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

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

Objective

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

Methods

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

Results

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

Conclusion

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

Molecular analysis to identify novel potential biomarkers as drug targets in colorectal cancer therapy: an integrated bioinformatics analysis

Colorectal cancer (CRC) is a heterogeneous disease that requires new diagnostic and prognostic markers. Integrated bioinformatics approach to identify novel therapeutic targets associated with CRC. Using GEO2R identified DEGs in CRC, and Funrich software facilitated the visualization of DEGs through Venn diagrams. From a total of 114 enhanced DEGs, potential hub genes were further filtered based on their nodal strength and edges using STRING database. To gain insights into the functional roles of these hub genes, gene ontology and pathway enrichment were conducted thorough g: profiler web server. Subsequently, overall survival plots from GEPIA and oncogenic predictive functions like mRNA expressions for stages and nodal metastasis were employed to identify hub genes in CRC patient samples. Additionally, the cBioPortal and HPA databases also revealed genetic alterations and expression levels in these hub genes in CRC patients, further supporting their involvement in colorectal cancer. Gene expression by RT-PCR shows upregulation of hub genes in HT-29 cells. Finally, our integrated bioinformatic analysis revealed that ABCE1, AURKA, HSPD1, PHKA1, CDK4, and YWHAE as hub genes with potential oncogenic roles in CRC. These genes hold promise as diagnostic and prognostic markers for colorectal tumorigenesis, providing insights into targeted therapies for improved patient outcomes.

Single-cell and bulk RNA sequencing reveal heterogeneity and diagnostic markers in papillary thyroid carcinoma lymph-node metastasis

PURPOSE

Papillary thyroid carcinoma (PTC) is characterized by lymph-node metastasis (LNM), which affects recurrence and prognosis. This study analyzed PTC LNM by single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) to find diagnostic markers and therapeutic targets.

METHODS

ScRNA-seq data were clustered and malignant cells were identified. Differentially expressed genes (DEGs) were identified in malignant cells of scRNA-seq and bulk RNA-seq, respectively. PTC LNM diagnostic model was constructed based on intersecting DEGs using glmnet package. Next, PTC samples from 66 patients were used to validate the two most significant genes in the diagnostic model, S100A2 and type 2 deiodinase (DIO2) by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and immunohistochemical (IHC). Further, the inhibitory effect of DIO2 on PTC cells was verified by cell biology behavior, western blot, cell cycle analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, and xenograft tumors.

RESULTS

Heterogeneity of PTC LNM was demonstrated by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis. A total of 19 differential genes were used to construct the diagnostic model. S100A2 and DIO2 differ significantly at the RNA (p < 0.01) and protein level in LNM patient tissues (p < 0.001). And differed in PTC tissues with different pathologic typing (p < 0.001). Further, EdU (p < 0.001) and cell biology behavior revealed that PTC cells overexpressed DIO2 had reduced proliferative capacity. Cell cycle proteins were reduced and cells are more likely to be stuck in G2/M phase (p < 0.001).

CONCLUSIONS

This study explored the heterogeneity of PTC LNM using scRNA-seq. By combining with bulk RNA-seq data, diagnostic markers were explored and the model was established. Clinical diagnostic efficacy of S100A2 and DIO2 was validated and the treatment potential of DIO2 was discovered.

Mechanism of immune escape mediated by receptor tyrosine kinase KIT in thyroid cancer

OBJECTIVE

Thyroid cancer (TC) is one of the fastest-growing malignant tumors. This study sought to explore the mechanism of immune escape mediated by receptor tyrosine kinase (KIT) in TC.

METHODS

The expression microarray of TC was acquired through the GEO database, and the difference analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis were carried out. KIT levels in TC cell lines (K1/SW579/BCPAP) and human normal thyroid cells were detected using reverse transcription quantitative polymerase chain reaction and western blot analysis. TC cells were transfected with overexpressed (oe)-KIT and CD8+ T cells were cocultured with SW579 cells. Subsequently, cell proliferation, migration, and invasion abilities, CD8+ T cell proliferation, cytokine levels (interferon-γ [IFN-γ]/tumor necrosis factor-α [TNF-α]) were determined using colony formation assay, Transwell assays, flow cytometry, and enzyme-linked immunosorbent assay. The phosphorylation of MAPK pathway-related protein (ERK) was measured by western blot analysis. After transfection with oe-KIT, cells were treated with anisomycin (an activator of the MAPK pathway), and the protein levels of p-ERK/ERK and programmed death-ligand 1 (PD-L1) were detected.

