Environmental Vibrio parahaemolyticus DNA signatures validation

Insignia is a novel DNA computational system which uses highly efficient algorithms to compare bacterial genomes and to identify specific DNA signatures to distinguish a target bacterium, or group of bacteria, from all other known bacterial species. It is currently being validated using different bacterial groups, including Vibrio spp. In this study, the genomic analysis by Insignia was conducted on Vibrio parahaemolyticus, a halophilic gram-negative bacteria which constitutes a leading cause of seafood-borne disease. Insignia was used to identify 37 V. parahaemolyticus-specific signatures and to design PCR assays to validate the representative signature sequences by TaqMan essays. The 37 assays targeted loci distributed around the genome and detected genes coding for hypothetical proteins and for proteins involved in adhesion, starvation and virulence. A panel of V. parahaemolyticus environmental strains isolated from the North Adriatic Sea (Italy) and from the Black Sea (Georgia) was used to validate the selected signatures. The signature assays revealed both sensitive and specific and the method allowed a more accurate identification of the tested bacterial strains at the species level when compared to biochemical and PCR standard methods. Using Insignia, it was possible to distinguish two different groups among the strains previously identified as V. parahaemolyticus: most of the strains were included in a "V. parahaemolyticus-like group" showing nearly all of the signatures assayed while a small group of 10 strains contained only a few of the signatures tested. By sequencing the 16S rDNA of this latter group, it was confirmed that they were not V. parahaemolyticus but in fact belonged to other Vibrio species. No significant genome-wide differences were detected between the strains isolated in Italy and in Georgia though the very different geographical origin.     

The search for novel diagnostic and prognostic biomarkers in cholangiocarcinoma

The poor prognosis of cholangiocarcinoma (CCA) is in part due to late diagnosis, which is currently achieved by a combination of clinical, radiological and histological approaches. Available biomarkers determined in serum and biopsy samples to assist in CCA diagnosis are not sufficiently sensitive and specific. Therefore, the identification of new biomarkers, preferably those obtained by minimally invasive methods, such as liquid biopsy, is important. The development of innovative technologies has permitted to identify a significant number of genetic, epigenetic, proteomic and metabolomic CCA features with potential clinical usefulness in early diagnosis, prognosis or prediction of treatment response. Potential new candidates must be rigorously evaluated prior to entering routine clinical application. Unfortunately, to date, no such biomarker has achieved validation for these purposes. This review is an up-to-date of currently used biomarkers and the candidates with promising characteristics that could be included in the clinical practice in the next future. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.     

Combined analysis of secreted proteins and glycosylation identifies prognostic features in cholangiocarcinoma

Secreted proteins are overexpressed in cholangiocarcinoma (CCA) and actively involved in promoting metastatic spread. Many of these proteins possess one or more sites of glycosylation and their various glycoforms have potential utility as prognostic or diagnostic biomarkers. To evaluate the effects of secretome glycosylation on patient outcome, we elucidated the glycosylation patterns of proteins secreted by parental and metastatic CCA cells using liquid chromatography-mass spectrometry. Our analysis showed that the secretome of CCA cells was dominated by fucosylated and fucosialylated glycoforms. Based on the glycan and protein profiles, we evaluated the combined prognostic significance of glycosyltransferases and secretory proteins. Significantly, genes encoding fucosyltransferases and sialyltransferases showed favorable prognostic effects when combined with secretory protein-coding gene expression, particularly thrombospondin-1. Combining these measures may provide improved risk assessment for CCA and be used to indicate stages of disease progression.     

A network module-based method for identifying cancer prognostic signatures

Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin     

T-cell receptors provide potential prognostic signatures for breast cancer

Although T-cell receptors (TCRs) are related to the progression of breast cancer (BC), their prognostic values remain unclear. We downloaded the messenger RNA (mRNA) profiles and corresponding clinical information of 1413 BC patients from the Cancer Genome Atlas and Gene Expression Omnibus database, respectively. The different expression analysis of 104 TCRs in BC samples was performed, and the consensus clustering based on 104 TCRs was performed by using the K-mean method of R language. Univariate cox regression analysis was used to screen TCRs significantly associated with the prognosis of BC, and LASSO Cox analysis was applied to optimize key TCRs. The risk score was calculated using the prognostic model constructed based on six optimal TCRs, and multivariate Cox regression analysis was used to determine whether it was an independent prognostic signature. Finally, the nomogram was constructed to predict the overall survival of BC patients. Six optimal TCRs (ZAP70, GRAP2, NFKBIE, IFNG, NFKBIA, and PAK5), which were favorable for the prognosis of BC patients, were screened. Risk score could reliably predict the prognosis of BC patients as an independent prognostic signature. In addition, when bringing into two independent prognostic signatures, age and risk score, the nomogram model could better predict the overall survival of BC patients. Our results suggested that the poor prognosis of BC patients with high risk might be due to an immunosuppressive microenvironment. In summary, a prognostic risk model based on six TCRs was established and could efficiently predict the prognosis of BC patients.     

