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Although papillary thyroid carcinoma (PTC) has a favorable prognosis after surgical or medical treatment, its survival rate is still very low. Therefore, finding more reliable therapy methods to limit PTC is a necessity. Compelling evidence has implicated the role of microRNAs (miRNAs or miRs) in PTC. This study aims at investigating the possible effect of microRNA-599 (miR-599) on proliferation, apoptosis, and epithelial-mesenchymal transition (EMT) of PTC cells by targeting Hey2 gene. Differentially expressed genes/miRNAs associated with PTC were screened based on microarray analysis. Then, the expression of the candidate gene, as well as, the regulatory miRNA were detected in PTC cells, the related signaling pathway was verified. Afterward, the relationship between the miR and the candidate gene was verified by dual-luciferase reporter gene assay. Subsequently, the effects of overexpressed miR and silenced candidate gene on cell proliferation, cell apoptosis, EMT, migration, and invasion were detected. In PTC tissues and cells, miR-599 was downregulated while Hey2 expressed highly. Hey2 is a target gene of miR-559. In addition, the expression of Bax and E-cadherin was elevated while that of Hey2, Notch1, Delta-like1, Hes1, N1ICD, Jagged1, Snail, Slug, N-cadherin and Vimentin, and Bcl-2 was reduced in cells treated with upregulated miR-599 or downregulated Hey2. Moreover, miR-599 overexpression or Hey2 silencing inhibited cell proliferation, migration, invasion, along with EMT but promoted apoptosis. This study verified that miR-599 promotes apoptosis and represses proliferation, EMT of PTC cells through inactivating the Notch signaling pathway by downregulating Hey2, which has great clinical significance for PTC treatment.

Thyroid cancer is the most common type of endocrine tumor. The TNM classification remains a standard for treatment determination and predicting prognosis in thyroid cancer. The genes modules associated with the progression of papillary thyroid carcinoma (PTC) were not clear. We applied a weighted gene co-expression network analysis (WGCNA) and differential expression analysis to systematically identified co-expressed gene modules and hub genes associated with PTC progression based on The Cancer Genome Atlas (TCGA) PTC transcriptome sequencing data. An independent validation cohort, GSE27155, was used to evaluate the preservation of gene modules. We identified two co-expressed genes modules associated with progression of PTC. Enrichment analysis indicated that the two modules were enriched in angiogenesis and extracellular matrix organization. DCN, COL1A1, COL1A2, COL5A2 and COL3A1 were hub genes in the co-expressed network. We systematically identified co-expressed gene modules and hub genes associated with PTC progression for the first time, which provided insights into the mechanisms underlying PTC progression and some potential targets for the treatment of PTC.

Interleukin 24 (IL24) has been documented to be highly expressed in several cancers, but its role in laryngeal squamous cell carcinoma (LSCC) remains unclarified. In this study, to reveal the function and its clinical significance of IL24 in LSCC, multiple detecting methods were used comprehensively. IL24 protein expression was remarkably higher in LSCC (n= 49) than non-cancerous laryngeal controls (n= 26) as detected by in-house immunohistochemistry. Meanwhile, the IL24 mRNA expression was also evaluated based on high throughput data from Gene Expression Omnibus, The Cancer Genome Atlas, ArrayExpress and Oncomine databases. Consistently with the protein level, IL24 mRNA expression level was also predominantly upregulated in LSCC (n= 172) compared to non-cancerous laryngeal tissues (n= 81) with the standard mean difference (SMD) being 1.25 and the area under the curve (AUC) of the summary receiver operating characteristic (sROC) being 0.89 (95% CI = 0.86–0.92). Furthermore, the related genes of IL24 and the differentially expressed genes (DEGs) of LSCC were intersected and sent for Gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein-protein interaction (PPI) analyses. In the GO annotation, the top terms of biological process (BP), cellular component (CC) and molecular function (MF) were extracellular matrix organization, extracellular matrix, cytokine activity, respectively. The top pathway of KEGG was ECM-receptor interaction. The PPI networks indicated the top hub genes of IL24-related genes in LSCC were SERPINE1, TGFB1, MMP1, MMP3, CSF2, and ITGA5. In conclusion, upregulating expression of IL24 may enhance the occurrence of LSCC, which owns prospect diagnostic ability and therapeutic significance in LSCC.

