Development of a risk scoring system for patients with papillary thyroid cancer

A rare incidence of mandibular metastasis of papillary thyroid carcinoma: A case report and review of literature

Papillary Thyroid Carcinoma (PTC) primarily metastasizes via regional lymphatics making its spread to the oral cavity exceedingly rare. Although this disease remains the most common endocrine malignancy, comprising roughly 85%-90% of all thyroid cancers, its occurrence within the oral cavity happens in less than 1% of oral malignancies. This study identifies a case involving a 77-year-old male with a history of well-differentiated PTC that was initially treated with a total thyroidectomy and adjuvant radioactive iodine. Five years after his initial treatment, surveillance imaging demonstrated a 3 cm expansile mass of the left mandible. An incisional biopsy then confirmed the diagnosis of metastatic PTC. A segmental resection of his mandible was completed with final reconstruction utilizing a fibula free flap. Given the limited number of cases involving metastatic spread of PTC to the oral cavity, a standardized treatment algorithm does not exist. Thus, this case serves to provide a documented report of this rare occurrence and to review literature that may help other clinicians treat patients with this malignancy type. There remains a need for future studies to create risk stratification models for patients with metastatic PTC that consider margin analysis, genetic characteristics, and risk factors to tailor individual treatment plans.

A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma

Metabolic reprogramming is one of the cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested based on datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. We used Kaplan-Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index (C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, and CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and therapeutic reaction. We also performed pan-cancer analyses of model genes to predict the mutational value in various cancers. Our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. This research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.

Risk stratification of patients with right-sided colorectal cancer based on the tumor-infiltrating M1 macrophage

The homing of M1 and M2 macrophages may play distinct roles in the tumor microenvironment (TME). However, these roles of macrophages in the TME remain unclear. We downloaded RNA sequencing data from The Cancer Genome Atlas (TCGA) database for patients with CRC. Subsequently, Kaplan-Meier survival curves were generated to assess the differential infiltration of M1 and M2 macrophages based on CRC location. Differentially expressed gene (DEG) and functional analyses were performed to screen the roles of DEGs. Critical prognostic genes were identified using least absolute shrinkage and selection operator regression. The risk scores were calculated for each patient. In patients with right-sided CRC, reduced M1 macrophage infiltration was associated with poor prognosis. M1 macrophage infiltration positively correlated with CD8+ T cell infiltration. A risk model was developed and validated for performance using GSE103479 and GSE72970. Nine genes were identified as independent prognostic genes that could be potential biomarkers for effectively predicting survival in patients with right-sided CRC. Kaplan-Meier curves for overall survival and progression-free survival analyses revealed that the high-risk group of patients with right-sided CRC had a poor prognosis. This novel M1 macrophage-related risk model may provide a gene signature for predicting the survival outcomes of patients with right-sided CRC and facilitate further studies examining the relationship between infiltration of M1 macrophages and the prognosis of such patients.

The Role of Altered Mitochondrial Metabolism in Thyroid Cancer Development and Mitochondria-Targeted Thyroid Cancer Treatment

Thyroid cancer (TC) is the most common type of endocrine malignancy. Tumour formation, progression, and metastasis greatly depend on the efficacy of mitochondria-primarily, the regulation of mitochondria-mediated apoptosis, Ca²⁺ homeostasis, dynamics, energy production, and associated reactive oxygen species generation. Recent studies have successfully confirmed the mitochondrial aetiology of thyroid carcinogenesis. In this review, we focus on the recent progress in understanding the molecular mechanisms of thyroid cancer relating to altered mitochondrial metabolism. We also discuss the repurposing of known drugs and the induction of mitochondria-mediated apoptosis as a new trend in the development of anti-TC therapy.

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

Identification and Complete Validation of Prognostic Gene Signatures for Human Papillomavirus-Associated Cancers: Integrated Approach Covering Different Anatomical Locations

Human papillomavirus (HPV) infects squamous epithelium and is a major cause of cervical cancer (CC) and a subset of head and neck cancers (HNC). Virus-induced tumorigenesis, molecular alterations, and related prognostic markers are expected to be similar between the two cancers, but they remain poorly understood. We present integrated molecular analysis of HPV-associated tumors from TCGA and GEO databases and identify prognostic biomarkers. Analysis of gene expression profiles identified common upregulated genes and pathways of DNA replication and repair in the HPV-associated tumors. We established 34 prognostic gene signatures with a universal cutoff value in TCGA-CC using Elastic Net Cox regression analysis. We were able to externally validate our results in the TCGA-HNC and several GEO data sets, and demonstrated prognostic power in HPV-associated HNC, but not in HPV-negative cancers. The HPV-related prognostic and predictive indicator did not discriminate other cancers, except bladder urothelial carcinoma. These results identify and completely validate a highly selective prognostic system and its cross-usefulness in HPV-associated cancers, regardless of the tumor's anatomical subsite.IMPORTANCE Persistent infection with high-risk HPV interferes with cell function regulation and causes cell mutations, which accumulate over the long term and eventually develop into cancer. Results of pathway enrichment analysis presumably showed this accumulation of intracellular damage during the chronic HPV-infected state. We used highly advanced statistical methods to identify the most appropriate genes and coefficients and developed the HPV-related prognostic and predictive indicator (HPPI) risk scoring system. We applied the same cutoff value to training and validation sets and demonstrated good prognostic performance in both data sets, and confirmed a consistent trend in external validation. Moreover, HPPI presented significant validation results for bladder cancer suspected to be related to HPV. This suggested that our risk scoring system based on the prognostic gene signature could play an important role in the development of treatment strategies for patients with HPV-related cancer.

