Identification of key genes of papillary thyroid cancer using 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.

Integration analysis of cis- and trans-regulatory long non-coding RNAs associated with immune-related pathways in non-small cell lung cancer

Background

Long non-coding RNAs (lncRNAs) are importantly involved in the initiation and progression of non-small cell lung cancer (NSCLC). However, the classification and mechanisms of lncRNAs remain largely elusive.

Aim

Hence, we addressed this through bioinformatics analysis.

Methods and results

We utilized microarray technology to analyze lncRNAs and mRNAs in twenty paired NSCLC tumor tissues and adjacent normal tissues. Gene set enrichment analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were conducted to discern the biological functions of identified differentially expressed transcripts. Additionally, networks of lncRNA-mRNA co-expression, including cis-regulation, lncRNA-transcription factor (TF)-mRNA, trans-regulation, and lncRNA-miRNA-mRNA interactions were explored. Furthermore, the study examined differentially expressed transcripts and their prognostic values in a large RNA-seq dataset of 1016 NSCLC tumors and normal tissues extracted from the Cancer Genome Atlas (TCGA). The analysis revealed 391 lncRNAs and 344 mRNAs with differential expression in NSCLC tumor tissues compared to adjacent normal tissues. Subsequently, 43,557 co-expressed lncRNA-mRNA pairs were identified, including 27 lncRNA-mRNA pairs in cis, 9 lncRNA-TF-mRNA networks, 34 lncRNA-mRNA pairs in trans, and 8701 lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks. Notably, these lncRNAs were found to be involved in immune-related pathways. Six significant transcripts, including NTF4, PTPRD-AS, ITGA11, HID1-AS1, RASGRF2-AS1, and TBX2-AS1, were identified within the ceRNA network and trans-regulation.

Conclusion

This study brings important insights into the regulatory roles of lncRNAs in NSCLC, providing a fresh perspective on lncRNA research in tumor biology.

Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis

PURPOSE

To perform an extensive exploratory analysis to build a deeper insight into clinically relevant molecular biomarkers in Papillary, Follicular, and Anaplastic thyroid carcinomas (PTC, FTC, ATC).

METHODS

Thirteen Thyroid Cancer (THCA) datasets incorporating PTC, FTC, and ATC were derived from the Gene Expression Omnibus. Genes differentially expressed (DEGs) between THCA and normal were identified and subjected to GO and KEGG analyses. Multiple topological properties were harnessed and protein-protein interaction (PPI) networks were constructed to identify the hub genes followed by survival analysis and validation.

RESULTS

There were 70, 87, and 377 DEGs, and 23, 27, and 53 hub genes for PTC, FTC, and ATC samples, respectively. Survival analysis detected 39 overall and 49 relapse-free survival-relevant hub genes. Six hub genes, BCL2, FN1, ITPR1, LYVE1, NTRK2, TBC1D4, were found common to more than one THCA type. The most significant hub genes found in the study were: BCL2, CD44, DCN, FN1, IRS1, ITPR1, MFAP4, MKI67, NTRK2, PCLO, TGFA. The most enriched and significant GO terms were Melanocyte differentiation for PTC, Extracellular region for FTC, and Extracellular exosome for ATC. Prostate cancer for PTC was the most significantly enriched KEGG pathway. The results were validated using TCGA data.

CONCLUSIONS

The findings unravel potential biomarkers and therapeutic targets of thyroid carcinomas.

DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation

Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks as the 5th most prevalent cancer in the USA, with thousands diagnosed annually. This paper presents an ensemble learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of 633 samples with 969 mutations across 41 genes, collected from individuals of various demographics. Feature extraction encompasses techniques including Hahn moments, central moments, raw moments, and various matrix-based methods. Evaluation employs three testing methods: self-consistency test (SCT), independent set test (IST), and 10-fold cross-validation test (10-FCVT). The proposed ensemble learning model demonstrates promising performance, achieving 96% accuracy in the independent set test (IST). Statistical measures such as training accuracy, testing accuracy, recall, sensitivity, specificity, Mathew's Correlation Coefficient (MCC), loss, training accuracy, F1 Score, and Cohen's kappa are utilized for comprehensive evaluation.

