation and progression of thyroid carcinoma (THCA). However, the exact function of TOP2A in papillary thyroid cancer (PTC) remained elusive. The current study aimed to evaluate the TOP2A expression, prognosis significance and key signaling pathways involved in PTC.

Methods

We firstly evaluated the expression of TOP2A in PTC via UALCAN, cBioportal, HPA and LinkdedOmics databases. Genetic alteration of TOP2A in PTC was then explored in cBioportal. Prognostic impacts of TOP2A expression on disease-free survival (DFS) of PTC patients were subsequently evaluated using Kaplan–Meier plotter and Gepia databases. Taking gender, age, cancer stage, T, N and M stages into consideration, we compared survival difference between TOP2A high and low expression groups. KEGG pathway analysis in WebGestalt and GSEA analysis were further performed to reveal the potential TOP2A-associated signaling pathways involved in PTC. Finally, the upstream microRNAs of TOP2A were assessed using DIANA, TargetScan, miRDB and miRWALK database, followed by mechanism exploration of upstream microRNAs.

Results

1) The mRNA and protein of TOP2A were highly expressed in PTC tissue compared with normal thyroid tissue. TOP2A expression was associated with patient’s age, N stage and cancer stage (all P<0.05). TOP2A protein was mainly localized to nucleoplasm. 2) Most of samples occurred the missense substitution, and mutation site was located at K1199E. Nucleotide mutations were mainly presented as G>A (35.29%). 3) TOP2A high expression significantly influenced the DFS of PTC patients (P=0.015). Restricted survival analysis showed that TOP2A high expression caused poorer DFS of female patients (P=0.003) and those with age <60 years old (P=0.002), early clinical stage (P=0.012), N0 stage (P=0.002) or M0 stage (P=0.040). 4) Pathway analysis suggested that TOP2A positively participated in the cell cycle, oocyte meiosis and p53 signaling pathways (all P<0.05) involved in thyroid cancer.

Conclusion

The expression of TOP2A was higher in PTC tissue, which resulted in a worse DFS of patients with PTC. TOP2A might act as an effective therapeutic target for PTC treatment.

Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis

The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein-protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets.

Integrated bioinformatics analysis for the identification of key genes and signaling pathways in thyroid carcinoma

Thyroid carcinoma (TC) is one of the most common types of endocrine neoplasm with poor prognosis due to its aggressive behavior. Biomarkers for early diagnosis and prevention of TC are in urgent demand. By using a bioinformatics analysis, the present study aimed to identify essential genes and pathways associated with TC. First, the GSE27155 and GSE50901 expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were obtained using the two microarray datasets and further subjected to integrated analysis. A gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed 45 common DEGs in the two datasets. GO and KEGG pathway analysis indicated that the biological functions of the DEGs included protein binding, cardiac muscle cell potential involved in contraction, aldehyde dehydrogenase activity, the TGF-β receptor signaling pathway and the canonical Wnt signaling pathway. A protein-protein interaction network was also constructed and visualized to display the nodes of the top 9 up- and 36 downregulated common DEGs. The integrated bioinformatics analysis indicated that potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) was the most significantly upregulated DEG. The transcriptional levels of KCNJ2 were confirmed to be elevated in TC tissues compared with those in normal tissues using reverse transcription-quantitative PCR analysis. Furthermore, the expression level of KCNJ2 was significantly associated with the 5-year survival rate of patients with TC, which was determined using the Kaplan-Meier method. In TC cell lines, KCNJ2 was also upregulated as compared with that in a normal control cell line. Finally, small interfering RNA was used to knock down the expression of KCNJ2, which was demonstrated to inhibit cell proliferation, migration and invasion, while increasing apoptosis in TC cells. In conclusion, in the present study, KCNJ2 was screened as an oncogene with a crucial role in TC development and progression and may represent a promising candidate biomarker and therapeutic target for TC.

TMEM158 May Serve as a Diagnostic Biomarker for Anaplastic Thyroid Carcinoma: An Integrated Bioinformatic Analysis

Anaplastic thyroid carcinoma (ATC) is a rare but extremely lethal malignancy. However, little is known about the pathogenesis of ATC. Given its high mortality, it is critical to improve our understanding of ATC pathogenesis and to find new diagnostic biomarkers. In the present study, two gene microarray profiles (GSE53072 and GSE65144), which included 17 ATC and 17 adjacent non-tumorous tissues, were obtained. Bioinformatic analyses were then performed. Immunohistochemistry (IHC) and receiver operating characteristic (ROC) curves were then used to detect transmembrane protein 158 (TMEM158) expression and to assess diagnostic sensitivity. A total of 372 differentially expressed genes (DEGs) were identified. Through protein-protein interaction (PPI) analysis, we identified a significant module with 37 upregulated genes. Most of the genes in this module were related to cell-cycle processes. After co-expression analysis, 132 hub genes were selected for further study. Nine genes were identified as both DEGs and genes of interest in the weighted gene co-expression network analysis (WGCNA). IHC and ROC curves confirmed that TMEM158 was overexpressed in ATC tissue as compared with other types of thyroid cancer and normal tissue samples. We identified 8 KEGG pathways that were associated with high expression of TMEM158, including aminoacyl-tRNA biosynthesis and DNA replication. Our results suggest that TMEM158 may be a potential oncogene and serve as a diagnostic indicator for ATC.

Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis

Purpose

The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC.

Methods

Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR.

Results

A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3–1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells.

Conclusion

Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

Identification of Hub Genes in Anaplastic Thyroid Carcinoma: Evidence From Bioinformatics Analysis

Anaplastic thyroid carcinoma (ATC) is a rare type of thyroid cancer that results in fatal clinical outcomes; the pathogenesis of this life-threatening disease has yet to be fully elucidated. This study aims to identify the hub genes of ATC that may play key roles in ATC development and could serve as prognostic biomarkers or therapeutic targets. Two microarray datasets (GSE33630 and GSE53072) were obtained from the Gene Expression Omnibus database; these sets included 16 ATC and 49 normal thyroid samples. Differential expression analyses were performed for each dataset, and 420 genes were screened as common differentially expressed genes using the robust rank aggregation method. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to explore the potential bio-functions of these differentially expressed genes (DEGs). The terms and enriched pathways were primarily associated with cell cycle, cell adhesion, and cancer-related signaling pathways. Furthermore, a protein-protein interaction network of DEG expression products was constructed using Cytoscape. Based on the whole network, we identified 7 hub genes that included CDK1, TOP2A, CDC20, KIF11, CCNA2, NUSAP1, and KIF2C. The expression levels of these hub genes were validated using quantitative polymerase chain reaction analyses of clinical specimens. In conclusion, the present study identified several key genes that are involved in ATC development and provides novel insights into the understanding of the molecular mechanisms of ATC development.

Updates on the Management of Thyroid Cancer

The diagnostic modalities, stratification tools, and treatment options for patients with thyroid cancer have rapidly evolved since the development of the American Thyroid Association (ATA) guidelines in 2015. This review compiles newer concepts in diagnosis, stratification tools and treatment options for patients with differentiated thyroid cancer (DTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid cancer (ATC). Newer developments apply precision medicine in thyroid cancer patients to avoid over-treatment in low risk disease and under-treatment in high risk disease. Among novel patient-tailored therapies are selective RET inhibitors that have shown efficacy in the treatment of MTC with limited systemic toxicity compared with non-specific tyrosine kinase inhibitors. The combination of BRAF and MEK inhibitors have revolutionized management of BRAF V600E mutant ATC. Several immunotherapeutic agents are being actively investigated in the treatment of all forms of thyroid cancer. In this review, we describe the recent advances in the diagnosis and management of DTC, MTC, and ATC, with an emphasis on novel treatment modalities.

Identification of Differentially Expressed Genes Associated with Idiopathic Pulmonary Arterial Hypertension by Integrated Bioinformatics Approaches

Idiopathic pulmonary arterial hypertension (IPAH) is a fatal cardiovascular disease event with significant morbidity and mortality. However, its potential molecular mechanisms and potential key genes have not been totally evaluated. The gene expression profile of GSE33463, including 30 individuals diagnosed with IPAH and 41 normal controls, was downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified using limma package in R. Gene Ontology (GO) annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to get further insight into the possible functions of the identified DEGs. Then, the protein-protein interaction (PPI) network of all DEGs was constructed. Nodes with higher degree centrality (≥10) were considered as hub proteins in the PPI network. Area under the curve (AUC) values obtained from the receiver operating characteristic (ROC) curve analysis was utilized to assess the diagnostic effectiveness of hub genes in discriminating IPAH from normal individuals. Sixty-nine DEGs were identified, including 41 upregulated and 28 downregulated DEGs. The GO enrichment analysis indicated that genes were significantly enriched in oxygen carrier activity, oxygen binding, heme binding, molecular carrier activity, and antioxidant activity. KEGG pathway enrichment showed that genes were mainly involved in cytokine and cytokine receptor, Chemokine signaling pathway, interleukin-17 signaling pathway, and Toll-like receptor (TLR) signaling pathway. JUN, ALAS2, HBD, EPB42, TLR7, SLC4A1, and CXCR4 were identified as the hub genes nodes. The area under the ROC curve indicated that three hub genes have high diagnostic value in IPAH with AUC of 0.934 [95% confidence interval (CI): 0.849-0.979] in TLR7, 0.910 (95% CI: 0.818-0.965) in JUN, and 0.895 (95% CI: 0.800-0.955) in CXCR4. The identified candidate key genes and pathways help us understand the molecular mechanisms underlying the pathogenesis of IPAH. TLR7, JUN, and CXCR4 may be novel biomarkers in IPAH diagnosis.

Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

Purposes

Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.

Results

The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.

