A robust biomarker of differential correlations improves the diagnosis of cytologically indeterminate thyroid cancers

The role of Na+-coupled bicarbonate transporters (NCBT) in health and disease

Cellular and organism survival depends upon the regulation of pH, which is regulated by highly specialized cell membrane transporters, the solute carriers (SLC) (For a comprehensive list of the solute carrier family members, see: https://www.bioparadigms.org/slc/ ). The SLC4 family of bicarbonate (HCO3-) transporters consists of ten members, sorted by their coupling to either sodium (NBCe1, NBCe2, NBCn1, NBCn2, NDCBE), chloride (AE1, AE2, AE3), or borate (BTR1). The ionic coupling of SLC4A9 (AE4) remains controversial. These SLC4 bicarbonate transporters may be controlled by cellular ionic gradients, cellular membrane voltage, and signaling molecules to maintain critical cellular and systemic pH (acid-base) balance. There are profound consequences when blood pH deviates even a small amount outside the normal range (7.35-7.45). Chiefly, Na+-coupled bicarbonate transporters (NCBT) control intracellular pH in nearly every living cell, maintaining the biological pH required for life. Additionally, NCBTs have important roles to regulate cell volume and maintain salt balance as well as absorption and secretion of acid-base equivalents. Due to their varied tissue expression, NCBTs have roles in pathophysiology, which become apparent in physiologic responses when their expression is reduced or genetically deleted. Variations in physiological pH are seen in a wide variety of conditions, from canonically acid-base related conditions to pathologies not necessarily associated with acid-base dysfunction such as cancer, glaucoma, or various neurological diseases. The membranous location of the SLC4 transporters as well as recent advances in discovering their structural biology makes them accessible and attractive as a druggable target in a disease context. The role of sodium-coupled bicarbonate transporters in such a large array of conditions illustrates the potential of treating a wide range of disease states by modifying function of these transporters, whether that be through inhibition or enhancement.

Artificial intelligence defines protein-based classification of thyroid nodules

Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

Large-scale transcriptomic analysis of coding and non-coding pathological biomarkers, associated with the tumor immune microenvironment of thyroid cancer and potential target therapy exploration

Papillary thyroid carcinoma (PTC) is the most prevalent endocrine malignancy with a steadily increasing global incidence in recent decades. The pathogenesis of PTC is poorly understood, and the present diagnostic protocols are deficient. Thus, identifying novel prognostic biomarkers to improve our understanding of the mechanisms of pathogenesis, diagnosis, and designing therapeutic strategies for PTC is crucial. In this study, we integrated 27 PTC transcriptomic datasets and identified overlapping differentially expressed genes (DEGs) and differentially expressed microRNAs, collectively known as thyroid tumor-enriched proteins (TTEPs), and TTEmiRs, respectively. Our integrated bioinformatics analysis revealed that TTEPs were associated with tumor stages, poor surgical outcomes, distant metastasis, and worse prognoses in PTC cohorts. In addition, TTEPs were found to be associated with tumor immune infiltrating cells and immunosuppressive phenotypes of PTC. Enrichment analysis suggested the association of TTEPs with epithelial-to-mesenchymal transition (EMT), cell-matrix remodeling, and transcriptional dysregulation, while the TTEmiRs (miR-146b-5p and miR-21-5p) were associated with the modulation of the immune response, EMT, migration, cellular proliferation, and stemness. Molecular docking simulations were performed to evaluate binding affinities between TTEPs and antrocinnamomin, antcin, and antrocin, the bioactive compounds from one of the most reputable Taiwan indigenous medicinal plants (Antrodia camphorata). Our results revealed that antcin exhibited higher binding efficacies toward FN1, ETV5, and NRCAM, whereas antrocin demonstrated the least. Among the targets, fibronectin (FN1) demonstrated high ligandability potential for the compounds whereas NRCAM demonstrated the least. Collectively, our results hinted at the potential of antcin for targeting TTEPs. In conclusion, this comprehensive bioinformatics analysis strongly suggested that TTEPs and TTEmiRs could be used as potential diagnostic biomarker signatures and be exploited as potential targets for therapeutics development.

SLC4A4 promotes prostate cancer progression in vivo and in vitro via AKT-mediated signalling pathway

Background

Prostate cancer (PCa) is the second leading cause of cancer-related male deaths worldwide. The purpose of this study was to investigate the effects of homo sapiens solute carrier family 4 member 4 (SLC4A4), which encodes the electrogenic Na+/HCO3− cotransporter isoform 1 (NBCe1), in the development and progression of PCa.