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Background

Sperm-associated antigen 5 (SPAG5) has been identified as a novel driver oncogene involved in multiple cancers; however, its role in lung adenocarcinoma (LUAD) needs further investigation. Our study aims to elucidate the potential significance of SPAG5 in LUAD prognosis and its implications for the efficacy of immunotherapy.

Methods

In this study, we used bioinformatics analysis and tissue microarray (TMA) staining to examine the potential role of SPAG5 in LUAD survival and response to immunotherapy. We used the Oncomine, TIMER2.0, Gene Expression Profiling Interactive Analysis (GEPIA), Sangerbox, PredicScan, and Kaplan-Meier Plotter databases to examine the expression and prognostic role of SPAG5 in the LUAD of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases. We also used Cancer Single-cell State Atlas (CancerSEA) and Tumor Immune Estimation Resource (TIMER2.0) to analyze the association of SPAG5 with malignant phenotype and tumor immune microenvironment. Furthermore, Immune Cell Abundance Identifier (ImmuCellAI) analysis of TCGA sequencing data was used to predict the role of SPAG5 in determining the response to immune checkpoint blockade (ICB) treatment in LUAD. Co-expression analysis of programmed death-ligand 1 (PD-L1) and SPAG5 was performed using LUAD TMA immunohistochemistry (IHC) analysis.

Results

Our findings indicate that SPAG5 is overexpressed in LUAD and is positively correlated with advanced clinical stage, poor overall survival, relapse-free survival, and progression-free survival outcomes. SPAG5 may be involved in regulating the cell cycle, proliferation, invasion, DNA damage and repair, and tumor immunosuppression. Furthermore, TMA IHC analysis showed a positive correlation between PD-L1 expression in LUAD and SPAG5 which suggests that SPAG5 may serve as a potential predictor of response to ICB therapy in LUAD.

Conclusions

Our results highlight the role of SAPG5 in promoting a tumor malignancy phenotype and immunosuppression in LUAD and suggest that SPAG5 may serve as a potential response marker for ICB therapy.

Research into the mechanism of intervention of SanQi in endometriosis based on network pharmacology and molecular docking technology

BACKGROUND

By using network pharmacology and molecular docking technology, we have explored the mechanism of action of Sanqi in the treatment of endometriosis (EMS), in order to provide reference for clinical studies of Chinese medicine treatment of Ems and Chinese medicine pharmacology.

METHODS

There are 123 intersecting targets between the active ingredients of Sanqi and disease targets. In the Protein-Protein Interaction network, Jun proto-oncogene, AP-1 transcription factor subunit, tumor necrosis factor, interleukin 6, etc., are the core proteins. The top 20 genes ranked by degree have been analyzed according to the Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis, and 20 pathways have been identified.

RESULTS

On the Kyoto Encyclopedia of Genes and Genomes pathway, the most important part is the phosphatidylinositol 3'-kinase-Akt signaling pathway, and on the Gene Ontology pathway, it is the Heme binding. The top 3 targets docked to quercetin have a certain affinity when it is docked to their degree value. Among the chemical components of Sanqi, quercetin has the most targets, suggesting that it may play a major role in the treatment of EMS.

CONCLUSION

The results of molecular docking provide further evidence of the potential role of Sanqi for EMS. Overall, our study provides a new direction for the treatment of EMS and provides the basis for Sanqi as a drug for the treatment of EMS.

Bioinformatics analysis of downstream circRNAs and miRNAs regulated by Runt-related transcription factor 1 in papillary thyroid carcinoma

BACKGROUND

This study sought to clarify the role of Runt-related transcription factor 1's (RUNX1's) regulation of downstream circular ribonucleic acid (circRNA) in the occurrence and development of papillary thyroid carcinoma (PTC) and to explore its mechanism of action.