BACKGROUND Oral squamous cell carcinoma (OSCC) is the sixth most prevalent cancer worldwide, with low 5-year survival rate. To identify novel prognostic markers for OSCC and determine the immune and stromal landscape of OSCC, a risk signature for OSCC patients was constructed in this study. MATERIAL AND METHODS Immune and stromal scores for OSCC samples from the Genomic Data Commons Data Portal were computed to delineate the tumor microenvironment landscape of oral cancer based on the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data algorithm. An immune score-based risk signature was constructed by combining random forest and support vector machine methods. Correlation analysis of risk signature gene expression and immune cell infiltration was conducted, and the distinguishing power of individual signature genes was evaluated by analyzing receiver operating characteristics (ROC) curves. Differentially enriched pathways between high and low risk groups were investigated via gene set variation analysis. ROC curves were plotted for signature genes to examine their ability to distinguish the recurrence and survival status of OSCC patients from GSE84846. RESULTS An immune score-related risk signature composed of ARMH1, F2RL2, AC004687.1, COL6A5, AC008750.1, RAB19, CRLF2, GRIP2, and FAM162B performed well in the prognostic stratification of OSCC patients and could effectively distinguish their survival status. Lists of pathways, including cytokine-cytokine receptor interaction and cell adhesion molecules displayed remarkable differential enrichment between high and low risk OSCC patients. CONCLUSIONS An immune score-based risk signature constructed presently may be useful to decide appropriate treatment options for individual OSCC patients.

Background Thyroid cancer (TC) is the most common endocrine malignancy; basigin (also known as BSG) plays a crucial role in tumor cell invasion, metastasis, and angiogenesis. This study was designed to identify the change of BSG expression in TC and its possible potential mechanism. Methods The BSG expression levels in TC were demonstrated using data collected from in-house immunohistochemical (IHC), RNA-sequencing (RNA-seq), microarrays, and literatures. Integrated analysis was performed to determined BSG expression levels in TC comprehensively. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed with the integration of BSG co-expressed genes and differentially expressed genes (DEGs) in TC tissues to explore the potential mechanisms of BSG in TC. Results The protein expression level of BSG was significantly higher in TC cases based on the IHC experiments. In addition, the combined SMD for BSG expression was 0.39 (p < 0.0001), the diagnostic odds ratio was 3.69, and the AUC of the sROC curve was 0.6986 using 1182 TC cases and 437 non-cancerous cases from 17 independent datasets. Furthermore, BSG co-expressed genes tended to be enriched in gene terms of the extracellular matrix (ECM), cell adhesion, and cell-cell interactions. The expression levels of nine hub BSG co-expressed genes were markedly upregulated in TC cases. Conclusion BSG expression levels were closely correlated with the progression of TC and may affect the signals of the ECM, cell adhesion, and cell-cell interactions.

// Shao-Long Lu 1 , Zhi-Hua Ye 1 , Tong Ling 1 , Si-Yuan Liang 1 , Hui Li 1 , Xiao-Zhun Tang 2 , Yan-Song Xu 2 and Wei-Zhong Tang 1 1 Department of Colorectal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P. R. China 2 Department of Emergency, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P. R. China Correspondence to: Wei-Zhong Tang, email: tangweizhong0771@163.com Keywords: D-dimer, prognosis, colorectal cancer Received: June 16, 2017 Accepted: August 26, 2017 Published: September 15, 2017 ABSTRACT D-dimer, one of the canonical markers of hypercoagulability, was reported to be a potential prognostic marker of colorectal cancer. However, an inconsistent conclusion existed in several published studies. Thus, we performed this meta-analysis to provide a comprehensive insight into the prognostic role for pretreatment D-dimer in colorectal cancer. Six databases (English: Pubmed, Embase and Web of Science; Chinese: CNKI, Wangfang and VIP) were utilized for the literature retrieval. Hazard ratio (HR) was pooled by Stata 12.0. A total of fifteen studies (2283 cases) corresponded to this meta-analysis and provided available data to evaluate the prognostic role of D-dimer for colorectal cancer. The pooled HR reached 2.167 (95%. CI (confidence interval): 1.672–2.809, P < 0.001) utilizing random effect model due to obvious heterogeneity among the included studies (I 2 : 73.3%; P < 0.001). To explore the heterogeneity among the studies, we conducted a sensitivity analysis and found a heterogeneous study. After removing it, the heterogeneity reduced substantially (I 2 : 0%; P = 0.549) and we obtained a more convincing result by fixed effect model (HR = 2.143, 95% CI = 1.922–2.390, P < 0.001, 14 studies with 2179 cases). In summary, high pretreatment plasma D-dimer predicts poor survival of colorectal cancer based on the current evidence. Further prospective researches are necessary to confirm the role of D-dimer in colorectal cancer.