Exploration of a novel prognostic risk signatures and immune checkpoint molecules in endometrial carcinoma microenvironment

In this study, we devoted to investigate immune-related genes and tumor microenvironment (TME) in EC based on The Cancer Genome Atlas (TCGA) database. In total 799 up-regulated and 139 down-regulated immune-related and differentially expressed genes in EC were investigated for functional annotations and prognosis. By a conjoint Cox regression analysis, we built two risk models for OS and DFS, as well as the consistent nomograms. Immune-related pathways were revealed mostly enriched in the low-risk group. By further analyzing TME based on the risk signatures, the higher immune cell infiltration and activation, lower tumor purity and higher tumor mutational burden were found in low-risk group, which presented a better prognosis. Both the expression and immunophenoscore of immune checkpoints PD-1, CTLA4, PD-L1 and PD-L2 increased significantly in low-risk group. These findings may provide new ideas for novel biomarkers and immunotherapy targets in EC.     

NK cell and IFN signatures are positive prognostic biomarkers for resectable pancreatic cancer

To establish prognostic biomarkers and to identify potential novel therapeutic targets, we performed integrative immunomonitoring of blood and tumor in patients with resectable pancreatic cancer. Flow cytometry (FC) was employed for phenotyping immune cells, multiplex bead assays for plasma cytokine and chemokine determination, and RNA-Seq for the analysis of gene expression in the tumor. Nineteen pancreatic cancer patients were stratified into those with longer or shorter than median recurrence-free survival after surgery (median, 426 days). There were no significant differences between the two groups for clinical parameters including age, sex, surgical procedure, stage, or postoperative adjuvant therapy. However, we found that the percentages of NK cells as assessed by FC in peripheral blood mononuclear cells were higher in patients with late recurrence (P?=?.037). RNA-Seq data indicated no differences in the amount of immune cells or stromal cells between the two groups, although NK cells in the tumor did tend to be higher in patients with late recurrence (P?=?.058). Type I and II IFN signatures were enriched in late-recurring tumors (FDR q-value <0.001), while genes related to KRAS signaling and the epithelial mesenchymal transition (EMT) were enriched in early recurrence. We conclude that tumor-intrinsic properties of metastasis and recurrence influence prognosis, whereas NK cells that might contribute to prevent metastasis are associated with longer recurrence-free survival. Therefore, enhancement of NK cell activity and inhibition of the EMT and KRAS signaling might represent appropriate therapeutic targets following surgical resection of pancreatic cancer.     

Development and validation of stable ferroptosis- and pyroptosis-related signatures in predicting prognosis and immune status in breast cancer

To develop and validate the predictive effects of stable ferroptosis- and pyroptosis-related features on the prognosis and immune status of breast cancer (BC). We retrieved as well as downloaded ferroptosis- and pyroptosis-related genes from the FerrDb and GeneCards databases. The minimum absolute contraction and selection operator (LASSO) algorithm in The Cancer Genome Atlas (TCGA) was used to construct a prognostic classifier combining the above two types of prognostic genes with differential expression, and the Integrated Gene Expression (GEO) dataset was used for validation. Seventeen genes presented a close association with BC prognosis. Thirteen key prognostic genes with prognostic value were considered to construct a new expression signature for classifying patients with BC into high- and low-risk groups. Kaplan–Meier analysis revealed a worse prognosis in the high-risk group. The receiver operating characteristic (ROC) curve and multivariate Cox regression analysis identified its predictive and independent features. Immune profile analysis showed that immunosuppressive cells were upregulated in the high-risk group, and this risk model was related to immunosuppressive molecules. We successfully constructed combined features of ferroptosis and pyroptosis in BC that are closely related to prognosis, clinicopathological and immune features, chemotherapy efficacy and immunosuppressive molecules. However, further experimental studies are required to verify these findings.     

Development and validation of a prognostic gene-expression signature for lung adenocarcinoma

Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.     

Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures

Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order creating a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view, without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1) the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2) the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin (http://binom.curie.fr).     

Plasma lipidomics reveals potential prognostic signatures within a cohort of cystic fibrosis patients

Cystic fibrosis (CF) is associated with abnormal lipid metabolism. We have recently shown variations in plasma levels of several phosphatidylcholine (PC) and lysophopshatidylcholine (LPC) species related to disease severity in CF patients. Here our goal was to search for blood plasma lipid signatures characteristic of CF patients bearing the same mutation (F508del) and different phenotypes, and to study their correlation with forced expiratory volume in 1 s (FEV1) and Pseudomonas aeruginosa chronic infection, evaluated at the time of testing (t = 0) and three years later (t = 3). Samples from 44 F508del homozygotes were subjected to a lipidomic approach based on LC-ESI-MS. Twelve free fatty acids were positively correlated with FEV1 at t = 0 (n = 29). Four of them (C20:3n-9, C20:5n-3, C22:5n-3, and C22:6n-3) were also positively correlated with FEV1 three years later, along with PC(32:2) and PC(36:4) (n = 31). Oleoylethanolamide (OEA) was negatively correlated with FEV1 progression (n = 17). Chronically infected patients at t = 0 showed lower PC(32:2), PC(38:5), and C18:3n-3 and higher cholesterol, cholesterol esters, and triacylglycerols (TAG). Chronically infected patients at t = 3 showed significantly lower levels of LPC(18:0). These results suggest a potential prognostic value for some lipid signatures in, to our knowledge, the first longitudinal study aimed at identifying lipid biomarkers for CF.