METHODS

The levels of RUNX1 were analyzed in PTC tumor tissues and adjacent non-tumor tissues in different types and at different stages via reverse-transcription quantitative polymerase chain reaction (RT-qPCR). The expression pattern and functional role of RUNX1 were analyzed in PTC cells via RT-qPCR, Western blotting, and Transwell assays. This study explored the differential expression of circRNA and microRNA (miRNA) in cells after knocking down RUNX1 through high-throughput sequencing and examined the changes in downstream signaling pathways through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses.

RESULTS

RUNX1 was upregulated in PTC tissues, and the expression levels of RUNX1 were related to PTC stage. The knockdown of RUNX1 inhibited the proliferation, migration, and invasion of cells. The high-throughput sequencing results showed that after RUNX1 knockdown, 29 circRNAs (11 upregulated and 18 downregulated) and 20 miRNAs (8 upregulated and 12 downregulated) had the most significant differential expression. The GO analysis of the differential circRNA downstream genes showed that the iron channel-related pathways, endosomal transport, learning, and memory pathways had the largest number of differential genes, and the most significant changes. The KEGG analysis showed that there were 2 pathways with P values <0.05; that is, the glycosaminoglycan synthesis and transcription dysregulation pathways. The GO analysis of the differential miRNA downstream genes showed that the protein binding and cytoplasmic pathways had the largest number of differential genes and the greatest level of difference. The KEGG analysis showed that the tumor-related pathways, phosphatidylinositol-3-kinase and protein kinase B, glycoprotein, cytoskeleton, Ras, and Rap1 pathways changed the most significantly.

CONCLUSIONS

RUNX1 is highly expressed in PTC. We conducted high-throughput sequencing to analyze the effect of knocking down RUNX1 on the levels of circRNA and miRNA in PTC. The GO and KEGG analyses revealed that the iron channel-related pathways, endosomal transport, learning and memory, glycosaminoglycan synthesis, and transcriptional disorder-related signaling pathways were enriched.

Expression of CXCR4 and CXCR7 in papillary thyroid carcinoma and adjacent tissues and their relationship with pathologic indicators of tumor aggressiveness

The receptors of chemokines play a significance role in the aggressiveness of tumor. CXCR4/CXCR7 promote metastasis of papillary thyroid carcinoma (PTC). This study examined the expresssion of chemokine receptors CXCR4/CXCR7 in human tissue specimens of PTC and peritumoral nonmalignant tissues. The correlation between CXCR4/CXCR7 and the clinicopathological factors in PTC was also determined. CXCR4/CXCR7 were examined in 115 PTC tissues from 115 patients using immunohistochemistry. Staining intensity was compared with patients and tumor characteristics including gender, age, tumor size, capsule invasion, multifocality, lymph node metastasis, and nature of paracancerous tissue. [Statistics: rank sum test, Spearman rank order correlation test; p < 0.05]. Higher expression rates of CXCR4/CXCR7 exhibited in PTC compared with peritumoral nonmalignant tissues. The expression of them was correlated in cancer and paracancerous specimens. A trend toward higher CXCR4/CXCR7 expression was found among tumors showing positive lymph nodes and capsule invasion, while no association with sex, age, tumor size, and nature of paracancerous tissue. Number of lymph nodes was associated with higher intensity IHC staining for CXCR4/CXCR7. Intense staining for CXCR4 was also associated with multifocality. Expression of CXCR4/CXCR7 by PTCs was correlated with lymph node metastasis and capsule invasion. Although multiple bias, they were thought to play a significance role in the aggressiveness of PTC, which provides potential targets for therapeutic interventions.

