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

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.

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

BACKGROUND

Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.

METHODS

In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.

RESULTS

38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.

CONCLUSION

We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.

Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

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.

Weighted gene co-expression network reveals driver genes contributing to phenotypes of anaplastic thyroid carcinoma and immune checkpoint identification for therapeutic targets

Background

Anaplastic thyroid carcinoma (ATC) is a rare but extremely malignant tumor, with a rapid growth rate and early metastasis thus leading to poor survival of patients. The molecular mechanisms underlying these aggressive traits of ATC remain unknown, which impedes the substantial progress in treatment to prolong ATC patient survival.

Methods

We applied weighted gene co-expression network analysis (WGCNA) to identify ATC-specific modules. The Metascape web and R package clusterProfiler were employed to perform enrichment analysis. Combined with differentially expressed gene analysis, we screened out the most potential driver genes and validated them using receiver operator characteristic (ROC) analysis, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting, immunohistochemistry (IHC), and triple immunofluorescence staining.

Results

A gene expression matrix covering 75 normal samples, 83 papillary thyroid carcinoma (PTC), 26 follicular thyroid carcinoma (FTC), 19 poor-differentiated thyroid carcinoma (PDTC), and 41 ATC tissue samples were integrated, based on which we detected three most potential ATC-specific modules and found that hub genes of these modules were enriched in distinct biological signals. Hub genes in the turquoise module were mainly enriched in mitotic cell cycle, tube morphogenesis, and cell differentiation, hub genes in the magenta module were mainly clustered in the extracellular matrix organization, positive regulation of cell motility, and regulation of Wnt signaling pathway, while hub genes in the blue module primarily participated in the inflammatory response, innate immune response, and adaptive immune response. We showed that 9 top genes, 8 transcription factors (TFs), and 4 immune checkpoint genes (ICGs) were differentially expressed in ATC compared to other thyroid samples and had high diagnostic values for ATC, among which, 9 novel ATC-specific genes (ADAM12, RNASE2, CASP5, KIAA1524, E2F7, MYBL1, SRPX2, HAVCR2, and TDO2) were validated with our clinical samples. Furthermore, we illustrated that ADAM12, RNASE2, and HAVCR2 were predominantly present in the cytoplasm.

Conclusion

Our study identified a set of novel ATC-specific genes that were mainly related to cell proliferation, invasion, metastasis, and immunosuppression, which might throw light on molecular mechanisms underlying aggressive phenotypes of ATC and provide promisingly diagnostic biomarkers and therapeutic targets.

Identification of Human Retinal Organoid Cell Differentiation-Related Genes via Single-Cell Sequencing Data Analysis

Objective. To study the development process of the human retina, we analyzed the development track of main cell types and transitional cell populations, identifying the retinal organoid cell differentiation-related genes (RDRGs). Methods. Single-cell RNA sequencing data (scRNA-Seq) of human retinal organoids were downloaded from Gene Expression Omnibus (GEO) database in this study. Data were processed with quality analysis and analysis of variance. Principal component analysis and $t$ -distributed stochastic neighbor embedding were used to conduct dimension reduction analysis and type annotation for the screened data. Marker genes and RDRGs were identified by differential analysis. Cell differentiation characteristics were determined by trajectory analysis. Enrichment pathways were analyzed by Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG), and functional modules were obtained by protein-protein interaction (PPI) network analysis. Results. iPSCs were mainly located at the root of differentiation trajectory, while neurons and astrocytes were distributed in different branches, respectively. Meanwhile, 220 RDRGs were obtained. They were involved in the biological functions related to vision and visual development, as well as significantly enriched in signaling pathways associated with retinal vascular development and retinal neuroregulation. Protein-protein interaction network construction and functional subnetwork analysis were conducted on RDRGs, and two functional submodules were obtained. The enrichment analysis presented that the two submodules played a vital role in retinal development, visual perception, and cell respiration. Conclusions. This study identified RDRGs and revealed the biological functions involved in these genes, which are expected to provide evidence for researching retinal development and diseases.

Network pharmacology approach and molecular docking to explore the potential mechanism of Wu-Wei-Wen-Tong Chubi capsules in rheumatoid arthritis

Network pharmacology, a holistic approach based on the theory of biological network technology, integrates information from biological systems, drugs, and diseases. Here, this theory was used to predict the targets of Wu-Wei-Wen-Tong Chubi capsule (WWWT) to explore the mechanism in the treatment of rheumatoid arthritis (RA). The ingredients of each herbal medicine in WWWT were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and the active ingredients were screened through bioavailability (OB) ≥30% and drug-likeness (DL) ≥ 0.18. SwissTargetPrection and TCMSP were utilized to calculate and predict the targets of active ingredients. RA-related targets were obtained by searching the Genecards and OMIM databases. The common targets of RA and WWWT were used for gene ontology (GO), KEGG pathway enrichment, protein-protein interaction (PPI) analysis, and molecular docking. And then, four key genes were screened for subsequent verification experiments. In total, 90 active compounds and 330 potential targets of WWWT, 1310 targets of RA, and 135 intersection targets were found. Additionally, GO and pathway analysis identified 4610 significant GO terms and 147 significant KEGG pathways. Based on the PPI network, 11 key genes including IL-6, MMP-9, and TNF-α were screened out for molecular docking. Molecular docking showed that these key genes have good binding activities to active compounds of WWWT such as oroxylin a, kaempferol, and luteolin. Simultaneously, Western blot experimental validation demonstrated that the protein expressions of IL-6, MMP-9, TNF-α, and VEGFA significantly decreased after WWWT treatment. The mechanism of WWWT in treating RA involves multiple active compounds acting on multiple targets, and multiple pathways, which provides an important reference for further elucidation the mechanism and clinical applications of WWWT in the treatment of RA.

