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

The role of MAPK, notch and Wnt signaling pathways in papillary thyroid cancer: Evidence from a systematic review and meta-analyzing microarray datasets employing bioinformatics knowledge and literature

Papillary thyroid cancer (PTC) is a prevalent kind of thyroid cancer (TC), with the risk of metastasis increasing faster than any other malignancy. So, understanding the role of PTC in pathogenesis requires studying the various gene expressions to find out which particular molecular biomarkers will be helpful. The authors conducted a comprehensive search on the PubMed microarray database and a meta-analysis approach on the remaining ones to determine the differentially expressed genes between PTC and normal tissues, along with the analyses of overall survival (OS) and recurrence-free survival (RFS) rates in patients with PTC. We considered the associated genes with MAPK, Wnt, and Notch signaling pathways. Two GEO datasets have been included in this research, considering inclusion and exclusion criteria. Nineteen genes were found to have higher differences through the meta-analysis procedure. Among them, ten genes were upregulated, and nine genes were downregulated. The expression of 19 genes was examined using the GEPIA2 database, and the Kaplan-Meier plot statistics were used to analyze RFS and the OS rates. We discovered seven significant genes with the validation: PRICKLE1, KIT, RPS6KA5, GADD45B, FGFR2, FGF7, and DTX4. To further explain these findings, it was discovered that the mRNA expression levels of these seven genes and the remaining 12 genes were shown to be substantially linked with the results of the experimental literature investigations on the PTC. Our research found nineteen panels of genes that could be involved in the PTC progression and metastasis and the immune system infiltration of these cancers.

E26 transformation-specific variant 4 as a tumor promotor in human cancers through specific molecular mechanisms

E26 transformation-specific (ETS) variant 4 (ETV4) is an important transcription factor that belongs to the ETS transcription factor family and is essential for much cellular physiology. Recent evidence has revealed that ETV4 is aberrantly expressed in many types of tumors, and its overexpression is related to poor prognosis of cancer patients. Additionally, increasing studies have identified that ETV4 promotes cancer growth, invasion, metastasis, and drug resistance. Mechanistically, the level of ETV4 is regulated by some post-translation modulations in a broad spectrum of cancers. However, little progress has been made to comprehensively summarize the critical roles of ETV4 in different human cancers. Hence, this review mainly focuses on the physiological functions of ETV4 in various human tumors. In addition, the molecular mechanisms of ETV4-mediated cancer progression were elucidated, including how ETV4 modulates its downstream signaling pathways and how ETV4 is regulated by some factors. On this basis, the present review may provide a valuable therapeutics strategy for future cancer treatment by targeting ETV4-related pathways.

Decoding pathogenesis factors involved in the progression of ATLL or HAM/TSP after infection by HTLV-1 through a systems virology study

Background

Human T-cell Leukemia Virus type-1 (HTLV-1) is a retrovirus that causes two diseases including Adult T-cell Leukemia/Lymphoma (ATLL cancer) and HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP, a neurodegenerative disease) after a long latency period as an asymptomatic carrier (AC). There are no obvious explanations about how each of the mentioned diseases develops in the AC carriers. Finding the discriminative molecular factors and pathways may clarify the destiny of the infection.

Methods

To shed light on the involved molecular players and activated pathways in each state, differentially co-expressed modules (DiffCoEx) algorithm was employed to identify the highly correlated genes which were co-expressed differently between normal and ACs, ACs and ATLL, as well as ACs and HAM/TSP samples. Through differential pathway analysis, the dysregulated pathways and the specific disease-genes-pathways were figured out. Moreover, the common genes between the member of DiffCoEx and differentially expressed genes were found and the specific genes in ATLL and HAM/TSP were introduced as possible biomarkers.

Results

The dysregulated genes in the ATLL were mostly enriched in immune and cancer-related pathways while the ones in the HAM/TSP were enriched in immune, inflammation, and neurological pathways. The differential pathway analysis clarified the differences between the gene players in the common activated pathways. Eventually, the final analysis revealed the involvement of specific dysregulated genes including KIRREL2, RAB36, and KANK1 in HAM/TSP as well as LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2 in ATLL.

Conclusion

The identified potential prognostic biomarkers and therapeutic targets are proposed as the most important platers in developing ATLL or HAM/TSP. Moreover, the proposed signaling network clarifies the differences between the functional players in the activated pathways in ACs, ATLL, and HAM/TSP.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-021-01643-8.