Ursolic acid inhibits proliferation, migration and invasion of human papillary thyroid carcinoma cells via CXCL12/CXCR4/CXCR7 axis through cancer-associated fibroblasts

As a pentacyclic triterpenoid compound, Ursolic acid (UA) broads range of biological effects. CXCL12 is a ligand for CXCR4 and CXCR7 proteins on thyroid cancer cells. Here we examined the effects of UA on the proliferation, migration and invasion of papillary thyroid carcinoma (PTCs) in a dose-manner. In addition, UA can reduce the expression levels of CXCR4 and CXCR7 in PTCs. In addition to this direct anticancer pathway, studies have shown that UA can play an anticancer role by affecting the secretion of CXCL12 in cancer-associated fibroblasts (CAFs). After treated with UA, normal fibroblasts and CAFs culture medium (CM) showed differential CXCL12 expression levels. We prepared fibroblast conditioned medium according to the intervention of UA, then cultured TPC-1 and B-CPAP cells with differential CM, and detected significant differences in the proliferation, migration and invasion of cancer cells. Our findings uncovered an indirect anticancer mechanism of UA. This cancer chemopreventive properties is expected to make UA a clinically useful chemopreventive agent.

The mechanism research on the anti-liver fibrosis of emodin based on network pharmacology

AIMS

This study was designated to illustrate the underlying mechanisms of emodin anti-liver fibrosis via network pharmacology and experiment.

METHODS

The TSMCP and Genecards database were applied to screen the relevant targets of emodin or liver fibrosis. The essential target was selected by using Cytoscape to analyze the topological network of potential targets. Furthermore, we constructed a preliminary molecule docking study to explore the binding site by Surflex-Dock suite SYBYL X 2.0. The DAVID database was selected for gene functional annotations and KEGG enrichment analysis. Moreover, we demonstrated the ameliorating effect of emodin on carbon tetrachloride (CCl4 )-induced liver injury in mice. We also verified the network predictions in vitro via various techniques.

RESULTS

The collected results showed that 35 targets were related to emodin, and 6,198 targets were associated with liver fibrosis. The Venn analysis revealed that 17 intersection targets were correlated with emodin anti-liver fibrosis. The topological network analysis suggested that the p53 was the remarkable crucial target. Besides, the molecule docking results showed that emodin could directly interact with p53 by binding the active site residues ASN345, GLN331, and TYR347. Finally, KEGG pathway enrichment results indicated that essential genes were mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathways. Moreover, our study confirmed that emodin alleviated CCl4 -induced liver injury in mice, inducing hepatic stellate cells (HSCs) apoptosis via regulating the p53/ERK/p38 axis.

CONCLUSIONS

This study partially verified the network pharmacological prediction of emodin inducing HSCs cell apoptosis through the p53/ERK/p38 axis.

Bioinformatics analysis of C3 and CXCR4 demonstrates their potential as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

Background

The molecular prognostic biomarkers of clear cell renal cell carcinoma (ccRCC) are still unknown. We aimed at researching the candidate biomarkers and potential therapeutic targets of ccRCC.

Methods

Three ccRCC expression microarray datasets (include GSE14762, GSE66270 and GSE53757) were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) between ccRCC and normal tissues were explored. The potential functions of identified DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). And then the protein - protein interaction network (PPI) was established to screen the hub genes. After that, the expressions of hub genes were identified by the oncomine database. The hub genes’ prognostic values of patients with ccRCC were analyzed by GEPIA database.

Results

A total of 137 DEGs were identified by utilizing the limma package and RRA method, including 63 upregulated genes and 74 downregulated genes. It is found that 137 DEGs were mainly enriched in 82 functional terms and 24 pathways in accordance with the research results. Thirteen highest-scoring genes were screened as hub genes (include 10 upregulated genes and 3 downregulated candidate genes) by utilizing the PPI network and module analysis. Through integrating the oncoming database and GEPIA database, the author found that C3 and CXCR4 are not only overexpressed in ccRCC, but also associated with the prognosis of ccRCC. Further results could reveal that patients with high C3 expression had a poor overall survival (OS), while patients with high CTSS and TLR3 expressions had a good OS; patients with high C3 and CXCR4 expressions had a poor disease-free survival (DFS), while ccRCC patients with high TLR3 expression had a good DFS.

Conclusion

These findings suggested that C3 and CXCR4 were the candidate biomarkers and potential therapeutic targets of ccRCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08525-w.

