Multi-omics analysis revealed TEK and AXIN2 are potential biomarkers in multifocal papillary thyroid cancer.

BACKGROUND: Papillary thyroid carcinoma (PTC), the most common endocrine cancer, accounts for 80-85% of all malignant thyroid tumors. This study focused on identifying targets that affect the multifocality of PTC. In a previous study, we determined 158 mRNAs related to multifocality in BRAF-mutated PTC using The Cancer Genome Atlas. METHODS: We used multi-omics data (miRNAs and mRNAs) to identify the regulatory mechanisms of the investigated mRNAs. miRNA inhibitors were used to determine the relationship between mRNAs and miRNAs. We analyzed the target protein levels in patient sera using ELISA and immunohistochemical staining of patients' tissues. RESULTS: We identified 44 miRNAs that showed a negative correlation with mRNA expression. Using in vitro experiments, we identified four miRNAs that inhibit TEK and/or AXIN2 among the target mRNAs. We also showed that the downregulation of TEK and AXIN2 decreased the proliferation and migration of BRAF ( +) PTC cells. To evaluate the diagnostic ability of multifocal PTC, we examined serum TEK or AXIN2 in unifocal and multifocal PTC patients using ELISA, and showed that the serum TEK in multifocal PTC patients was higher than that in the unifocal PTC patients. The immunohistochemical study showed higher TEK and AXIN2 expression in multifocal PTC than unifocal PTC. CONCLUSIONS: Both TEK and AXIN2 play a potential role in the multifocality of PTC, and serum TEK may be a diagnostic marker for multifocal PTC.

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.

MicroRNA-133 Targets Phosphodiesterase 1C in Drosophila and Human Oral Cancer Cells to Regulate Epithelial-Mesenchymal Transition.

Non-coding microRNAs (miRNAs) have been proposed to play diverse roles in cancer biology, including epithelial-mesenchymal transition (EMT) crucial for cancer progression. Previous comparative studies revealed distinct expression profiles of miRNAs relevant to tumorigenesis and progression of oral cancer. With putative targets of these miRNAs mostly validated in vitro, it remains unclear whether similar miRNA-target relationships exist in vivo. In this study, we employed a hybrid approach, utilizing both Drosophila melanogaster and human oral cancer cells, to validate projected miRNA-target relationships relevant to EMT. Notably, overexpression of dme-miR-133 resulted in significant tissue growth in Drosophila larval wing discs. The RT-PCR analysis successfully validated a subset of its putative targets, including Pde1c. Subsequent experiments performed in oral cancer cells confirmed conserved targeting of human PDE1C by hsa-miR-133. Furthermore, the elevated level of miR-133 and its targeting of PDE1C was positively correlated with enhanced migrative ability of oral cancer cells treated with LPS, along with the molecular signature of a facilitated EMT process induced by LPS and TGF-ß. The analysis on the RNAseq data also revealed a negative correlation between the expression level of hsa-miR-133 and the survival of oral cancer patients. Taken together, our mammal-to-Drosophila-to-mammal approach successfully validates targeting of PDE1C by miR-133 both in vivo and in vitro, underlying the promoted EMT phenotypes and potentially influencing the prognosis of oral cancer patients. This hybrid approach will further aid to widen our scope in investigation of intractable human malignancies, including oral cancer.

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT.

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

Identification of Shared Genes and Pathways in Periodontitis and Type 2 Diabetes by Bioinformatics Analysis.

Introduction: It is well known that the presence of diabetes significantly affects the progression of periodontitis and that periodontitis has negative effects on diabetes and diabetes-related complications. Although this two-way relationship between type 2 diabetes and periodontitis could be understood through experimental and clinical studies, information on common genetic factors would be more useful for the understanding of both diseases and the development of treatment strategies. Materials and Methods: Gene expression data for periodontitis and type 2 diabetes were obtained from the Gene Expression Omnibus database. After preprocessing of data to reduce heterogeneity, differentially expressed genes (DEGs) between disease and normal tissue were identified using a linear regression model package. Gene ontology and Kyoto encyclopedia of genes and genome pathway enrichment analyses were conducted using R package 'vsn'. A protein-protein interaction network was constructed using the search tool for the retrieval of the interacting genes database. We used molecular complex detection for optimal module selection. CytoHubba was used to identify the highest linkage hub gene in the network. Results: We identified 152 commonly DEGs, including 125 upregulated and 27 downregulated genes. Through common DEGs, we constructed a protein-protein interaction and identified highly connected hub genes. The hub genes were up-regulated in both diseases and were most significantly enriched in the Fc gamma R-mediated phagocytosis pathway. Discussion: We have identified three up-regulated genes involved in Fc gamma receptor-mediated phagocytosis, and these genes could be potential therapeutic targets in patients with periodontitis and type 2 diabetes.