A Novel Immune-Related Prognostic Signature for Thyroid Carcinoma

Background:

Recent studies have confirmed that immune-associated genes perform a crucial function in recurrence and metastasis of thyroid carcinoma. A reliable immune-related prognostic signature for patients with thyroid cancer is needed. This study constructed a novel immune-related prognostic signature for thyroid cancer and evaluated its prognostic value by bioinformatics analysis.

Methods:

In this study, we anatomized differentially expressed immune-associated genes from The Cancer Genome Atlas database. The samples from The Cancer Genome Atlas database were randomly divided into training set and test set. A novel immune-related prognostic signature for thyroid cancer was developed by least absolute shrinkage and selection operator and Cox regression analysis: Risk score = (0.6846 × expression value of C-X-C motif chemokine ligand 5 [CXCL5]) + (1.1556 × expression value of Azurocidin 1 [AZU1]) + (−0.3156 × expression value of nucleotide binding oligomerization domain containing 1 [NOD1] + (0.0542 × expression value of TNF Receptor Superfamily Member 11b [TNFRSF11B]) + (0.0952 × expression value of VGF nerve growth factor inducible [VGF]). The established prognostic signature was evaluated based on training set and test set by survival curves, receiver–operator characteristic curves, risk score, survival status, heatmap, and independent prognostic analysis. Meanwhile, we appraised the correlation between target immune-associated genes and clinical stage, tumor-infiltrating immune cells respectively.

Results:

Five immune-associated genes were used for constructing an immune-related prognostic signature by least absolute shrinkage and selection operator, univariate, and multivariate analysis. Survival curves, receiver–operator characteristic curves, and independent prognostic analysis showed the signature had significant prediction value. Clinical and immune cell correlation analyses indicated that target immune-associated genes may participate in tumor immune infiltration and tumor progression.

Conclusions:

We constructed a novel 5 immune-associated genes signature for predicting the prognosis of patients with thyroid cancer, which may help clinical workers evaluate individualized therapy and prognosis.

Identification of prognostic mRNAs in metastatic cutaneous melanoma

Cutaneous melanoma is the most common cause of skin cancer-related deaths worldwide. There is an urgent need to identify prognostic biomarkers to facilitate decision-making for treatment of metastatic cutaneous melanoma. Gene expression microarrays and RNA-seq technology have recently improved or changed current prognostic and therapeutic strategies for several cancers. However, according to the current melanoma staging system, prognosis is almost entirely dependent on clinicopathological features. To identify novel prognostic biomarkers, we investigated gene expression and clinical data for patients with cutaneous melanoma from three cohorts of The Cancer Genome Atlas and Gene Expression Omnibus. Kaplan-Meier survival analysis using median values of each gene as cutoff value revealed that nine genes (ABCC3, CAPS2, CCR6, CDCA8, CLU, DPF1, PTK2B, SATB1, and SYNE1) were statistically significant prognostic biomarkers of metastatic cutaneous melanoma in all three independent cohorts. Low expression of two genes (CDCA8 and DPF1) and high expression of seven genes (ABCC3, CAPS2, CCR6, CLU, PTK2B, SATB1, and SYNE) were significantly associated with positive metastatic cutaneous melanoma prognoses. In conclusion, we suggest nine novel prognostic biomarkers for cutaneous metastatic melanoma.

An 8 miRNA-Based Risk Score System for Predicting the Prognosis of Patients With Papillary Thyroid Cancer

Background:

Dysregulation of microRNAs (miRNAs) in papillary thyroid cancer (PTC) might influence prognosis of PTC. This study is aimed to develop a risk score system for predicting prognosis of PTC.

Methods:

The miRNA and gene expression profiles of PTC were obtained from The Cancer Genome Atlas database. PTC samples were randomly separated into training set (n = 248) and validation set (n = 248). The differentially expressed miRNAs (DE-miRNAs) in the training set were screened using limma package. The independent prognosis-associated DE-miRNAs were identified for building a risk score system. Risk score of PTC samples in the training set was calculated and samples were divided into high risk group and low risk group. Kaplan-Meier curves and receiver operating characteristic (ROC) curve were used to assess the accuracy of the risk score system in the training set, validation set and entire set. Finally, a miRNA-gene regulatory network was visualized by Cytoscape software, followed by enrichment analysis.