Conclusion

We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

Integrated assessment of CDK1 upregulation in thyroid cancer

Cyclin-dependent kinase 1 (CDK1) has a unique role in cell cycle regulation, as it is crucial for cell cycle progression and cell division. The aim of the present study was to use a combination of various detection methods to examine the expression and clinical significance of CDK1 in thyroid cancer (THCA). We used in-house tissue microarrays, immunohistochemistry, public RNA-sequencing, gene microarrays, and meta-analyses to conduct a comprehensive analysis of the role of CDK1 in the occurrence and development of THCA. CDK1 protein expression was notably higher in THCA tissues than in non-cancer tissues as evidenced by the in-house tissue microarrays. The expression of CDK1 protein was also significantly higher in pathologic T3-T4 than in T1-T2 samples. The pooled standardized mean difference (SMD) for CDK1 was 0.71 (95% CI, 0.46-0.95) including a total of 931 THCA and 585 non-cancerous thyroid tissue samples. An aggregation of the immunohistochemistry results and the RNA-sequencing/microarray findings gave a pooled SMD for CDK1 expression of 2.13 (95% CI, 1.30-2.96). The final area under curve (AUC) for the summarized receiver operating characteristic (sROC) was 0.7941 using all 1102 cases of THCA and 672 cases of controls. KEGG analysis with the co-expressed genes of CDK1 in THCA demonstrated the top enriched pathways to be the cell cycle, thyroid hormone synthesis, autoimmune thyroid disease, etc. In summary, we reveal the overexpression of CDK1 in THCA based on multiple detection methods that combine independent cohorts. However, further studies are required to elucidate the molecular mechanisms of CDK1 that promotes the biological aggressiveness of THCA cells.

Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics Analysis

BACKGROUND The aim of this study was to evaluate the differently expressed genes (DEGs) relevant to type 2 diabetes mellitus (T2DM) and pathway by performing integrated bioinformatics analysis. MATERIAL AND METHODS The gene expression datasets GSE7014 and GSE29221 were downloaded in GEO database, and DEGs from type 2 diabetes mellitus and normal skeletal muscle tissues were identified. Biological function analysis of the DEGs was enriched by GO and KEEG pathway. A PPI network for the identified DEGs was built using the STRING database. RESULTS Thirty top DEGs were identified from 2 datasets: GSE7014 and GSE29221. Of the 30 top DEGs, 20 were up-regulated and 10 were down-regulated. The 20 up-regulated genes were enriched in regulation of mRNA, protein biding, and phospholipase D signaling pathway. The 10 down-regulated genes were enriched in telomere maintenance via semi-conservative replication, AGE-RAGE signaling pathway in diabetic complications, and insulin resistance pathway. In the PPI network of 20 up-regulated DEGs, there were 40 nodes and 84 edges, with an average node degree of 4.2. For the 10 down-regulated DEGs, we found a total of 30 nodes and 105 edges, with an average node degree of 7.0 and local clustering coefficient of 0.812. Among the 30 DEGs, 10 hub genes (CNOT6L, CNOT6, CNOT1, CNOT7, RQCD1, RFC2, PRIM1, RFC4, RFC5, and RFC1) were also identified through Cytoscape. CONCLUSIONS DEGs of T2DM may play an essential role in disease development and may be potential pathogeneses of T2DM.

Integrated analysis of transcriptome data revealed MMP3 and MMP13 as critical genes in anaplastic thyroid cancer progression

To better understand the molecular mechanisms of anaplastic thyroid carcinoma (ATC), we aimed to identify the hub genes specifically involved in ATC by integrated bioinformatics analysis. In this study, using three Gene Expression Omnibus data sets with the same platform GPL570, we screened hub genes involved in ATC progression. In vitro experiments, such as western blot analysis, Transwell assays, and coimmunoprecipitation, was performed to verify our findings. By comparing three subtypes of thyroid cancer with normal tissue, we found ATC harbored more changed genes than well and poorly differentiated thyroid cancer. Using specifically differentially expressed genes between ATC and normal thyroid tissues to perform Gene ontology (GO) analysis, ATC showed enrichments of GO terms involved in lymphocyte migration and activation, collagen catabolic and metabolic process, thyroid hormone synthesis, and embolism. Using genes involved in extracellular matrix, coexpression network analysis and protein-protein interaction analysis were performed to identify matrix metalloproteinase 3 (MMP3) and MMP13 as two hub genes. Our experimental data indicated that both MMP3 and MMP13 were upregulated in ATC and knockdown of either of them could notably suppress ATC cell invasion and migration. Mechanistically, Gene Set Enrichment Analysis, coimmunoprecipitation, and rescue experiments revealed MMP3 and MMP13 not only interacted with each other, but also regulated each other through the janus kinase/signal transducer and activator of transcription 3 and mammalian target of rapamycin pathways. In conclusion, we identified a specific molecular mechanisms for the development of ATC by integrated analysis of transcriptome and in vitro experiments, which suggested that MMP3 and MMP13 might be developed as novel therapeutic targets for ATC.

Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.