FRZB as a key molecule in abdominal aortic aneurysm progression affecting vascular integrity.

Abdominal aortic aneurysm (AAA), when ruptured, results in high mortality. The identification of molecular pathways involved in AAA progression is required to improve AAA prognosis. The aim of the present study was to assess the key genes for the progression of AAA and their functional role. Genomic and clinical data of three independent cohorts were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (GSE57691, GSE7084, and GSE98278). To develop AAA diagnosis and progression-related differentially expressed genes (DEGs), we used a significance analysis of microarray (SAM). Spearman correlation test and gene set analysis were performed to identify potential enriched pathways for DEGs. Only the Frizzled-related protein (FRZB) gene and chromosome 1 open reading frame 24 (C1orf24) exhibited significant down-regulation in all analyses. With FRZB, the pathways were associated with RHO GTPase and elastin fiber formation. With C1orf24, the pathways were elastic fiber formation, extracellular matrix organization, and cell-cell communication. Since only FRZB was evolutionally conserved in the vertebrates, function of FRZB was validated using zebrafish embryos. Knockdown of frzb remarkably reduced vascular integrity in zebrafish embryos. We believe that FRZB is a key gene involved in AAA initiation and progression affecting vascular integrity.

AML poor prognosis factor, TPD52, is associated with the maintenance of haematopoietic stem cells through regulation of cell proliferation.

Acute myeloid leukaemia (AML) is a blood cancer where undifferentiated myeloid cells are increased in the bone marrow and peripheral blood. As AML is dangerous and shows poor prognosis, many researchers categorised the relevant cytogenetic factors according to risk and prognosis. However, the specific reasons for poor cytogenetic factors remain unknown. We analysed a large data set from AML patients and found that TPD52 expression is elevated in patient groups with poor cytogenetic factors. As the amino acid sequence of TPD52 is evolutionally conserved in vertebrates, zebrafish embryos were used to investigate the function of TPD52. Since myeloid-biased haematopoietic stem cells (HSCs) are relevant to AML, the function of TPD52 in the development of HSCs was investigated. We determined that the zebrafish paralog, tpd52, is important for the maintenance of HSCs through regulation of cell proliferation. As tpd52 is linked to cell proliferation in zebrafish embryos, the proliferation-related gene, CD59, was correlated to TPD52 in every AML cohort with a high correlation coefficient. We suggest that TPD52 can be a novel therapeutic target for AML patients with poor cytogenetic factors. Additionally, more studies between TPD52 and CD59 will further increase the value of TPD52 as a novel target.

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.

MicroRNA-31 Regulates Expression of Wntless in Both Drosophila melanogaster and Human Oral Cancer Cells.

Recent comparative studies have indicated distinct expression profiles of short, non-coding microRNAs (miRNAs) in various types of cancer, including oral squamous cell carcinoma (OSCC). In this study, we employed a hybrid approach using Drosophila melanogaster as well as OSCC cell lines to validate putative targets of oral cancer-related miRNAs both in vivo and in vitro. Following overexpression of Drosophila miR-31, we found a significant decrease in the size of the imaginal wing discs and downregulation of a subset of putative targets, including wntless (wls), an important regulator of the Wnt signaling pathway. Parallel experiments performed in OSCC cells have also confirmed a similar miR-31-dependent regulation of human WLS that was not initially predicted as targets of human miR-31. Furthermore, we found subsequent downregulation of cyclin D1 and c-MYC, two of the main transcriptional targets of Wnt signaling, suggesting a potential role of miR-31 in regulating the cell cycle and proliferation of OSCC cells. Taken together, our Drosophila-based in vivo system in conjunction with the human in vitro platform will thus provide a novel insight into a mammal-to-Drosophila-to-mammal approach to validate putative targets of human miRNA and to better understand the miRNA-target relationships that play an important role in the pathophysiology of oral cancer.