Results:

Totally, 162 DE-miRNAs between tumor and control groups in the training set were identified. An 8 independent prognosis-associated DE-miRNAs, (including miR-1179, miR-133b, miR-3194, miR-3912, miR-548j, miR-6720, miR-6734, and miR-6843) based risk score system was developed. The area under ROC curve in the training set, validation set and entire set was all above 0.93. A miRNA-gene regulatory network involving the 8 DE-miRNAs were built and functional enrichment analysis suggested the genes in the network were significantly enriched into 13 pathways, including calcium signaling pathway and hedgehog signaling pathway.

Conclusion:

The risk score system developed this study might be used for predicting the prognosis of PTC. Besides, the 8 miRNAs might affect the prognosis of PTC via hedgehog signaling pathway and calcium signaling pathway.

Development and Validation of a Risk Scoring System Derived from Meta-Analyses of Papillary Thyroid Cancer

BACKGROUND

The aim of this study was to develop a scoring system to stratify the risk of papillary thyroid cancer (PTC) and to select the proper management.

METHODS

We performed a systematic search of MEDLINE and Embase. Data regarding patients' prognoses were obtained from the included studies. Odds ratios (ORs) with statistical significance were extracted from the publications. To generate a risk scoring system (RSS), ORs were summed (RSS1), and summed after natural-logarithmic transformation (RSS2). RSS1 and RSS2 were compared to the eighth edition of the American Joint Committee on Cancer (AJCC) staging system and the 2015 American Thyroid Association (ATA) guidelines for thyroid nodules and differentiated thyroid carcinoma.

RESULTS

Five meta-analyses were eligible for inclusion in the study. Eight variables (sex, tumour size, extrathyroidal extension, BRAF mutation, TERT mutation, histologic subtype, lymph node metastasis, and distant metastasis) were included. RSS1 was the best of the analysed models.

CONCLUSION

We developed and validated a new RSS derived from previous meta-analyses for patients with PTC. This RSS seems to be superior to previously published systems.

A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study

BACKGROUND

Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations.

OBJECTIVE

Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA.

METHODS

We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral).

RESULTS

Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data.

CONCLUSIONS

Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

Identification of a Recurrence Signature and Validation of Cell Infiltration Level of Thyroid Cancer Microenvironment

Though many patients with thyroid cancer may be indolent, there are still about 50% lymph node metastases and 20% the recurrence rates. There is still no ideal method to predict its relapse. In this study, we analyzed the gene transcriptome profiles of eight Gene Expression Omnibus (GEO), and next screened 77 commonly differential expressed genes. Next, Least Absolute Shrinkage and Selection Operator (LASSO) regression model was performed and seven genes (i.e., FN1, PKIA, TMEM47, FXYD6, SDC2, CD44, and GGCT) were then identified, which is highly associated with recurrence data from the Cancer Genome Atlas (TCGA) database. These patients were then divided into low and high-risk groups with specific risk-score formula. Univariate and multivariate Cox regression further revealed that the 7-mRNA signature plays a functional causative role independent of clinicopathological characteristics. The 7-mRNA-signature integrated nomogram showed better discrimination, and decision curve analysis demonstrated that it is clinically useful. Besides, patient with lower risk score shows a relatively lower level of activated dendritic cells (DCs), resting DCs, regulatory T cells and γδT cells, and process of DCs apoptotic. In conclusion, our present immune-related classifier could produce a potential tool for predicting early-relapse.

GABRQ expression is a potential prognostic marker for patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Novel biomarkers of ccRCC may provide crucial information on tumor features and prognosis. The present study aimed to determine whether the expression of γ-aminobutyric acid (GABA) A receptor subunit θ (GABRQ) could serve as a novel prognostic marker of ccRCC. GABA is the main inhibitory neurotransmitter in the brain that activates the receptor GABA_A, which is comprised of three subunit isoforms: GABRA3, GABRB3 and GABRQ. A recent study reported that GABRQ is involved in the initiation and progression of hepatocellular carcinoma; however, the role of GABRQ in ccRCC remains unknown. In the present study, clinical and transcriptomic data were obtained from cohorts of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Differential GABRQ expression levels among early (TI and II), late (TIII and IV), nonmetastatic (M0) and metastatic (M1, primary tumor) stages of ccRCC samples were then identified. Furthermore, the use of GABRQ as a prognostic gene was analyzed using Uno's C-index based on the time-dependent area under the curve (AUC), the AUC of the receiver operating characteristic curve at 5 years, the Kaplan-Meier survival curve and multivariate analysis. The survival curve analysis revealed that low GABRQ mRNA expression was significantly associated with a poor prognosis of ccRCC (P<0.001 and P=0.0012 for TCGA and ICGC data, respectively). In addition, analyses of the C-index and AUC values further supported this discriminatory power. Furthermore, the prognostic value of GABRQ mRNA expression was confirmed by multivariate Cox regression analysis. Taken together, these results suggested that GABRQ mRNA expression may be considered as a novel prognostic biomarker of ccRCC.

Development of a risk scoring system for patients with papillary thyroid cancer

As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network-Regularized high-dimensional Cox-regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event-free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.