High neuropilin and tolloid-like 1 expression associated with metastasis and poor survival in epithelial ovarian cancer via regulation of actin cytoskeleton

Screening and identification of key biomarkers associated with endometriosis using bioinformatics and next-generation sequencing data analysis

Background

Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis.

Methods

Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes.

Results

A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network.

Conclusions

This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis.

KIF2A correlates with lymphovascular invasion and higher tumor stage, and can be used to predict worse prognosis in patients with endometrial carcinoma

Kinesin family protein 2A (KIF2A) is a microtubule depolymerase that participates in the progression of various cancers; however, its clinical utility in endometrial carcinoma (EC) remains unclear. The aim of the present study was to assess KIF2A expression and its relationship with prognosis in patients with EC. Data from 230 patients with EC who underwent tumor resection were reviewed in the current, retrospective study. KIF2A expression was measured in 230 formalin-fixed paraffin-embedded (FFPE) specimens of tumor tissue and 50 FFPE specimens of non-tumor tissue using immunohistochemistry (IHC). KIF2A expression was elevated in EC tumor tissue vs. non-tumor tissue (P<0.001). Furthermore, tumor KIF2A expression was linked with lymphovascular invasion (P=0.004) and higher International Federation of Gynecology and Obstetrics (FIGO) stage (P=0.001). High tumor KIF2A expression (IHC score>3) was correlated with shorter disease-free survival (DFS; P=0.014) and overall survival (OS; P=0.012). Moreover, the time-dependent receiver operating characteristic curves revealed that tumor KIF2A expression had an acceptable use for estimating the relapse and death risks at each timepoint within 6 years, with each area under the curve remaining stable at ≥0.7. Notably, tumor KIF2A expression (high vs. low) independently forecast shorter DFS (hazard ratio, 2.506; P=0.013), but not OS (P>0.05). Furthermore, information from The Human Protein Atlas database indicated that high tumor KIF2A expression was associated with worse OS in patients with EC (P=0.027). Tumor KIF2A is not only associated with lymphovascular invasion and higher FIGO stage, but also reflects unfavorable survival in patients with EC.

Satellite cells sourced from bull calves and dairy cows differs in proliferative and myogenic capacity - Implications for cultivated meat

Cultivated meat produced with primary muscle satellite cells (SCs) will need a continuous supply of isolated cell material from relevant animal donors. Factors such as age, sex, and breed, along with the sustainability and availability of donor animals, could determine the most appropriate donor type for an efficient production. In this study, we focus on the proliferation and differentiation of bovine SCs isolated from bull calf and dairy cow muscle samples. The proliferative performance of bull calf SCs was significantly better than SCs from dairy cows, however a dynamic differentiation assay revealed that the degree of fusion and formation of myotubes were similar between donor types. Furthermore, the proliferation of SCs from both donor types was enhanced using an in-house developed serum-free media compared to 10% FBS, which also delayed myogenic differentiation and increased final cell population density. Using gene chip transcriptomics, we identified several differentially expressed genes between the two donor types, which could help explain the observed cellular differences. This data also revealed a high biological variance between the three replicate animals within donor type, which seemed to be decreased when using our in-house serum-free media. With the use of the powerful imaging modalities of Cytation 5, we developed a novel high contrast brightfield-enabled label-free myotube quantification method along with a more efficient end-point fusion analysis using Phalloidin-staining. The results give new insights into the bovine SC biology and potential use of bull calves and dairy cows as relevant donor animals for cultivated beef cell sourcing. The newly developed differentiation assays will further enhance future research within the field of cultivated meat and SC biology.

Identification of a Prognostic Microenvironment-Related Gene Signature in Glioblastoma Patients Treated with Carmustine Wafers

Despite the state-of-the-art treatment, patients diagnosed with glioblastoma (GBM) have a median overall survival (OS) of 14 months. The insertion of carmustine wafers (CWs) into the resection cavity as adjuvant treatment represents a promising option, although its use has been limited due to contrasting clinical results. Our retrospective evaluation of CW efficacy showed a significant improvement in terms of OS in a subgroup of patients. Given the crucial role of the tumor microenvironment (TME) in GBM progression and response to therapy, we hypothesized that the TME of patients who benefited from CW could have different properties compared to that of patients who did not show any advantage. Using an in vitro model of the glioma microenvironment, represented by glioma-associated-stem cells (GASC), we performed a transcriptomic analysis of GASC isolated from tumors of patients responsive and not responsive to CW to identify differentially expressed genes. We found different transcriptomic profiles, and we identified four genes, specifically down-regulated in GASC isolated from long-term survivors, correlated with clinical data deposited in the TCGA–GBM dataset. Our results highlight that studying the in vitro properties of patient-specific glioma microenvironments can help to identify molecular determinants potentially prognostic for patients treated with CW.

ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using R

The stage of cancer is a discrete ordinal response that indicates the aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with the stage of cervical cancer to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients have been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets, such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which enhances the runjags R package by enabling users to easily fit cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P > N, such as for TCGA and other high-throughput genomic data. We demonstrate the use of this package by applying it to the TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional datasets, and it effectively performs variable selection.

Upregulation of FGD6 Predicts Poor Prognosis in Gastric Cancer

Background: The aim of this study was to investigate the prognostic significance of faciogenital dysplasia 6 (FGD6) in gastric cancer (GC).