Identification of differential key biomarkers in the synovial tissue between rheumatoid arthritis and osteoarthritis using bioinformatics analysis

INTRODUCTION/OBJECTIVES

Rheumatoid arthritis (RA) and osteoarthritis (OA) are two common joint diseases with similar clinical manifestations. Our study aimed to identify differential gene biomarkers in the synovial tissue between RA and OA using bioinformatics analysis and validation.

METHOD

GSE36700, GSE1919, GSE12021, GSE55235, GSE55584, and GSE55457 datasets were downloaded from the Gene Expression Omnibus database. A total of 57 RA samples and 46 OA samples were included. The differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Protein-protein interaction (PPI) network of DEGs and the hub genes were constructed and visualized via Search Tool for the Retrieval of Interacting Genes/Proteins, Cytoscape, and R. Selected hub genes were validated via reverse transcription-polymerase chain reaction.

RESULTS

A total of 41 DEGs were identified. GO functional enrichment analysis showed that DEGs were enriched in immune response, signal transduction, regulation of immune response for biological process, in plasma membrane and extracellular region for cell component, and antigen binding and serine-type endopeptidase activity for molecular function. KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction and chemokine signaling pathway. PPI network analysis established 70 nodes and 120 edges and 15 hub genes were identified. The expression of CXCL13, CXCL10, and ADIPOQ was statistically different between RA and OA synovial tissue.

CONCLUSION

Differential expression of CXCL13, CXCL10, and ADIPOQ between RA and OA synovial tissue may provide new insights for understanding the RA development and difference between RA and OA. Key Points • Bioinformatics analysis was used to identify the differentially expressed genes in the synovial tissue between rheumatoid arthritis and osteoarthritis. • CXCL13, CXCL10, and ADIPOQ might provide new insight for understanding the differences between RA and OA.

Diagnosis of thyroid neoplasm using support vector machine algorithms based on platelet RNA-seq

OBJECTIVE

To assess the capacity of support vector machine (SVM) algorithms that are developed based on platelet RNA-seq data in identifying thyroid neoplasm patients and differentiating patients with thyroid adenomas, papillary thyroid cancer and metastasized papillary thyroid cancer.

METHODS

Platelets were collected and isolated from 109 patients and 63 healthy controls. RNA-seq was performed to find transcripts with differential levels. Genes corresponding to these altered transcripts were identified using R packages. All samples were subsampled into a training set and a validation set. Two SVM algorithms were developed and trained with the training set, using the genes with differential transcript levels (GDTLs) as classifiers, and validated with the validation set. GO and KEGG pathway enrichment analysis were performed using the R package clusterProfiler.

RESULTS

We detected 765 GDTLs (442 up-regulated and 323 down-regulated) in platelets of patients and healthy controls. The algorithm identifying thyroid neoplasm patients achieved an accuracy of 97%, with an AUC (area under curve) of 0.998. The other algorithm differentiating patients with multiclass thyroid neoplasms had an average accuracy of 80.5%. GO analysis showed that GDTLs were strongly involved in biological processes such as neutrophil degranulation, neutrophil activation, autophagy and regulation of multi-organism process. KEGG pathway enrichment analysis revealed that GDTLs were mainly enriched in NOD-like receptor signaling pathway and pathways in endocytosis, osteoclast differentiation, human cytomegalovirus infection and tuberculosis.

CONCLUSION

Our results indicated that the combination of SVM algorithms and platelet RNA-seq data allowed for thyroid neoplasm diagnostics and multiclass thyroid neoplasm classification.

The role of PI3K signaling pathway and its associated genes in papillary thyroid cancer

BACKGROUND

One of the well-differentiated types of thyroid cancer is papillary thyroid cancer (PTC), often developed by genetic mutations and radiation.

METHODS

In this study, the public NCBI-GEO database was systematically searched. The eligible datasets were the targets for biomarker discovery associated with PI3K signaling pathway.

RESULTS

Only two datasets were suitable and passed the inclusion criteria. The meta-analysis outcomes revealed eleven upregulation and thirteen downregulation genes differentially expressed between PTC and healthy tissues. Moreover, the outcomes for survival and disease-free rates for each gene were illustrated.

