Association between collagen type XI α1 gene polymorphisms and papillary thyroid cancer in a Korean population

Development of a thyroid cancer prognostic model based on the mitophagy-associated differentially expressed genes

BACKGROUND

The prevalence of thyroid cancer (ThyC), a frequent malignant tumor of the endocrine system, has been rapidly increasing over time. The mitophagy pathway is reported to play a critical role in ThyC onset and progression in many studies. This research aims to create a mitophagy-related survival prediction model for ThyC patients.

METHODS

Genes connected to mitophagy were found in the GeneCards database. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided information on the expression patterns of ThyC-related genes. To identify differentially expressed genes (DEGs), R software was employed. The prognostic significance of each DEG was assessed using the prognostic K-M curve. The prognostic model was built using LASSO, ROC, univariate, and multivariate Cox regression analyses. Finally, a nomogram model was developed to predict the survival outcome of ThyC patients in the clinical setting.

RESULTS

Through differential analysis, functional enrichment analysis, and protein-protein interaction (PPI) network analysis, we screened 10 key genes related to mitophagy in ThyC. The risk model was eventually developed using LASSO and Cox regression analyses based on the six DEGs related to mitophagy. An altered expression level of a mitophagy-related prognostic gene, GGCT, was found to be the most significant one, according to the KM survival curve analysis. An immunohistochemical (IHC) investigation revealed that ThyC tissues expressed higher levels of GGCT than normal thyroid tissues. The ROC curve verified the satisfactory performance of the model in survival prediction. Multivariate Cox regression analysis showed that the pathological grade, residual tumor volume, and initial tumor lesion type were significantly linked to the prognosis. Finally, we created a nomogram to predict the overall survival rate of ThyC patients at 3-, 5-, and 7- year time points.

CONCLUSION

The nomogram risk prediction model was developed to precisely predict the survival rate of ThyC patients. The model was validated based on the most significant DEG GGCT gene expression in ThyC. This model may serve as a guide for the creation of precise treatment plans for ThyC patients.

In Silico Analysis of Collagens Missense SNPs and Human Abnormalities

Collagens are the most abundant proteins in the extra cellular matrix/ECM of human tissues that are encoded by different genes. There are single nucleotide polymorphisms/SNPs which are considered as the most useful biomarkers for some disease diagnosis or prognosis. The aim of this study is screening and identifying the functional missense SNPs of human ECM-collagens and investigating their correlation with human abnormalities. All of the missense SNPs were retrieved from the NCBI SNP database and screened for a global frequency of more than 0.1. Seventy missense SNPs that met the screening criteria were characterized for functional and stability impact using six and three protein analysis tools, respectively. Next, HOPE and geneMANIA analysis tools were used to show the effect of SNPs on three-dimensional structure (3D) and physical interaction of proteins. Results showed that 13 missense SNPs (rs2070739, rs28381984, rs13424243, rs1800517, rs73868680, rs12488457, rs1353613, rs59021909, rs9830253, rs2228547, rs3753841, rs2855430, and rs970547), which are in nine different collagen genes, affect the structure and function of different collagen proteins. Among these polymorphisms, COL4A3-rs13424243 and COL6A6-rs59021909 were predicted as the most effective ones. On the other hand, designed mutated and native 3D of rs13424243 variant illustrated that it can disturb the protein motifs. Also, geneMANIA predicted that COL4A3 and COL6A6 are interacting with some proteins including: DDR1, COL6A1, COL11A2 and so on. Based on our findings, ECM-collagens functional SNPs are important and may be considered as a risk factor or molecular marker for human disorders in the future studies.

Collagen XI alpha 1 chain, a potential therapeutic target for cancer

Extracellular matrix (ECM) plays an important role in the progression of cancer. Collagen is the most abundant component in ECM, and it is involved in the biological formation of cancer. Although type XI collagen is a minor fibrillar collagen, collagen XI alpha 1 chain (COL11A1) has been found to be upregulated in a variety of cancers including ovarian cancer, breast cancer, thyroid cancer, pancreatic cancer, non-small-cell lung cancer, and transitional cell carcinoma of the bladder. High levels of COL11A1 usually predict poor prognosis, while COL11A1 is related to angiogenesis, invasion, and drug resistance of cancer. However, little is known about the specific mechanism by which COL11A1 regulates tumor progression. Here, we have organized and summarized the recent developments regarding elucidation of the relationship between COL11A1 and various cancers, as well as the interaction between COL11A1 and intracellular signaling pathways. In addition, we have selected therapeutic agents targeting COL11A1. All these indicate the possibility of using COL11A1 as a target for cancer treatment.

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.