Methods: The data of GC patients from The Cancer Genome Atlas (TCGA) database were used for the primary study. Then, our data were validated by the GEO database and RuiJin cohort. The relationship between the FGD6 level and various clinicopathological features was analyzed by logistic regression and univariate Cox regression. Multivariate Cox regression analysis was used to evaluate whether FGD6 was an independent prognostic factor for survival of patients with GC. The relationship between FGD6 and overall survival time was explored by the Kaplan–Meier method. In addition, gene set enrichment analysis (GSEA) was performed to investigate the possible biological processes of FGD6.

Results: The FGD6 level was significantly overexpressed in GC tissues, compared with adjacent normal tissues. The high expression of FGD6 was related to a high histological grade, stage, and T classification and poor prognosis of GC. Multivariate Cox regression analysis showed that FGD6 was an independent prognostic factor for survival of patients with GC. GSEA identified that the high expression of FGD6 was mainly enriched in regulation of actin cytoskeleton.

Conclusion: FGD6 may be a prognostic biomarker for predicting the outcome of patients with GC.

Identification of microenvironment related potential biomarkers of biochemical recurrence at 3 years after prostatectomy in prostate adenocarcinoma

Prostate adenocarcinoma is one of the leading adult malignancies. Identification of multiple causative biomarkers is necessary and helpful for determining the occurrence and prognosis of prostate adenocarcinoma. We aimed to identify the potential prognostic genes in the prostate adenocarcinoma microenvironment and to estimate the causal effects simultaneously. We obtained the gene expression data of prostate adenocarcinoma from TCGA project and identified the differentially expressed genes based on immune-stromal components. Among these genes, 68 were associated with biochemical recurrence at 3 years after prostatectomy in prostate adenocarcinoma. After adjusting for the minimal sets of confounding covariates, 14 genes (TNFRSF4, ZAP70, ERMN, CXCL5, SPINK6, SLC6A18, CHRM2, TG, CLLU1OS, POSTN, CTSG, NETO1, CEACAM7, and IGLV3-22) related to the microenvironment were identified as prognostic biomarkers using the targeted maximum likelihood estimation. Both the average and individual causal effects were obtained to measure the magnitude of the effect. CIBERSORT and gene set enrichment analyses showed that these prognostic genes were mainly associated with immune responses. POSTN and NETO1 were correlated with androgen receptor expression, a main driver of prostate adenocarcinoma progression. Finally, five genes were validated in another prostate adenocarcinoma cohort (GEO: GSE70770). These findings might lead to the improved prognosis of prostate adenocarcinoma.

Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics

Collagen type X alpha 1 (COL10A1) is a member of the collagen family and the main matrix component. However, COL10A1 expression and prognosis relationship remains unclear in gastric cancer (GC). Through the analysis of database of Oncomine, the Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus (GEO), in contrast to the tissue of normal gastric, COL10A1 in gastric cancer, had been upregulated. The high expression of COL10A1 was obviously related to T stage (P = 0.025) and lymph node metastasis (P = 0.025). It has been illustrated by the analysis of logistic regression that COL10A1's heightened expression in gastric cancer had been essentially linked with pathological stage, tumor differentiation, and T classification. The Kaplan-Meier curve in the Kaplan-Meier plotter database (P = 0.0371) and GSE84437 (P = 0.002) indicate that patients with high COL10A1 expression possess poor prognosis, specifically GC patients with lymph node metastasis have it. TCGA's Multivariate analysis (P = 0.025) and GSE84437 dataset (P = 0.034) show that high expression COL10A1 is a key independent predictor of poor overall survival. Searching KEGG pathway enrichment by GSEA, the results suggested that 29 pathways were enriched. qRT-PCR technique was used for verification of the COL10A1's high expression in gastric cancer in contrast to the normal gastric tissues. In conclusion, COL10A1 is of great importance in predicting the survival rate of GC patients.

Identification of key factors associated with early- and late-onset ovarian serous cystadenocarcinoma

Aim: To uncover the molecular mechanisms of early-onset ovarian serous cystadenocarcinoma (EOOSC; patients <50 years old) and late-onset ovarian serous cystadenocarcinoma (LOOSC; patients ≥50 years old). Materials & methods: Bioinformatics was utilized to identify the key factors. Results: 478 EOOSC and 899 LOOSC individual differentially expressed genes were identified and enriched in different pathways. The expression of key genes LAG3, LRRC63 and MT1B significantly influenced the overall survival of EOOSC patients. The expression of key genes RDH12, NTSR1, ZSCAN16, CT45A3 and EPPIN_WFDC6 significantly affected the overall survival of LOOSC patients. Conclusions: The molecular mechanisms of EOOSC and LOOSC appear to be different, so that patients might be treated individually in respect of age.

High neuropilin and tolloid-like 1 expression associated with metastasis and poor survival in epithelial ovarian cancer via regulation of actin cytoskeleton

Abnormal expression of neuropilin and tolloid‐like 1 (NETO1) has been detected in some human carcinomas. However, the expression of NETO1 and the underlying mechanism in epithelial ovarian cancer (EOC) remain unknown. In this study, we found that a higher NETO1 expression in EOC tissue samples compared to normal ovarian tissue samples was significantly correlated with worse overall survival. Additionally, Cox regression analysis suggested that NETO 1 was independently associated with overall survival. NETO1 overexpression enhanced the EOC cells’ migration and invasion capability in vitro via regulation of actin cytoskeleton. Mechanistically, silencing NETO1 reduced the expression of β‐tubulin, F‐actin and KIF2A. In conclusion, our results demonstrated the critical role of NETO1 in EOC invasion, and therapies aimed at inhibiting its expression or activity might significantly control EOC growth, invasion and metastatic dissemination.