RESULTS

Differentially expressed genes (N = 2472) were obtained from the GEO database. KIT was reduced in TC samples and lower in tumor cells than those in normal cells. Overexpression of KIT inhibited immune escape of TC cells. Specifically, the proliferation, migration, and invasion abilities of TC cells were lowered, the proliferation level of CD8+ T cells was elevated, and IFN-γ and TNF-α levels were increased. KIT inhibited the activation of the MAPK pathway in TC cells and downregulated PD-L1.

CONCLUSION

KIT suppressed immune escape of TC by blocking the activation of the MAPK pathway and downregulating PD-L1.

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.

Expression pattern and diagnostic value of ferroptosis-related genes in acute myocardial infarction

BACKGROUND

Ferroptosis is a form of regulatory cell death (RCD) caused by iron-dependent lipid peroxidation. The role of ferroptosis in the process of acute myocardial infarction (AMI) is still unclear and requires further study. Therefore, it is helpful to identify ferroptosis related genes (FRGs) involved in AMI and explore their expression patterns and molecular mechanisms.

METHODS

The AMI-related microarray datasets GSE66360 and GSE61144 were obtained using the Gene Expression Omnibus (GEO) online database. GO annotation, KEGG pathway enrichment analysis and Protein-protein interaction (PPI) analysis were performed for the common significant differential expression genes (CoDEGs) in these two datasets. The FRGs were obtained from the FerrDb V2 and the differentially expressed FRGs were used to identify potential biomarkers by receiver operating characteristic (ROC) analysis. The expression of these FRGs was verified using external dataset GSE60993 and GSE775. Finally, the expression of these FRGs was further verified in myocardial hypoxia model.

RESULTS

A total of 131 CoDEGs were identified and these genes were mainly enriched in the pathways of "inflammatory response," "immune response," "plasma membrane," "receptor activity," "protein homodimerization activity," "calcium ion binding," "Phagosome," "Cytokine-cytokine receptor interaction," and "Toll-like receptor signaling pathway." The top 7 hub genes ITGAM, S100A12, S100A9, TLR2, TLR4, TLR8, and TREM1 were identified from the PPI network. 45 and 14 FRGs were identified in GSE66360 and GSE61144, respectively. FRGs ACSL1, ATG7, CAMKK2, GABARAPL1, KDM6B, LAMP2, PANX2, PGD, PTEN, SAT1, STAT3, TLR4, and ZFP36 were significantly differentially expressed in external dataset GSE60993 with AUC ≥ 0.7. Finally, ALOX5, CAMKK2, KDM6B, LAMP2, PTEN, PTGS2, and ULK1 were identified as biomarkers of AMI based on the time-gradient transcriptome dataset of AMI mice and the cellular hypoxia model.

CONCLUSION

In this study, based on the existing datasets, we identified differentially expressed FRGs in blood samples from patients with AMI and further validated these FRGs in the mouse time-gradient transcriptome dataset of AMI and the cellular hypoxia model. This study explored the expression pattern and molecular mechanism of FRGs in AMI, providing a basis for the accurate diagnosis of AMI and the selection of new therapeutic targets.

Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Network and Novel Potential Biomarkers in Acute Myocardial Infarction

Background

Circular RNA (circRNA) plays an important role in the regulation of gene expression and the occurrence of human diseases. However, studies on the role of circRNA in acute myocardial infarction (AMI) are limited. This study was performed to explore novel circRNA-related regulatory networks in AMI, aiming to better understand the molecular mechanism of circRNAs involvement in AMI and provide basis for further scientific research and clinical decision-making.

Methods

The AMI-related microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database. After differential expression analysis, the regulatory relationships between these DERNAs were identified by online databases circBank, circInteractome, miRDB, miRWalk, Targetscan, and then two circRNA-miRNA-mRNA regulatory networks were constructed. Differentially expressed genes (DEGs) in this network were selected followed by enrichment analysis and protein–protein interaction (PPI) analysis. Hub genes were identified using Cytohubba plug-in of Cytoscape software. Hub genes and hub gene-related miRNAs were used for receiver operating characteristic curve (ROC) analysis to identify potential biomarkers. The relative expression levels of these biomarkers were further assessed by GSE31568 (miRNA) and GSE66360 (mRNA). Finally, on the basis of the above analysis, myocardial hypoxia model was constructed to verify the expression of Hub genes and related circRNAs.

Results

A total of 83 DEcircRNAs, 109 CoDEmiRNAs and 1204 DEGs were significantly differentially expressed in these datasets. The up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. These circRNA-related DEGs were mainly enriched in the terms of “FOXO signaling pathway,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway.” The top 10 hub genes ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1 were identified from the PPI network. Hub genes GPM6A, SRSF1, ANK2 and hub gene-related circRNAs hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, and hsa_circ_0045519 were identified as potential biomarkers in AMI.

