A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration

CPA4 as a biomarker promotes the proliferation, migration and metastasis of clear cell renal cell carcinoma cells

Clear cell renal cell carcinoma (ccRCC) is a commonly occurring and highly aggressive urological malignancy characterized by a significant mortality rate. Current therapeutic options for advanced ccRCC are limited, necessitating the discovery of novel biomarkers and therapeutic targets. Carboxypeptidase A4 (CPA4) is a zinc-containing metallocarboxypeptidase with implications in various cancer types, but its role in ccRCC remains unexplored. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized in order to investigate the differential expression patterns of CPA4. The expression of CPA4 in ccRCC patients was further verified using immunohistochemical (IHC) examination of 24 clinical specimens. A network of protein-protein interactions (PPI) was established, incorporating CPA4 and its genes that were expressed differentially. Functional enrichment analyses were conducted to anticipate the contribution of CPA4 in the development of ccRCC. To validate our earlier study, we conducted real-time PCR and cell functional tests on ccRCC cell lines. Our findings revealed that CPA4 is overexpressed in ccRCC, and the higher the expression of CPA4, the worse the clinical outcomes such as TNM stage, pathological stage, histological grade, etc. Moreover, patients with high CPA4 expression had worse overall survival, disease-specific survival and progress-free interval than patients with low expression. The PPI network analysis highlighted potential interactions contributing to ccRCC progression. Functional enrichment analysis indicated the involvement of CPA4 in the regulation of key pathways associated with ccRCC development. Additionally, immune infiltration analysis suggested a potential link between CPA4 expression and immune response in the tumour microenvironment. Finally, cell functional studies in ccRCC cell lines shed light on the molecular mechanisms underlying the role of CPA4 in promoting ccRCC formation. Overall, our study unveils CPA4 as a promising biomarker with prognostic potential in ccRCC. The identified interactions and pathways provide valuable insights into its implications in ccRCC development and offer a foundation for future research on targeted therapies. Further investigation of CPA4's involvement in immune responses may contribute to the development of immunotherapeutic strategies for ccRCC treatment.

A comprehensive analysis and experimental validation of TK1 in uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is becoming a main malignant cancer that threaten to women's health. Thymidine kinase 1 (TK1) is considering to be associated with tumorigenesis and development. Nevertheless, the function of TK1 in UCEC is still unclear. Herein, we analyzed the TK1 expression level in pan-cancer and found that TK1 was upregulated in a variety of cancers including UCEC. Patients of UCEC with high expression of TK1 were related to poor outcome. TK1 was also related to clinical stage, histologic grade and lymph node metastasis. Abnormal expression of TK1 in UCEC was related to promoter methylation while gene mutation was not frequent. TK1 and its associated genes appeared to be prominent in cell cycle and DNA replication, according to GO and KEGG analysis. Analysis of immune infiltration revealed a negative correlation between TK1 and CD8 + T cells, macrophages, and dendritic cells. In vitro experiments, TK1 knockdown resulted in the inhibition of proliferation, migration, invasion and EMT in UCEC cell lines.

TGFA expression is associated with poor prognosis and promotes the development of cervical cancer

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the second most common cancers in women aged 20-39. While HPV screening can help with early detection of cervical cancer, many patients are already in the medium to late stages when they are identified. As a result, searching for novel biomarkers to predict CESC prognosis and propose molecular treatment targets is critical. TGFA is a polypeptide growth factor with a high affinity for the epidermal growth factor receptor. Several studies have shown that TGFA can improve cancer growth and progression, but data on its impact on the occurrence and advancement of CESC is limited. In this study, we used clinical data analysis and bioinformatics techniques to explore the relationship between TGFA and CESC. The results showed that TGFA was highly expressed in cervical cancer tissues and cells. TGFA knockdown can inhibit the proliferation, migration and invasion of cervical cancer cells. In addition, after TGFA knockout, the expression of IL family and MMP family proteins in CESC cell lines was significantly reduced. In conclusion, TGFA plays an important role in the occurrence and development of cervical cancer. Therefore, TGFA may become a new target for cervical cancer treatment.

CD72, a new immune checkpoint molecule, is a novel prognostic biomarker for kidney renal clear cell carcinoma

BACKGROUND

The incidence and mortality of clear cell carcinoma of the kidney increases yearly. There are limited screening methods and advances in treating kidney renal clear cell carcinoma (KIRC). It is important to find new biomarkers to screen, diagnose and predict the prognosis of KIRC. Some studies have shown that CD72 influences the development and progression of colorectal cancer, nasopharyngeal cancer, and acute lymphoid leukemia. However, there is a lack of research on the role of CD72 in the pathogenesis of KIRC. This study aimed to determine whether CD72 is associated with the prognosis and immune infiltration of KIRC, providing an essential molecular basis for the early non-invasive diagnosis and immunotherapy of KIRC.