Methods

The expression levels of SLC4A4 in PCa and normal prostate tissues were evaluated by immunohistochemistry. The SLC4A4 knockdown cell model was structured by lentiviral infection, and the knockdown efficiency was validated by RT-qPCR and Western blotting. The effects of SLC4A4 knockdown on cell proliferation, apoptosis and cycle, migration, and invasion were detected by Celigo cell counting assay and CCK-8 assay, flow cytometry analysis, wound-healing, and Transwell assay, respectively. Tumor growth in nude mice was surveyed by in vivo imaging and Ki-67 staining. Furthermore, underlying mechanism of SLC4A4 silence induced inhibition of PCa progression was explored by human phospho-kinase array.

Results

Our results revealed that SLC4A4 expression was up-regulated in PCa tissues and human PCa cell lines. High expression of SLC4A4 in tumor specimens was significantly correlated with disease progression. SLC4A4 knockdown inhibited cell proliferation, migration and invasion, while facilitated apoptosis, which was also confirmed in vivo. Moreover, SLC4A4 promoted PCa progression through the AKT-mediated signalling pathway.

Conclusion

The results of this study indicated that SLC4A4 overexpression was closely associated with the progression of PCa; SLC4A4 knockdown suppressed PCa development in vitro and in vivo. SLC4A4 acts as a tumor promotor in PCa by regulating key components of the AKT pathway and may therefore act as a potential therapeutic target for PCa treatment.

Evaluation of the Prognostic Relevance of Differential Claudin Gene Expression Highlights Claudin-4 as Being Suppressed by TGFβ1 Inhibitor in Colorectal Cancer

Background: Claudins (CLDNs) are a family of closely related transmembrane proteins that have been linked to oncogenic transformation and metastasis across a range of cancers, suggesting that they may be valuable diagnostic and/or prognostic biomarkers that can be used to evaluate patient outcomes. However, CLDN expression patterns associated with colorectal cancer (CRC) remain to be defined.

Methods: The mRNA levels of 21 different CLDN family genes were assessed across 20 tumor types using the Oncomine database. Correlations between these genes and patient clinical outcomes, immune cell infiltration, clinicopathological staging, lymph node metastasis, and mutational status were analyzed using the GEPIA, UALCAN, Human Protein Atlas, Tumor Immune Estimation Resource, STRING, Genenetwork, cBioportal, and DAVID databases in an effort to clarify the potential functional roles of different CLDN protein in CRC. Molecular docking analyses were used to probe potential interactions between CLDN4 and TGFβ1. Levels of CLDN4 and CLDN11 mRNA expression in clinical CRC patient samples and in the HT29 and HCT116 cell lines were assessed via qPCR. CLDN4 expression levels in these 2 cell lines were additionally assessed following TGFβ1 inhibitor treatment.

Results: These analyses revealed that COAD and READ tissues exhibited the upregulation of CLDN1, CLDN2, CLDN3, CLDN4, CLDN7, and CLDN12 as well as the downregulation of CLDN5 and CLDN11 relative to control tissues. Higher CLDN11 and CLDN14 expression as well as lower CLDN23 mRNA levels were associated with poorer overall survival (OS) outcomes. Moreover, CLDN2 and CLDN3 or CLDN11 mRNA levels were significantly associated with lymph node metastatic progression in COAD or READ lower in COAD and READ tissues. A positive correlation between the expression of CLDN11 and predicted macrophage, dendritic cell, and CD4+ T cell infiltration was identified in CRC, with CLDN12 expression further being positively correlated with CD4+ T cell infiltration whereas a negative correlation was observed between such infiltration and the expression of CLDN3 and CLDN15. A positive correlation between CLDN1, CLDN16, and neutrophil infiltration was additionally detected, whereas neutrophil levels were negatively correlated with the expression of CLDN3 and CLDN15. Molecular docking suggested that CLDN4 was able to directly bind via hydrogen bond with TGFβ1. Relative to paracancerous tissues, clinical CRC tumor tissue samples exhibited CLDN4 and CLDN11 upregulation and downregulation, respectively. LY364947 was able to suppress the expression of CLDN4 in both the HT29 and HCT116 cell lines.