Conclusion

In this study, the potential circRNAs associated with AMI were identified and two circRNA-miRNA-mRNA regulatory networks were constructed. This study explored the mechanism of circRNA involvement in AMI and provided new clues for the selection of new diagnostic markers and therapeutic targets for AMI.

hsa_circ_0000285 facilitates thyroid cancer progression by regulating miR-127-5p/CDH2

Thyroid cancer (THCA) is a leading endocrine cancer and becomes the fifth most commonly diagnosed malignancy in females. It is confirmed that circular RNAs (circRNAs) perform regulatory potencies in the pathological progress of THCA. Our purpose was to certify the trait of hsa_circ_0000285 (circ_0000285) and investigate its modulatory mechanism in THCA progression. We identified the expression profile of hsa_circ_0000285 in THCA by conducting qRT-PCR assay. Therewith, the potential of hsa_circ_0000285 in THCA development was determined with a set of functional experiments, including CCK-8, wound healing assay, Western blot, and xenograft model. The molecular mechanism underlying hsa_circ_0000285 was investigated with bioinformatic analysis, RIP and dual-luciferase reporter experiments. As opposed to normal samples and cells, hsa_circ_0000285 level was overtly increased in THCA specimens and cells. The downregulation of hsa_circ_0000285 weakened the proliferative and migratory capacity of THCA cells and promoted cell apoptosis. In addition, hsa_circ_0000285 silence suppressed the tumor growth of xenograft model mice in vivo. Notably, we demonstrated that hsa_circ_0000285 might target miR-127-5p/CDH2 axis in THCA. Afterward, our findings manifested that miR-127-5p attenuation blocked the function of hsa_circ_0000285 depletion in THCA cells. In the final step, CDH2 was proven to mediate the repressive potency of miR-127-5p in the malignant behaviors of THCA. Mechanistically, hsa_circ_0000285 induced the development of THCA via functioning as a competing endogenous RNA (ceRNA) of miR-127-5p to enhance CDH2 expression, which provided a new perspective for THCA therapy.

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.

Identification of a Four-Gene Signature for Determining the Prognosis of Papillary Thyroid Carcinoma by Integrated Bioinformatics Analysis

Purpose

Although well-differentiated papillary thyroid carcinoma (PTC) has an indolent nature and usually an excellent prognosis, some patients experience disease recurrence or death. The aim of this study was to identify prognostic markers to stratify PTC patients.

Patients and Methods

Eight gene-expression profiles (GSE3467, GSE3678, GSE5364, GSE27155, GSE33630, GSE53157, GSE60542, and GSE104005) were obtained from the Gene Expression Omnibus and used to analyze differentially expressed genes (DEGs) between PTC tissues and non-tumor tissues. Univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed to identify prognostic genes and establish a risk-score model based on the integrated DEGs. Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves were used to validate the prognostic performance of the risk score. A nomogram was constructed based on The Cancer Genome Atlas dataset and Multivariable Cox regression analysis.

Results

A total of 165 upregulated and 207 downregulated DEGs were screened. A four-gene signature including PAPSS2, PCOLCE2, PTX3, and TGFBR3 was identified. The risk-score model showed a strong diagnosis performance for identifying patients with a poor prognosis. KM analysis showed that patients with low risk scores had a significantly more favorable overall survival (OS) than those with high risk scores (p = 0.0002). ROC curves based on the four-gene signature showed better performances in predicting 1-, 3-, and 5-year survival than did the American Joint Committee on Cancer staging system (area under the curve: 0.86 vs 0.84, 0.80 vs 0.63, and 0.79 vs 0.73, respectively). Furthermore, when combined with age and tumor status from the nomogram, the four-gene signature achieved a good performance in guiding postoperative follow-up surveillance of patients with PTC.

Conclusion

The four-gene signature was found to be a novel and reliable biomarker with great potential for clinical application in risk stratification and OS prediction in patients with PTC.

Identification of SLITRK6 as a Novel Biomarker in hepatocellular carcinoma by comprehensive bioinformatic analysis

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the adult liver and morbidity are increasing in recent years, however, there is still no effective strategy to prevent and diagnose HCC. Therefore, it is urgent to research the effective biomarker to predict clinical outcomes of HCC tumorigenesis. In the current study, differentially expressed genes in HCC and normal tissues were investigated using the Gene Expression Omnibus (GEO) dataset GSE144269 and The Cancer Genome Atlas (TCGA). Gene differential expression analysis and weighted correlation network analysis (WGCNA) methods were used to identify nine and 16 key gene modules from the GEO dataset and TCGA dataset, respectively, in which the green module in the GEO dataset and magenta module in TCGA were significantly correlated with HCC occurrence. Third, the enrichment score of gene function annotation results showed that these two key modules focus on the positive regulation of inflammatory response and cell differentiation, etc. Besides, PPI network analysis, mutation analysis, and survival analysis found that SLITRK6 had high connectivity, and its mutation significantly impacted overall survival. In addition, SLITRK6 was found to be low expressed in tumor cells. To summarize, SLITRK6 mutation was found to significantly affect the occurrence and prognosis of HCC. SLITRK6 was confirmed as a new potential gene target for HCC, which may provide a new theoretical basis for personalized diagnosis and chemotherapy of HCC in the future.