IL24 mRNA is known to have an apoptotic effect on cancer cells but not on noncancer cells. However, the expression level of the IL24 mRNA in head and neck squamous cell carcinoma (HNSCC) and its subgroups is rarely studied. In this study, the clinical implication of IL24 mRNA was evaluated in the common subgroups of HNSCC, including oral squamous cell carcinoma (OSCC), nasopharyngeal carcinoma (NPC), and laryngeal squamous cell carcinoma (LSCC) for analysis. Substantial IL24 mRNA expression data were calculated from several databases, such as the Gene Expression Omnibus (GEO), ArrayExpress, Sequence Read Archive (SRA), ONCOMINE, and The Cancer Genome Atlas (TCGA) databases. We ultimately collected a total of 41 microarrays and RNA-seq including 1,564 HNSCC and 603 noncancer tissue samples. IL24 mRNA was highly expressed in OSCC, LSCC, and NPC as shown by the separated standard mean difference (SMD), as well as HNSCC as a whole part (SMD = 1.47, 95% confdence interval (CI) = 1.24−1.70, P<0.0001). In all subgroups, the IL24 mRNA upregulation had the ability to distinguish cancer from noncancer tissue with area under the curves (AUCs) of the summary receiver operating characteristic (sROC) higher than 0.85. In conclusion, IL24 mRNA may be used as a potential marker for cancer screening, and its clinical diagnostic value needs to be further studied. It also provides a new idea for the treatment of the IL24 gene in HNSCC and its subgroups in the future.

Additional file 1: Supplementary Figure S1. Integrated assessment of the BSG mRNA level in thyroid cancer (THCA) based on gene microarray. A: Forest plot; B: Sensitivity; C: Funnel plot (Begg’s test); D: Funnel plot (Egger’s test). Supplementary Figure S2. Expression level of BSG confirmed using sROC curves using microarray data. A: Sensitivity; B: Specificity; C: Positive likelihood ratios; D: Negative likelihood ratios; E: Diagnostic odds ratio; F: AUC of sROC. Supplementary Figure S3. Roc curve of top 10 hub genes in thyroid cancer (THCA) based on RNA-seq data. A: Cyclin dependent kinase 1 (CDK1); B: kinesin family member 11 (KIF11); C: topoisomerase (DNA) II alpha (TOP2A); D: ribonucleotide reductase regulatory subunit M2 (RRM2); E: microtubule nucleation factor (TPX2); F: PDZ binding kinase (PBK); G: maternal embryonic leucine zipper kinase (MELK); H: DLG associated protein 5 (DLGAP5); I: kinetochore complex component (NDC80); J: and nucleolar and spindle associated protein 1 (NUSAP1). Supplementary Table S1. Clinical pathological parameters and BSG expression in THCA data from IHC. Supplementary Table 2. The scores and expression of Top 10 hub genes in THCA