LRRC17 Is Linked to Prognosis of Ovarian Cancer Through a p53-dependent Anti-apoptotic Function.

BACKGROUND/AIM: Since pathways involving LRRC17 are related to the survival of patients with various cancers, we analyzed LRRC17 as a prognostic gene in serous ovarian cancer. MATERIALS AND METHODS: Data were collected from Gene Expression Omnibus (GSE9891, GSE13876, and GSE26712) and The Cancer Genome Atlas (TCGA). We performed survival analyses using C statistics, area under the curve, survival plot with optimal cutoff level, and cox proportional regression. Zebrafish embryos were used as an in vivo model. RESULTS: The prognosis of patients with high LRRC17 expression was poorer than that of patients with low LRRC17 expression. Multivariate regression analysis showed that LRRC17 expression, age, and stage were independently related with survival. Knockdown of lrrc17 reduced survival rate and delayed development in zebrafish embryos. We also found that lrrc17 is important for cell viability by protecting from p53-dependent apoptosis. CONCLUSION: LRRC17 could be a prognostic gene in ovarian cancer as it regulates cancer cell viability through the p53 pathway.

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.

Role of kif2c, A Gene Related to ALL Relapse, in Embryonic Hematopoiesis in Zebrafish.

Relapse of acute lymphoblastic leukemia (ALL) is dangerous and it worsens the prognosis of patients; however, prognostic markers or therapeutic targets for ALL remain unknown. In the present study, using databases such as TARGET, GSE60926 and GSE28460, we determined that KIF2C and its binding partner, KIF18B are overexpressed in patients with relapsed ALL compared to that in patients diagnosed with ALL for the first time. As 50% of the residues are exactly the same and the signature domain of KIF2C is highly conserved between human and zebrafish, we used zebrafish embryos as a model to investigate the function of kif2c in vivo. We determined that kif2c is necessary for lymphopoiesis in zebrafish embryos. Additionally, we observed that kif2c is not related to differentiation of HSCs; however, it is important for the maintenance of HSCs as it provides survival signals to HSCs. These results imply that the ALL relapse-related gene KIF2C is linked to the survival of HSCs. In conclusion, we suggest that KIF2C can serve as a novel therapeutic target for relapsed ALL.

MicroRNA-98 is a prognostic factor for asbestos-induced mesothelioma.

Malignant pleural mesothelioma (MPM) is a type of cancer characterized by a short survival time and poor prognosis. Malignant pleural mesothelioma is most frequently associated with exposure to asbestos and other elongated mineral fibers. The aim of this study was to examine molecular differences between asbestos-exposed and non-exposed MPM patients and assess prognostic significances of molecular factors. Clinical and genetic data were downloaded from Cancer Genome Atlas. To identify the molecular differences, Significant Analysis of Microarray method was used. Prognostic significances of differentially expressed genes were confirmed by using Kaplan-Meier curve with the Log-Rank test. Although mRNAs did not exhibit any significant differences between the two patient groups, nine miRNAs were found to be down-regulated in the asbestos-exposed group. The top five pathways most relevant to the selected miRNAs were extracted through pathway enrichment analysis. Survival analysis revealed that high expression of only hsa-miR-98 was significantly associated with poor prognosis in patients with asbestos-exposed MPM. Evidence suggests that management of the aggressiveness and progression of asbestos-induced MPM may require high levels of hsa-miR-98 due to its tumor-suppressive role. This study might be helpful in enhancing our understanding of the biological mechanisms underlying asbestos-induced MPM and for acquiring greater insights into targeted therapy.Abbreviations: FDR: false discovery rate; MM: malignant mesothelioma; MPM: malignant pleural mesothelioma; mRNA: messenger RNA; miRNA: microRNA; SAM: significance analysis of microarrays; TCGA: the cancer genome atlas.