CONCLUSIONS

The present research suggests a panel signature of 24 gene biomarkers in diagnosing the PTC.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

Effect of trachea stiffness on tumor distribution in papillary thyroid microcarcinoma

Biomechanical factors play an important role in tumor distribution, epithelial-mesenchymal transition (EMT), invasion and other important processes. Despite fewer reports investigating biomechanical function in papillary thyroid carcinoma (PTC), a large number of PTC cases are located close to the trachea and the majority of advanced cases of PTC have been associated with invasion of the trachea. However, the effect of trachea stiffness on PTC distribution and growth remains unknown. To clarify this issue, two types of PTC cells (TPC-1 and KTC-1) were seeded on a substrate with different stiffness to observe cell proliferation and movement. To identify the effect of trachea stiffness on the thyroid, two thyroid lobes (left and right) were evenly divided into interior (close to the trachea) and lateral (away from the trachea) parts, based on the vertical line between the trachea and thyroid lateral margin with different von Mises stress values. As PTC originates from papillary thyroid microcarcinoma (PTMC) with a maximum diameter of <1 cm, the present study selected PTMC as the study subject to reflect initial PTC distribution in the thyroid. The association between the percentage of PTMC distribution in different parts of the thyroid and von Mises stress values was analyzed. Both PTC cells exhibited stronger proliferation and mobility on the stiff substrate compared with that on the soft substrate. Furthermore, the results of finite element analysis revealed that the von Mises stress values of the interior parts of the trachea were notably higher compared with that in the lateral parts. PTMC distribution in the interior trachea was notably greater compared with that in the lateral section. There was also an observed association between von Mises stress values and PTMC distribution. In addition, the results of RNA-sequencing and reverse transcription-quantitative PCR demonstrated that three biomechanical genes were overexpressed in PTMC located in the interior section compared with that in adjacent normal tissue, and the related signaling pathways were also activated in these tissues. On the whole, these results indicated that trachea stiffness may supply a suitable biomechanical environment for PTMC growth, and the related biomechanical genes may serve as novel targets for PTMC diagnosis and prognostic estimation.

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.

Syndecan-4 as a Pathogenesis Factor and Therapeutic Target in Cancer

Cancer is an important cause of morbidity and mortality worldwide. Advances in research on the biology of cancer revealed alterations in several key pathways underlying tumorigenesis and provided molecular targets for developing new and improved existing therapies. Syndecan-4, a transmembrane heparan sulfate proteoglycan, is a central mediator of cell adhesion, migration and proliferation. Although several studies have demonstrated important roles of syndecan-4 in cell behavior and its interactions with growth factors, extracellular matrix (ECM) molecules and cytoskeletal signaling proteins, less is known about its role and expression in multiple cancer. The data summarized in this review demonstrate that high expression of syndecan-4 is an unfavorable biomarker for estrogen receptor-negative breast cancer, glioma, liver cancer, melanoma, osteosarcoma, papillary thyroid carcinoma and testicular, kidney and bladder cancer. In contrast, in neuroblastoma and colorectal cancer, syndecan-4 is downregulated. Interestingly, syndecan-4 expression is modulated by anticancer drugs. It is upregulated upon treatment with zoledronate and this effect reduces invasion of breast cancer cells. In our recent work, we demonstrated that the syndecan-4 level was reduced after trastuzumab treatment. Similarly, syndecan-4 levels are also reduced after panitumumab treatment. Together, the data found suggest that syndecan-4 level is crucial for understanding the changes involving in malignant transformation, and also demonstrate that syndecan-4 emerges as an important target for cancer therapy and diagnosis.