Novel somatic alterations underlie Chinese papillary thyroid carcinoma

To characterize the somatic alterations of papillary thyroid carcinomas (PTC) in Chinese patients, we performed the next-generation-sequencing (NGS) study of the tumor-normal pairs of DNA and RNA samples extracted from 16 Chinese PTC patients. The whole genome sequencing (WGS) and transcriptome sequencing (RNA-seq) were conducted for 6 patients who were either current or former smokers and the whole exome sequencing (WES) and RNA-seq were conducted for another 10 patients who were never smokers. The NGS data were analyzed to identify somatic alteration events that may underlie PTC in Chinese patients. We identified a number of PTC driver genes harboring somatic driver mutations with significant functional impact such as COL11A1, TP53, PLXNA4, UBA1, AHNAK, CSMD2 and TTLL5 etc. Significant driver pathways underlying PTC were found, namely, the metabolic pathway, the pathway in cancer, the olfactory transduction pathway and the calcium signaling pathway. In addition, this study revealed genes with significant somatic copy number aberrations and corresponding somatic gene expression changes in PTC tumors, the most promising ones being BRD9, TRIP13, FZD3, and TFDP1 etc. We also identified several structural variants of PTCs, especially the novel in-frame fusion proteins such as TRNAU1AP-RCC1, RAB3GAP1-R3HDM1, and ENAH-ZSWIM5. Our study provided a list of novel PTC candidate genes with somatic alterations that may function as biomarkers for PTC in Chinese patients. The follow-up mechanism studies may be conducted based on the findings from this study.

Prediction of novel target genes and pathways involved in tall cell variant papillary thyroid carcinoma

BACKGROUND

Tall cell variant papillary thyroid carcinoma (TCPTC) is reportedly associated with aggressive clinicopathological parameters and poor outcomes; however, the molecular mechanisms underlying TCPTC remain poorly understood.

METHODS

The gene mutation types and mRNA expression profiles of patients with TCPTC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified. Pathways in the interaction network and the diagnostic approaches of candidate markers for TCPTC were investigated.

RESULTS

BRAF mutation was particularly prevalent in TCPTC with a mutation frequency of 78%. TCPTC was associated with a patient age >45 years, tumor multifocality, extrathyroidal extension, a higher T stage, advanced AJCC TNM stages, BRAF V600E mutation, and poor disease-free survival. We identified 4138 TCPTC-related DEGs and 301 TCPTC-specific DEGs. Intriguingly, the gene expression pattern revealed that the dysregulated levels of both putative oncogenes and tumor suppressors in TCPTC were higher than those in classical/conventional variant PTC (cPTC). Functional enrichment analyses revealed that these DEGs were involved in several cancer-related pathways. A protein-protein interaction (PPI) network was constructed from the 301 TCPTC-specific DEGs, and 3 subnetworks, and 8 hub genes were verified. Receiver operating characteristic (ROC) analyses revealed that 6 hub genes, including COL5A1, COL1A1, COL10A1, COL11A1, CCL20, and CXCL5, could be used not only for the differential diagnosis of PTC from normal samples, but also for the differential diagnosis of TCPTC from cPTC samples.

CONCLUSIONS

Our study might provide further insights into the investigations of the tumorigenesis mechanism of TCPTC and assists in the discovery of novel candidate diagnostic markers for TCPTC.

A Comprehensive Meta-analysis of Case-Control Association Studies to Evaluate Polymorphisms Associated with the Risk of Differentiated Thyroid Carcinoma

BACKGROUND

Linkage analyses and association studies suggested that inherited genetic variations play a role in the development of differentiated thyroid carcinoma (DTC).

METHODS

We combined the results from a genome-wide association study (GWAS) performed by our group and from published studies on DTC. With a first approach, we evaluated whether a SNP published as associated with the risk of DTC could replicate in our GWAS (using FDR as adjustment for multiple comparisons). With the second approach, meta-analyses were performed between literature and GWAS when both sources suggested an association, increasing the statistical power of the analysis.

RESULTS

rs1799814 (CYP1A1), rs1121980 (FTO), and 3 SNPs within 9q22 (rs965513, rs7048394, and rs894673) replicated the associations described in the literature. In addition, the meta-analyses between literature and GWAS revealed 10 more SNPs within 9q22, six within FTO, two within SOD1, and single variations within HUS1, WDR3, UGT2B7, ALOX12, TICAM1, ATG16L1, HDAC4, PIK3CA, SULF1, IL11RA, VEGFA, and 1p31.3, 2q35, 8p12, and 14q13.

CONCLUSION

This analysis confirmed several published risk loci that could be involved in DTC predisposition.

IMPACT

These findings provide evidence for the role of germline variants in DTC etiology and are consistent with a polygenic model of the disease. Cancer Epidemiol Biomarkers Prev; 25(4); 700-13. ©2016 AACR.