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.

An Integrative Multi-Omics Analysis of The Molecular Links between Aging and Aggressiveness in Thyroid Cancers

Aging modifies risk in all cancers, but age is used as a clinical staging criterion uniquely in thyroid cancer (TC). The molecular drivers of age-dependent TC onset and aggressiveness remain poorly understood. We applied an integrative, multi-omics data analysis approach to characterize these signatures. Our analysis reveals that aging, independent of BRAF^V600E mutational status, drives a significant accumulation of aggressiveness-related markers and poorer survival outcomes, most noticeably at age 55 and over. We identified that chromosomal alterations in loci 1p/1q as aging-associated drivers of aggressiveness, and that depleted infiltration with tumor surveillant CD8+T and follicular helper T cells, dysregulation of proteostasis- and senescence-related processes, and ERK1/2 signaling cascade are key features of the aging thyroid and TC onset/progression and aggressiveness in aging patients but not in young individuals. A panel of 23 genes, including those related to cell division such as CENPF, ERCC6L, and the kinases MELK and NEK2, were identified and rigorously characterized as aging-dependent and aggressiveness-specific markers. These genes effectively stratified patients into aggressive clusters with distinct phenotypic enrichment and genomic/transcriptomic profiles. This panel also showed excellent performance in predicting metastasis stage, BRAF^V600E, TERT promoter mutation, and survival outcomes and was superior to the American Thyroid Association (ATA) methodology in predicting aggressiveness risk. Our analysis established clinically relevant biomarkers for TC aggressiveness factoring in aging as an important component.

MiR-16-5p suppresses breast cancer proliferation by targeting ANLN

BACKGROUND

In recent years, gene expression-based analysis has been used for disease biomarker discovery, providing ways for better diagnosis, leading to improvement of clinical treatment efficacy. This study aimed to explore the role of miR-16-5p and ANLN in breast cancer (BC).

METHODS

Cohort datasets of BC were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) and analyzed by bioinformatics tools. qRT-PCR and western blotting were applied to validate ANLN and its protein expression. A dual-luciferase reporter assay was used to prove the regulatory relationship of miR-16-5p and ANLN. Finally, MTT, wound healing, Transwell invasion and flow cytometry analyses of the cell cycle and apoptosis were performed to assess cell proliferation, migration, invasion, cell cycle and apoptosis, respectively.

RESULTS

A total of 195 differentially expressed genes (DEGs) and 50 overlapping microRNAs (miRNAs) were identified. Among these DEGs and miRNAs, ANLN, associated with poor overall survival in BC, overlapped in the GSE29431, GSE42568, TCGA and GEPIA2 databases. Moreover, ANLN was highly expressed, while miR-16-5p was lower in BC cells than in breast epithelial cells. Then, we confirmed that ANLN was directly targeted by miR-16-5p in BC cells. Over-expression of miR-16-5p and knock-down of ANLN remarkably inhibited cell proliferation and migration as well as cell invasion, arrested the cells in G2/M phase and induced apoptosis in BC cells.

CONCLUSIONS

These findings suggest that miR-16-5p restrains proliferation, migration and invasion while affecting cell cycle and promotes apoptosis by regulating ANLN, thereby providing novel candidate biomarkers for the diagnosis and treatment of BC.

Activation of the TGF-β1/Smad signaling by KIF2C contributes to the malignant phenotype of thyroid carcinoma cells

Kinesin family member 2C (KIF2C) has been identified as a potential oncogene in various types of human cancers; however, the role of KIF2C in thyroid cancer has not yet been elucidated. Quantitative real-time polymerase chain reaction and western blotting were employed for gene expression analysis. Cell Counting Kit-8 and ethynyl-2'-deoxyuridine assays were performed to examine cell proliferation. Cell migration and invasion were assessed by wound-healing and transwell invasion assays. Results showed that KIF2C expression was upregulated in thyroid carcinoma cell lines. In addition, upregulation of KIF2C promoted the proliferation, migration, and invasion of thyroid carcinoma cells, while downregulation of KIF2C exerted the opposite effects. Overexpression of KIF2C induced the activation of transforming growth factor-β1 (TGF-β1)/Smad signaling in thyroid carcinoma cells. However, inhibition of TGF-β1/Smad signaling through silencing TGF-β1 attenuated the promoting effects of KIF2C overexpression on the malignant phenotype of thyroid carcinoma cells. Besides, overexpression of TGF-β1 suppressed the inhibitory effect of KIF2C knockdown on the proliferation and metastasis of thyroid carcinoma cells. In conclusion, our findings demonstrated that KIF2C contributed to the malignant phenotype of thyroid carcinoma cells by inducing the activation of TGF-β1/Smad signaling, thus uncovering a novel mechanism for thyroid carcinoma progression.