Integrated Bioinformatics Analysis Exhibits Pivotal Exercise-Induced Genes and Corresponding Pathways in Malignant Melanoma

Malignant melanoma represents a sort of neoplasm deriving from melanocytes or cells developing from melanocytes. The balance of energy and energy-associated body composition and body mass index could be altered by exercise, thereby directly affecting the microenvironment of neoplasm. However, few studies have examined the mechanism of genes induced by exercise and the pathways involved in melanoma. This study used three separate datasets to perform comprehensive bioinformatics analysis and then screened the probable genes and pathways in the process of exercise-promoted melanoma. In total, 1,627 differentially expressed genes (DEGs) induced by exercise were recognized. All selected genes were largely enriched in NF-kappa B, Chemokine signaling pathways, and the immune response after gene set enrichment analysis. The protein-protein interaction network was applied to excavate DEGs and identified the most relevant and pivotal genes. The top 6 hub genes (Itgb2, Wdfy4, Itgam, Cybb, Mmp2, and Parp14) were identified, and importantly, 5 hub genes (Itgb2, Wdfy4, Itgam, Cybb, and Parp14) were related to weak disease-free survival and overall survival (OS). In conclusion, our findings demonstrate the prognostic value of exercise-induced genes and uncovered the pathways of these genes in melanoma, implying that these genes might act as prognostic biomarkers for melanoma.

Diagnostic and prognostic value of thymidylate synthase expression in breast cancer

Nucleotide metabolism is the driving force of cell proliferation, and thymidylate synthase (TYMS) catalyzes a rate-limiting step in the initial synthesis of nucleotides. Previous studies reported that TYMS activity significantly affected the proliferation of tumour cells. However, the diagnostic and prognostic significance of TYMS expression in breast cancer remains unclear. Here, we used the Breast Cancer Integrative Platform (BCIP) to investigate the relationship between progression and prognosis of breast cancer with TYMS expression, and then verified the database analysis using immunohistochemical staining. Our results indicated TYMS expression was greater in breast cancer than adjacent normal tissues and greater in triple-negative breast cancer (TNBC) than non-TNBC tissues. TYMS expression also had significant positive correlations with histological grade, tumour size, and ER negativity, and PR negativity. The increased copy number of the TYMS gene appears to be the reason for its upregulation in breast cancer. Breast cancer patients with higher TYMS expression had poorer prognosis. Our data suggest that TYMS has potential use as a diagnostic and prognostic marker for breast cancer patients.

Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Background

Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA.

Methods

The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database.

Results

In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes.

Conclusion

The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer

Thyroid cancer (TC) is a frequently occurring malignant tumor with a rising steadily incidence. microRNA (miRNA/miR)‑193a‑3p is an miRNA that is associated with tumors, playing a crucial role in the genesis and progression of various cancers. However, the expression levels of miR‑193a‑3p and its molecular mechanisms in TC remain to be elucidated. The present study aimed to probe the expression of miR‑193a‑3p and its clinical significance in TC, including its underlying molecular mechanisms. Microarray and RNA sequencing data gathered from three major databases, specifically Gene Expression Omnibus (GEO), ArrayExpress and The Cancer Genome Atlas (TCGA) databases, and the relevant data from the literature were used to examine miR‑193a‑3p expression. Meta‑analysis was also conducted to evaluate the association between clinicopathological parameters and miR‑193a‑3p in 510 TC and 59 normal samples from the TCGA database. miRWalk 3.0, and the TCGA and GEO databases were used to predict the candidate target genes of miR‑193a‑3p. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and protein‑protein interaction network enrichment analyses were conducted by using the predicted candidate target genes to investigate the underlying carcinogenic mechanisms. A dual luciferase assay was performed to validate the targeting regulatory association between the most important hub gene cyclin D1 (CCND1) and miR‑193a‑3p. miR‑193a‑3p expression was considerably downregulated in TC compared with in the non‑cancer controls (P<0.001). The area under the curve of the summary receiver operating characteristic was 0.80. Downregulation of miR‑193a‑3p was also significantly associated with age, sex and metastasis (P=0.020, 0.044 and 0.048, respectively). Bioinformatics analysis indicated that a low miR‑193a‑3p expression may augment CCND1 expression to affect the biological processes of TC. In addition, CCND1, as a straightforward target, was validated through a dual luciferase assay. miR‑193a‑3p and CCND1 may serve as prognostic biomarkers of TC. Finally, miR‑193a‑3p may possess a crucial role in the genesis and progression of TC by altering the CCND1 expression.