Additional file 2: Figure S1. Representative H&E staining images of chronic liver disease patients with necro-inflammation activity at G0 (A), G1 (B), G2 (C), G3 (D) to G4 (E) according to the Scheuer’s classification. Scale, 200 μm. Figure S2. Representative Masson’s trichrome staining, collagen stained blue. Collagen portionate area increased significantly along with the degree of liver fibrosis (from S0 to S4, Table 1). Scale, 200 μm. Figure S3. PCA scores plot for CLD patients and normal controls using the identified four metabolite markers in training and validation sets. Figure S4. Correlation coefficient matrix among the four selected serum metabolites, previously proposed liver fibrosis markers, and clinical markers of chronic liver disease (fibrosis stages, necro-inflammation, and medication). Figure S5. 10-fold cross-validation AUROC and AUPR of machine learning methods and clinical indices. Figure S6. PCA scores plot for CLD patients of S0–2, S3 and S4 using the identified four metabolite markers in training and validation sets. Figure S7. Example decision trees from random forest models. (a) An example decision tree of Model 1. (b) An example decision tree of Model 2. (c) An example decision tree of Model 3. Figure S8. Micro-ROC and micro-PR of metabolite marker panel and clinical indicators in multi-group classification of S0–2 vs. S3 vs. S4. (a) micro-ROC and (b) micro-PR for the classification of S0-2 vs. S3 vs. S4 in Cohort 1. (c) micro-ROC and (d) micro-PR for the classification of S0-2 vs. S3 vs. S4 in Cohort 2.

Additional file 1. Fibrosis and Cirrhosis Patients with hepatitis B viral (HBV) Infection and Normal Controls (Cohort 1). Patients with CHB-induced Fibrosis and Cirrhosis and normal controls (Validation Set, Cohort 2). Inclusion and exclusion criteria for patients with chronic HBV infection. Exclusion criteria for normal controls. Medication the patients received at the time of sampling. Quality of care. Measurement of bile acids. Measurement of Amino Acids. Measurement of FFAs. Quality Control Procedure.

Additional file 3 Table S1. Clinical data of patients with chronic liver disease (CLD) and normal controls in Cohorts 1 and 2. Table S2. Serum bile acid, free fatty acid, and amino acid concentrations in patients with chronic liver disease (CLD) and in normal controls in Cohorts 1 and 2. Table S3. Results for measurement of the metabolite marker panel, APRI, FIB-4, and ALT/AST ratio in the prediction of liver fibrosis using the optimal cut-off values generated using the cohort specific data from this study. Table S4. Logistic regression analysis of metabolite marker panel-based RF-score to discriminate patients with fibrosis from patients with cirrhosis and S0–2 with S3–4 adjusting with potential confounding variables. Table S5. Net reclassification improvement and integral discriminant improvement analyses comparing RF and other clinical indexes on validation sets.

Papillary thyroid carcinoma (PTC) is recognized as one of the most prevalent types of thyroid cancer with poor prognosis. Long noncoding RNA (lncRNA) has undergone an intensive study for their involvement in tumor treatment. This study intends to unravel the association of lncRNA SLC26A4-AS1 with PTC. Initially, PTC-related expression profiling data (GSE33630) was utilized to screen differentially expressed lncRNAs in PTC and the underlying mechanisms involved with the mitogen-activated protein kinase (MAPK) pathway. Moreover, PTC tumor tissues and paracancerous tissues were arranged to determine expressions of TP53, SLC26A4-AS1, and genes related to epithelial-mesenchymal transition (EMT) and the MAPK pathway. Furthermore, SLC26A4-AS1 was overexpressed or underexpressed and JNK was underexpressed through cell transfection to examine the effect of SLC26A4-AS1 on PTC via MAPK pathway. Besides, tumor formation in nude mice was used to verify the fore experiment. LncRNA SLC26A4-AS1 regulating TP53 had the potential to participate in PTC by regulating the MAPK pathway. SLC26A4-AS1 was expressed poorly in PTC. Notably, SLC26A4-AS1 elevated E-cadherin expression while it reduced that of ERK and Vimentin. In addition, the overexpression of SLC26A4-AS1 inactivated the MAPK pathway by promoting TP53 and decreased cell migration, proliferation, and invasion. In addition to all these effects, the overexpression of SLC26A4-AS1 promoted apoptosis of TPC-1 cells. Additionally, the overexpression of lncRNA SLC26A4-AS1 reduced xenograft tumor volume in nude mice. Furthermore, the effect of SLC26A4-AS1 overexpression was found to be promoted after the MAPK pathway inactivation. Taken together, the overexpression of lncRNA SLC26A4-AS1 coffered anti-oncogenic effects on PTC through the inactivation of the MAPK pathway.