Conclusion: Together, these results suggest that the expression of different CLDN family genes is closely associated with CRC tumor clinicopathological staging and immune cell infiltration. Moreover, CLDN4 expression is closely associated with TGFβ1 in CRC, suggesting that it and other CLDN family members may represent viable targets for antitumor therapeutic intervention.

Semaphorin 3D inhibits proliferation and migration of papillary thyroid carcinoma by regulating MAPK/ERK signaling pathway

BACKGROUND

Semaphorin 3D (SEMA3D) plays an important role in the occurrence and development of multifarious cancers. However, the relationship between SEMA3D and papillary thyroid carcinoma (PTC) remains unclear. This study aimed to investigate the functions and mechanism of SEMA3D in papillary thyroid carcinoma (PTC).

METHODS

The expression of SEMA3D in PTC tissues and cell lines was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Western blotting and immunohistochemistry (IHC) were used to detect the expression of the related proteins. CCK-8 and colony formation assays and Transwell assays were used to evaluate cell proliferation and migration, respectively. A xenograft model was induced to further verify the effect of SEMA3D in vivo.

RESULTS

In this study, we found that SEMA3D was downregulated in PTC tissues and PTC cell lines (TPC-1 and BCPAP). The expression level of SEMA3D was significantly related to age (P < 0.01), extrathyroidal extension (P < 0.01), TNM stage (P < 0.01) and lymph node metastasis (P < 0.01). In vitro experiments showed that overexpression of SEMA3D inhibited the proliferation and migration of TPC-1 and BCPAP cells and that upregulated SEMA3D inhibited the phosphorylation of ERK and the expression of the phenotype-related proteins PCNA and MMP2. In addition, SEMA3D overexpression inhibited tumour growth in vivo.

CONCLUSION

In this study, we found that SEMA3D is significantly downregulated in PTC tissues. SEMA3D inhibits the proliferation and migration of PTC cells and suppresses tumour growth in vivo, possibly partially through the MAPK/ERK signalling pathway, suggesting that SEMA3D may be a reliable molecular marker for the diagnosis and treatment of PTC.

Whole-Genome Profiles of Malay Colorectal Cancer Patients with Intact MMR Proteins

Background: This study aimed to identify new genes associated with CRC in patients with normal mismatch repair (MMR) protein expression. Method: Whole-genome sequencing (WGS) was performed in seven early-age-onset Malay CRC patients. Potential germline genetic variants, including single-nucleotide variations and insertions and deletions (indels), were prioritized using functional and predictive algorithms. Results: An average of 3.2 million single-nucleotide variations (SNVs) and over 800 indels were identified. Three potential candidate variants in three genes—IFNE, PTCH2 and SEMA3D—which were predicted to affect protein function, were identified in three Malay CRC patients. In addition, 19 candidate genes—ANKDD1B, CENPM, CLDN5, MAGEB16, MAP3K14, MOB3C, MS4A12, MUC19, OR2L8, OR51Q1, OR51AR1, PDE4DIP, PKD1L3, PRIM2, PRM3, SEC22B, TPTE, USP29 and ZNF117—harbouring nonsense variants were prioritised. These genes are suggested to play a role in cancer predisposition and to be associated with cancer risk. Pathway enrichment analysis indicated significant enrichment in the olfactory signalling pathway. Conclusion: This study provides a new spectrum of insights into the potential genes, variants and pathways associated with CRC in Malay patients.

A New Story of the Three Magi: Scaffolding Proteins and lncRNA Suppressors of Cancer

Scaffolding molecules exert a critical role in orchestrating cellular response through the spatiotemporal assembly of effector proteins as signalosomes. By increasing the efficiency and selectivity of intracellular signaling, these molecules can exert (anti/pro)oncogenic activities. As an archetype of scaffolding proteins with tumor suppressor property, the present review focuses on MAGI1, 2, and 3 (membrane-associated guanylate kinase inverted), a subgroup of the MAGUK protein family, that mediate networks involving receptors, junctional complexes, signaling molecules, and the cytoskeleton. MAGI1, 2, and 3 are comprised of 6 PDZ domains, 2 WW domains, and 1 GUK domain. These 9 protein binding modules allow selective interactions with a wide range of effectors, including the PTEN tumor suppressor, the β-catenin and YAP1 proto-oncogenes, and the regulation of the PI3K/AKT, the Wnt, and the Hippo signaling pathways. The frequent downmodulation of MAGIs in various human malignancies makes these scaffolding molecules and their ligands putative therapeutic targets. Interestingly, MAGI1 and MAGI2 genetic loci generate a series of long non-coding RNAs that act as a tumor promoter or suppressor in a tissue-dependent manner, by selectively sponging some miRNAs or by regulating epigenetic processes. Here, we discuss the different paths followed by the three MAGIs to control carcinogenesis.

Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P < 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P < 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.

Prognostic Value of SLC4A4 and its Correlation with Immune Infiltration in Colon Adenocarcinoma

BACKGROUND SLC4A4 is differentially expressed in a variety of tumors, but its significance in colon adenocarcinoma has not been determined. MATERIAL AND METHODS Transcriptomes of two cohorts, GSE41258 and GSE32323, contained in The Cancer Genome Atlas (TCGA) were analysed to determine differences in SLC4A4 expression between tumor and normal tissue and their correlations with overall survival. The relationships between SLC4A4 expression and clinical characteristics were determined by COX regression analysis and logistic regression analysis, and correlations of SLC4A4 levels with tumor infiltrating immune cells (TIICs) and genes with high mutation frequency were evaluated by Pearson correlation analysis. Molecular functions and signaling pathways that might be affected by changes in SLC4A4 expression were determined by gene set enrichment analysis (GSEA). The overall distribution of TIICs was determined by two web servers: tumor immune estimation resource (TIMER) and CIBERSORT. RESULTS SLC4A4 expression was lower in colon adenocarcinoma than in normal colon tissue, suggesting that SLC4A4 was associated with poor prognosis. Reduced SLC4A4 expression was also associated with lymph node invasion and distant metastasis and was moderately correlated with increased expression of MUC4 and SMAD4, two genes with high mutation frequency in colon adenocarcinoma. GSEA indicated that changes in SLC4A4 expression affects several biological processes, including mismatch repair, base excision repair, and DNA replication. Eight TIICs in the tumor microenvironment differed significantly in groups with low and high expression of SLC4A4. CONCLUSIONS SLC4A4 may be a novel biomarker predicting prognosis in patients with colon adenocarcinoma. TIICs differed significantly in samples with higher and lower expression of SLC4A4.

Digital gene expression analysis might aid in the diagnosis of thyroid cancer

Background

Thyroid cancer represents approximately 90% of endocrine cancers. Difficulties in diagnosis and low inter-observer agreement are sometimes encountered, especially in the distinction between the follicular variant of papillary thyroid carcinoma (fvptc) and other follicular-patterned lesions, and can present significant challenges. In the present proof-of-concept study, we report a gene-expression assay using NanoString nCounter technology (NanoString Technologies, Seattle, WA, U.S.A.) that might aid in the differential diagnosis of thyroid neoplasms based on gene-expression signatures.

Methods

Our cohort included 29 patients with classical papillary thyroid carcinoma (ptc), 13 patients with fvptc, 14 patients with follicular thyroid carcinoma (ftc), 14 patients with follicular adenoma (fa), and 14 patients without any abnormality. We developed a 3-step classifier that shows good correlation with the pathologic diagnosis of various thyroid neoplasms. Step 1 differentiates normal from abnormal thyroid tissue; step 2 differentiates benign from malignant lesions; and step 3 differentiates the common malignant entities ptc, ftc, and fvptc.

Results

Using our 3-step classifier approach based on selected genes, we developed an algorithm that attempts to differentiate thyroid lesions with varying levels of sensitivity and specificity. Three genes-namely SDC4, PLCD3, and NECTIN4/PVRL4-were the most informative in distinguishing normal from abnormal tissue with a sensitivity and a specificity of 100%. One gene, SDC4, was important for differentiating benign from malignant lesions with a sensitivity of 89% and a specificity of 92%. Various combinations of genes were required to classify specific thyroid neoplasms.

Conclusions

This preliminary proof-of-concept study suggests a role for nCounter technology, a digital gene expression analysis technique, as an adjunct assay for the molecular diagnosis of thyroid neoplasms.

Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database

Background

Thyroid carcinoma is a malignancy with high morbidity and mortality. Genetic alterations play pivot roles in the pathogenesis of thyroid carcinoma, where long noncoding RNA (lncRNA) have been identified to be crucial. This study sought to investigate the biological functions of lncRNA expression profiles in thyroid carcinoma.