MYCT1 Inhibits the Adhesion and Migration of Laryngeal Cancer Cells Potentially Through Repressing Collagen VI

MYCT1, a target of c-Myc, inhibits laryngeal cancer cell migration, but the underlying mechanism remains unclear. In the study, we detected differentially expressed genes (DEGs) from laryngeal cancer cells transfected by MYCT1 using RNA-seq (GSE123275). DEGs from head and neck squamous cell carcinoma (HNSCC) were first screened by comparison of transcription data from the Gene Expression Omnibus (GSE6631) and the Cancer Genome Atlas (TCGA) datasets using weighted gene co-expression network analysis (WGCNA). GO and KEGG pathway analysis explained the functions of the DEGs. The DEGs overlapped between GSE6631and TCGA datasets were then compared with ours to find the key DEGs downstream of MYCT1 related to the adhesion and migration of laryngeal cancer cells. qRT-PCR and Western blot were applied to validate gene expression at mRNA and protein levels, respectively. Finally, the cell adhesion, migration, and wound healing assays were to check cell adhesion and migration abilities, respectively. As results, 39 overlapping genes were enriched in the GSE6631 and TCGA datasets, and most of them revealed adhesion function. Thirteen of 39 genes including COL6 members COL6A1, COL6A2, and COL6A3 were overlapped in GSE6631, TCGA, and GSE123275 datasets. Similar to our RNA-seq results, we confirmed that COL6 is a target of MYCT1 in laryngeal cancer cells. We also found that MYCT1 inhibited the adhesion and migration of laryngeal cancer cells via COL6. These indicate that COL6 is a potential target of MYCT1 and participates the adhesion and migration of laryngeal cancer cells, which provides an important clue for further study on how MYCT1 regulating COL6 in laryngeal cancer progression.

Exosomal hsa-miR-129-2 and hsa-miR-889 from a 6-microRNA signature might be a potential biomarker for predicting prognosis of papillary thyroid carcinoma

BACKGROUND

Papillary thyroid carcinoma (PTC) is a subtype of thyroid cancer with increasing incidence over time.

OBJECTIVE

This study aimed to build a risk score (RS) system for PTC patients.

METHODS

PTC microRNA (miRNA) and messenger RNA (mRNA) expression data were extracted from The Cancer Genome Atlas (TCGA) database. The 491 PTC samples were randomly divided into training and validation sets. Using the limma software package, differentially expressed mRNAs (DEGs) and miRNAs (DEMs) between the tumor and control groups were screened. In order to construct an RS system, a survival package was used to select independent miRNAs related to prognosis. Enrichment analysis was performed, and a miRNA-mRNA co-expression network was constructed. High-throughput sequencing was also used to verify the prognostic miRNAs in exosomes.

RESULTS

We found 1363 DEGs and 171 DEMs between the tumor and control groups. After identifying 26 DEMs that were significantly related to prognosis, 6 independent prognosis-associated miRNAs were selected to build an RS system. The areas under the curves of the overall survival rates of the training, validation, and entire sets were 0.847, 0.772, and 0.819, respectively. By conducting pathway analysis using the miRNA-mRNA co-expression network, one overlapping factor and five overlapping pathways were obtained. In addition, high-throughput sequencing revealed that the hsa-miR-129-2, hsa-miR-548j, hsa-miR-6734, and hsa-miR-889 expression levels in TCGA tumor tissues and exosomes were consistent, and those of hsa-miR-129-2 and hsa-miR-889 between patients and controls were significantly different in exosomes.

CONCLUSION

The six-miRNA RS system in exosomes may comprise independent signatures for predicting PTC patient prognosis.

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

Background

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

Materials and Methods

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

Results

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

Conclusion

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

Gene Expression Patterns Unveil New Insights in Papillary Thyroid Cancer

Background and objectives: Papillary thyroid carcinoma is the most frequent variety of all malignant endocrine tumors. It represents a heterogeneous malignancy with various clinical outcomes, emphasizing the need to identify powerful biomarkers with clinical relevance. Materials and Methods: Available gene expression data (level 3) for thyroid cancers were downloaded from the Cancer Genome Atlas (TCGA), followed by bioinformatic analyses performed on the data set. Results: Based on gene expression analysis, we were able to identify common and specific gene signatures for the three main types of papillary thyroid carcinoma (classical, follicular variant, and tall-cell). The survival rate was not significantly different among the main subtypes, but we were able to identify a biological adhesion signature with impact in patient prognostic. Conclusions: Taken together, the gene expression signature and particular adhesion signature, along with ITGA10 and MSLN in particular, could be used as a prognostic tool with important clinical relevance.