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.

Targeting Non-Oncogene Addiction: Focus on Thyroid Cancer

Thyroid carcinoma (TC) is the most common malignancy of endocrine organs with an increasing incidence in industrialized countries. The majority of TC are characterized by a good prognosis, even though cases with aggressive forms not cured by standard therapies are also present. Moreover, target therapies have led to low rates of partial response and prompted the emergence of resistance, indicating that new therapies are needed. In this review, we summarize current literature about the non-oncogene addiction (NOA) concept, which indicates that cancer cells, at variance with normal cells, rely on the activity of genes, usually not mutated or aberrantly expressed, essential for coping with the transformed phenotype. We highlight the potential of non-oncogenes as a point of intervention for cancer therapy in general, and present evidence for new putative non-oncogenes that are essential for TC survival and that may constitute attractive new therapeutic targets.

Identification and Validation of Core Genes Involved in the Development of Papillary Thyroid Carcinoma via Bioinformatics Analysis

BACKGROUND

Papillary thyroid carcinoma (PTC) is a common endocrine malignant neoplasm, and its incidence increases continuously worldwide in the recent years. However, efficient clinical biomarkers were still deficient; the present research is aimed at exploring significant core genes of PTC.

METHODS

We integrated three cohorts to identify hub genes and pathways associated with PTC by comprehensive bioinformatics analysis. Expression profiles GSE33630, GSE35570, and GSE60542, including 114 PTC tissues and 126 normal tissues, were enrolled in this research. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to search for the crucial biological behaviors and pathways involved in PTC carcinogenesis. Protein-protein interaction (PPI) network was constructed, and significant modules were deeply studied.

RESULTS

A total of 831 differentially expressed genes (DEGs) were discovered, comprising 410 upregulated and 421 downregulated genes in PTC tissues compared to normal thyroid tissues. PPI network analysis demonstrated the interactions between those DEGs, and top 10 pivotal genes (TGFB1, CXCL8, LRRK2, CD44, CCND1, JUN, DCN, BCL2, ACACB, and CXCL12) with highest degree of connectivity were extracted from the network and verified by TCGA dataset and RT-PCR experiment of PTC samples. Four of the hub genes (CXCL8, DCN, BCL2, and ACACB) were linked to the prognosis of PTC patients and considered as clinically relevant core genes via survival analysis.

CONCLUSION

In conclusion, we propose a series of key genes associated with PTC development and these genes could serve as the diagnostic biomarkers or therapeutic targets in the future treatment for PTC.

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.

Identification and analysis of genes associated with papillary thyroid carcinoma by bioinformatics methods

The molecular mechanism of the occurrence and development of papillary thyroid carcinoma (PTC) has been widely explored, but has not been completely elucidated. The present study aimed to identify and analyze genes associated with PTC by bioinformatics methods. Two independent datasets were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between PTC tissues and matched non-cancerous tissues were identified using GEO2R tool. The common DEGs in the two datasets were screened out by VennDiagram package, and analyzed by the following tools: KOBAS, Database for Annotation, Visualization, and Integrated Discovery (DAVID), Search tool for the retrieval of interacting genes/proteins (STRING), UALCAN and Gene expression profiling interactive analysis (GEPIA). A total of 513 common DEGs, including 259 common up-regulated and 254 common down-regulated genes in PTC, were screened out. These common up-regulated and down-regulated DEGs were most significantly enriched in cytokine–cytokine receptor interaction and metabolic pathways, respectively. Protein–protein interactions (PPI) network analysis showed that the up-regulated genes: FN1, SDC4, NMU, LPAR5 and the down-regulated genes: BCL2 and CXCL12 were key genes. Survival analysis indicated that the high expression of FN1 and NMU genes significantly decreased disease-free survival of patients with thyroid carcinoma. In conclusion, the genes and pathways identified in the current study will not only contribute to elucidating the pathogenesis of PTC, but also provide prognostic markers and therapeutic targets for PTC.

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.