Material/Methods

The lncRNAs expression profiles were acquired from The Cancer Genome Atlas (TCGA) database according to 510 thyroid cancer tissues and 58 normal thyroid tissues. By using R package edgeR, differentially expressed RNAs were obtained. Also, an overall survival model was established based on Cox regression and clinical data then testified by Kaplan-Meier plot, receiver operating characteristic (ROC)-curve and C-index analysis. We investigated the co-expressed genes with lncRNAs involved in the prognostic model, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted R package clusterProfile.

Results

A total of 352 lncRNAs were identified as differentially expressed in thyroid carcinoma, and an overall survival model consisting of 8 signature lncRNAs was proposed (ROC=0.862, C-index=0.893, P<0.05), 3 of which (DOCK9-DT, FAM111A-DT, and LINC01736) represent co-expressed mRNAs. However, as an oncogene, only FAM111A-DT increased the prognostic risk in thyroid carcinoma. Furthermore, we found differential genes LINC01016, LHX1-DT, IGF2-AS, ND MIR1-1HG-AS1, significantly related to lymph node metastasis (P<0.05).

Conclusions

In this study, we clarified the differential lncRNA expression profiles which were related to the tumorigenesis and prognosis in thyroid carcinoma. Our results provide new rationale and understandings to the pathogenesis and regulatory mechanisms of thyroid carcinoma.

Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.

False Discovery Rate Control in Cancer Biomarker Selection Using Knockoffs

The discovery of biomarkers that are informative for cancer risk assessment, diagnosis, prognosis and treatment predictions is crucial. Recent advances in high-throughput genomics make it plausible to select biomarkers from the vast number of human genes in an unbiased manner. Yet, control of false discoveries is challenging given the large number of genes versus the relatively small number of patients in a typical cancer study. To ensure that most of the discoveries are true, we employ a knockoff procedure to control false discoveries. Our method is general and flexible, accommodating arbitrary covariate distributions, linear and nonlinear associations, and survival models. In simulations, our method compares favorably to the alternatives; its utility of identifying important genes in real clinical applications is demonstrated by the identification of seven genes associated with Breslow thickness in skin cutaneous melanoma patients.

PFKFB2 Promoter Hypomethylation as Recurrence Predictive Marker in Well-Differentiated Thyroid Carcinomas

Despite the low mortality rates, well-differentiated thyroid carcinomas (WDTC) frequently relapse. BRAF and TERT mutations have been extensively related to prognosis in thyroid cancer. In this study, the methylation levels of selected CpGs (5-cytosine-phosphate-guanine-3) comprising a classifier, previously reported by our group, were assessed in combination with BRAF and TERT mutations. We evaluated 121 WDTC, three poorly-differentiated/anaplastic thyroid carcinomas (PDTC/ATC), 22 benign thyroid lesions (BTL), and 13 non-neoplastic thyroid (NT) tissues. BRAF (V600E) and TERT promoter (C228T and C250T) mutations were tested by pyrosequencing and Sanger sequencing, respectively. Three CpGs mapped in PFKFB2, ATP6V0C, and CXXC5 were evaluated by bisulfite pyrosequencing. ATP6V0C hypermethylation and PFKFB2 hypomethylation were detected in poor-prognosis (PDTC/ATC and relapsed WDTC) compared with good-prognosis (no relapsed WDTC) and non-malignant cases (NT/BTL). CXXC5 was hypomethylated in both poor and good-prognosis cases. Shorter disease-free survival was observed in WDTC patients presenting lower PFKFB2 methylation levels (p = 0.004). No association was observed on comparing BRAF (60.7%) and TERT (3.4%) mutations and prognosis. Lower PFKFB2 methylation levels was an independent factor of high relapse risk (Hazard Ratio = 3.2; CI_95% = 1.1–9.5). PFKFB2 promoter methylation analysis has potential applicability to better stratify WDTC patients according to the recurrence risk, independently of BRAF and TERT mutations.

Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

RNAs may act as competing endogenous RNAs (ceRNAs), a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA) to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA)-messenger RNA (mRNA)long non-coding RNA (lncRNA) networks from miRcode database and weighted correlation network analysis (WGCNA), based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB). We applied the pipeline into The Cancer Genome Atlas (TCGA) thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs) were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA-mRNA-lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients' survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

BACKGROUND

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.