Integrated regulatory mechanisms of miRNAs and targeted genes involved in colorectal cancer

PURPOSE

CRC (Colorectal cancer) is a lethal cancer for death worldwide and the underlying pathological mechanisms for CRC progression remain unclear. We aimed to explore the regulatory mechanism of CRC and provide novel biomarkers for CRC screening.

METHODS

Downloading from GEO (Gene Expression Omnibus) database, Microarray data GSE44861 were consisted of 111 colon tissues samples including 55 from adjacent noncancerous tissues and 56 from tumors tissues. After data pre-processing, up- and down regulated DEGs (differentially expressed genes) were identified using Bayes moderated t-test. Then DIVAD (Database for Annotation, Visualization and Integrated Discovery) was recruited to perform functional analysis for DEGs. Thereafter, PPI (protein-protein interaction) network was constructed by mapping DEGs into STRING (Search Tool for the Retrieval of Interacting Genes) database. Further, PPI modules were constructed and the protein domains of DEGs in the modules were analyzed. Moreover, miRNA regulatory network was established through GSEA (gene set enrichment analysis) method.

RESULTS

In summary, 96 up- and 212 down-regulated DEGs were identified. Totally, ten DEGs with high degrees in the constructed PPI network were selected, in which COLL1A1, PTGS2 and ASPN were also identified as crucial genes in PPI modules. Furthermore, COLL1A1 was predicted to be targeted by miR-29, while PTGS2 and ASPN were both predicted to be regulated by miR-101 and miR-26.

CONCLUSION

COL11A1 might involve in the progression of CRC via being targeted by miR-29, whereas PTGS2 and ASPN were both regulated by miR-101 and miR-26. Moreover, ASPN may be supposed as a novel biomarker for CRC detection and prevention.

Tumor matrix protein collagen XIα1 in cancer

The extracellular matrix is increasingly recognized as an essential player in cancer development and progression. Collagens are one of the most important components of the extracellular matrix, and have themselves been implicated in many aspects of neoplastic transformation. Collagen XI is a minor collagen whose main physiologic function is to regulate the diameter of major collagen fibrils. The α1 chain of collagen XI (colXIα1) has known pathogenic roles in several musculoskeletal disorders. Recent research has highlighted the importance of colXIα1 in many types of cancer, including its roles in metastasis, angiogenesis, and drug resistance, as well as its potential utility in screening tests and as a therapeutic target. High levels of colXIα1 overexpression have been reported in multiple expression profile studies examining differences between cancerous and normal tissue, and between beginning and advanced stage cancer. Its expression has been linked to poor progression-free and overall survival. The consistency of these data across cancer types is particularly striking, including colorectal, ovarian, breast, head and neck, lung, and brain cancers. This review discusses the role of collagen XIα1 in cancer and its potential as a target for cancer therapy.

A missense polymorphism (rs11895564, Ala380Thr) of integrin alpha 6 is associated with the development and progression of papillary thyroid carcinoma in Korean population

Purpose

Integrins play crucial roles in the pathogenesis of papillary thyroid carcinoma (PTC). The aim of this study was to investigate whether two single nucleotide polymorphisms (SNPs) (rs2141698, -1687A/G; rs11895564, Ala380Thr) of the integrin alpha 6 (ITGA6) gene are associated with the development and clinicopathologic characteristics of PTC such as the size (<1 cm and ≥1 cm), number (unifocality and multifocality), location (one lobe and both lobes), extrathyroid invasion, and cervical lymph node metastasis.

Methods

We enrolled 104 PTC patients and 318 control subjects. Genotypes of each SNP were determined by direct sequencing. SNPStats, SNPAnalyzer, and Helixtree programs were used to evaluate odds ratios (ORs), 95% confidence intervals (CIs), and P-values. Multiple logistic regression models were performed to analyze genetic data.

Results

A missense SNP rs11895564 was associated with the development of PTC. The A allele frequency of rs11895564 was higher in PTC patients than in controls (13.5% vs. 7.1%; P = 0.005; OR, 2.04; 95% CI, 1.24 to 3.37). In the clinicopathologic characteristics, the A allele frequency of rs11895564 showed difference in the size (19.6% in <1 cm vs. 6.9% in ≥1 cm; P = 0.010; OR, 0.30; 95% CI, 0.12 to 0.75) and number (8.5% in unifocality vs. 20.8% in multifocality; P = 0.015; OR, 2.85; 95% CI, 1.23 to 6.59) of PTC.

Conclusion

These results suggest that the A allele of rs11895564 (Ala380Thr) in ITGA6 may be a risk factor of PTC, and also contribute to the progression of PTC in the Korean population.