Identification of prognostic biomarkers related to the tumor microenvironment in thyroid carcinoma

Thyroid Carcinoma (THCA) is the most common endocrine tumor that is mainly treated using surgery and radiotherapy. In addition, immunotherapy is a recently developed treatment option that has played an essential role in the management of several types of tumors. However, few reports exist on the use of immunotherapy to treat THCA. The study downloaded the miRNA, mRNA and lncRNA data for THCA patients from the TCGA database ( https://portal.gdc.cancer.gov/ ). Thereafter, the tumor samples were divided into cold and hot tumors, based on the immune score of the tumor microenvironment. Moreover, the differentially expressed lncRNAs and miRNAs were obtained. Finally, the study jointly constructed a ceRNA network through differential analysis of the mRNA data for cold and hot tumors. The study first assessed the level of immune infiltration in the THCA tumor microenvironment then divided the samples into cold and hot tumors, based on the immune score. Additionally, a total of 568 up-regulated and 412 down-regulated DEGs were screened by analyzing the differences between hot and cold tumors. Thereafter, the study examined the differentially expressed genes for lncRNA and miRNA. The results revealed 629 differentially expressed genes related to lncRNA and 114 associated with miRNA. Finally, a ceRNA network of the differentially expressed genes was constructed. The results showed a five-miRNA hubnet, i.e., hsa-mir-204, hsa-mir-128, hsa-mir-214, hsa-mir-150 and hsa-mir-338. The present study identified the immune-related mRNA, lncRNA and miRNA in THCA then constructed a ceRNA network. These results are therefore important as they provide more insights on the immune mechanisms in THCA. The findings also provides additional information for possible THCA immunotherapy.

Differentially Expressed Genes and Enriched Signaling Pathways in the Adipose Tissue of Obese People

As the prevalence of obesity increases, so does the occurrence of obesity-related complications, such as cardiovascular and cerebrovascular diseases, diabetes, and some cancers. Increased adipose tissue is the main cause of harm in obesity. To better understand obesity and its related complications, we analyzed the mRNA expression profiles of adipose tissues from 126 patients with obesity and 275 non-obese controls. Using an integrated bioinformatics method, we explored the functions of 113 differentially expressed genes (DEGs) between them. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses revealed that upregulated DEGs were enriched in immune cell chemotaxis, complement-related cascade activation, and various inflammatory signaling pathways, while downregulated DEGs enriched in nutrient metabolism. The CIBERSORT algorithm indicated that an increase in macrophages may be the main cause of adipose tissue inflammation, while decreased γδ T cells reduce sympathetic action, leading to dysregulation of adipocyte thermogenesis. A protein-protein interaction network was constructed using the STRING database, and the top 10 hub genes were identified using the cytoHubba plug-in in Cytoscape. All were confirmed to be obesity-related using a separate dataset. In addition, we identified chemicals related to these hub genes that may contribute to obesity. In conclusion, we have successfully identified several hub genes in the development of obesity, which provide insights into the possible mechanisms controlling obesity and its related complications, as well as potential biomarkers and therapeutic targets for further research.

Identification of a Six Gene Prognosis Signature for Papillary Thyroid Cancer Using Multi-Omics Methods and Bioinformatics Analysis

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer. PTC is typically curable with an excellent survival rate; however, some patients experience disease recurrence or death. This study aimed to discover potential key genes and signaling pathways of PTC, which could provide new insights for thyroid lesions. Four GEO microarray datasets were integrated to screen for candidate genes involved in PTC progression. A total of 164 upregulated and 168 downregulated differentially expressed genes (DEGs) were screened. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes were used in pathway enrichment analyses for DEGs. A protein-protein interaction network was then built and analyzed utilizing STRING and Cytoscape, followed by the identification of 13 hub genes by cytoHubba. CDH3, CTGF, CYR61, OGN, FGF13, and CHRDL1 were selected through survival analyses. Furthermore, immune infiltration, mutations and methylation analysis indicated that these six hub genes played vital roles in immune surveillance and tumor progression. ROC and K-M plots showed that these genes had good prognostic values for PTC which was validated by TCGA dataset. Finally, GSEA for a single hub gene revealed that each candidate hub gene had close associations with PTC development. These findings provided new insights into PTC pathogenesis and identified six candidate gene prognosis signature for PTC.