RESULTS

We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.

CONCLUSIONS

Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

A 10-Gene Classifier for Indeterminate Thyroid Nodules: Development and Multicenter Accuracy Study

BACKGROUND

In most of the world, diagnostic surgery remains the most frequent approach for indeterminate thyroid cytology. Although several molecular tests are available for testing in centralized commercial laboratories in the United States, there are no available kits for local laboratory testing. The aim of this study was to develop a prototype in vitro diagnostic (IVD) gene classifier for the further characterization of nodules with an indeterminate thyroid cytology.

METHODS

In a first stage, the expression of 18 genes was determined by quantitative polymerase chain reaction (qPCR) in a broad histopathological spectrum of 114 fresh-tissue biopsies. Expression data were used to train several classifiers by supervised machine learning approaches. Classifiers were tested in an independent set of 139 samples. In a second stage, the best classifier was chosen as a model to develop a multiplexed-qPCR IVD prototype assay, which was tested in a prospective multicenter cohort of fine-needle aspiration biopsies.

RESULTS

In tissue biopsies, the best classifier, using only 10 genes, reached an optimal and consistent performance in the ninefold cross-validated testing set (sensitivity 93% and specificity 81%). In the multicenter cohort of fine-needle aspiration biopsy samples, the 10-gene signature, built into a multiplexed-qPCR IVD prototype, showed an area under the curve of 0.97, a positive predictive value of 78%, and a negative predictive value of 98%. By Bayes' theorem, the IVD prototype is expected to achieve a positive predictive value of 64-82% and a negative predictive value of 97-99% in patients with a cancer prevalence range of 20-40%.

CONCLUSIONS

A new multiplexed-qPCR IVD prototype is reported that accurately classifies thyroid nodules and may provide a future solution suitable for local reference laboratory testing.

Decreased expression of semaphorin 3D is associated with genesis and development in colorectal cancer

Background

Semaphorin 3D (SEMA3D) plays important roles in the genesis and progress of many cancers. However, the relationship between SEMA3D and colorectal cancer (CRC) remains unknown. The aim of this study was to investigate whether SEMA3D can be used as a predictive marker for the diagnosis, metastasis, and prognosis of CRC by assessing the expression of SEMA3D in the tissues and serum of CRC patients.

Methods

Real-time quantitative polymerase chain reaction (qPCR) was used to measure the expression of SEMA3D mRNA in 100 CRC tissues and matched normal tissues. qPCR was also used to detect the expression of SEMA3D mRNA in the CRC cell line RKO. RKO cells were transfected with SEMA3D small-interring RNA (siRNA) to interfere with endogenous SEMA3D. The migratory ability of control and SEMA3D siRNA-transfected RKO cells was determined by transwell assays. Enzyme-linked immunosorbent assay (ELISA) was utilized to detect the levels of SEMA3D in the serum of 80 CRC patients and 100 normal healthy controls. The expression of SEMA3D in 215 CRC tissues was assessed using immunohistochemistry (IHC). Then, statistical analyses were adopted to assess SEMA3D protein levels and clinical pathological characteristics.

Results

The mRNA expression of SEMA3D was significantly lower in CRC tissues than in paired normal tissues (t = 5.027, P < 0.0001). Compared with normal healthy controls, the serum levels of SEMA3D were decreased significantly in CRC patients (t = 3.656, P = 0.0003). The expression of SEMA3D protein was linked to lymph node metastasis, and low expression led to lymph node metastasis (χ² = 8.415, P = 0.004). The expression of SEMA3D in CRC tissues was a favorable prognostic factor. Patients with a higher expression of SEMA3D experienced longer survival (P = 0.002, log-rank [Mantel-Cox]; Kaplan-Meier). In addition, multivariate Cox’s proportional hazard model revealed that SEMA3D is an independent prognostic marker (hazard ratio [HR] 1.818, 95% CI 1.063–3.110, P = 0.029). Moreover, transwell assays showed that knocking down SEMA3D significantly increased RKO cell migration (t = 9.268, P = 0.0008).

Conclusions

SEMA3D might function as a tumor suppressor during the formation and development of CRC. SEMA3D might become a predictive marker for the diagnosis, metastasis, and prognosis of CRC and provide a novel target for the prevention and treatment of CRC.