METHODS

Using TCGA, GTE, GEO, and ImmPort databases, we obtained the differentially expressed mRNA (DEmRNA) associated with the prognosis and immunity of KIRC patients. We used the Kruskal-Wallis test to identify clinicopathological parameters associated with target gene expression. We performed univariate and multivariate COX regression analyses to determine the effect of target gene expression and clinicopathological parameters on survival. We analyzed the target genes' relevant functions and signaling pathways through enrichment analysis. Finally, the correlation of target genes with tumor immune infiltration was explored by ssGSEA and Spearman correlation analysis.

RESULTS

The results revealed that patients with KIRC with higher expression of CD72 have a poorer prognosis. CD72 was associated with the Pathologic T stage, Pathologic stage, Pathologic M stage, Pathologic N stage, Histologic grade in KIRC patients, Laterality, and OS event. It was an independent predictor of the overall survival of KIRC patients. Functional enrichment analysis showed that CD72 was significantly enriched in oncogenic and immune-related pathways. According to ssGSEA and Spearman correlation analysis, CD72 expression was significantly associated with tumor immune cells and immune checkpoints.

CONCLUSION

Our study suggests that CD72 is associated with tumor immunity and may be a biomarker relevant to the diagnosis and prognosis of KIRC patients.

RNA 5-Methylcytosine regulators are associated with cell adhesion and predict prognosis of endometrial cancer

Background

RNA methylation is a significant form of post-transcriptional modification that has been implicated in various diseases, including cancers. One prominent type of RNA methylation is 5-Methylcytosine (m5C), which primarily regulates RNA stability, transcription, and translation. However, the role of m5C-related gene regulation in cell adhesion within uterine corpus endometrial carcinoma (UCEC) remains unexplored. Therefore, the objective of this study was to investigate the association between RNA m5C methylation and UCEC and develop a prognostic predictive model to forecast survival outcomes in UCEC patients.

Methods

The RNA datasets were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The dataset was used to explore the interaction relationships of m5C regulators in UCEC. Unsupervised clustering analysis identified clusters with distinct m5C modification patterns. Different clusters underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment level analysis to investigate the effects of pathways related to m5C methylation, which were further validated through in vitro cellular experiments. A prognostic predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis.

Results

Two clusters with distinct m5C modification patterns were identified using unsupervised cluster analysis. Furthermore, the prognosis of cluster 2 was found to be worse. Enrichment analysis showed alterations in cell adhesion-related pathways in both clusters, as well as differences between the clusters. Through this analysis, we identified 25 genes with significant prognostic value. Finally, a prognostic predictive model comprising NSUN2 and YBX1 was constructed.

Conclusions

In conclusion, diverse m5C modification patterns display distinct cell adhesion properties in UCEC, which are correlated with prognosis and offer significant potential as prognostic markers for UCEC assessment. We developed a prognostic predictive model to accurately predict the prognosis of UCEC.

Is HOXA5 a Novel Prognostic Biomarker for Uterine Corpus Endometrioid Adenocarcinoma?

Endometrial cancer (EC) is one of the most pervasive malignancies in females worldwide. HOXA5 is a member of the homeobox (HOX) family and encodes the HOXA5 protein. HOXA5 is associated with various cancers; however, its association with EC remains unclear. This study aimed to determine the association between HOXA5 gene expression and the prognosis of endometrioid adenocarcinoma, a subtype of EC (EAEC). Microarray data of HOXA5 were collected from the Gene Expression Omnibus datasets, consisting of 79 samples from GSE17025 and 20 samples from GSE29981. RNA-sequencing, clinical, and survival data on EC were obtained from The Cancer Genome Atlas cohort. Survival analysis revealed that HOXA5 overexpression was associated with poor overall survival in patients with EAEC (p = 0.044, HR = 1.832, 95% CI = 1.006-3.334). Cox regression analysis revealed that HOXA5 was an independent risk factor for poor prognosis in EAEC. The overexpression of HOXA5 was associated with a higher histological grade of EAEC, and it was also associated with TP53 mutation or the high copy number of EC. Our findings suggest the potential of HOXA5 as a novel biomarker for predicting poor survival outcomes in patients with EAEC.

TRIM28 modulates nuclear receptor signaling to regulate uterine function

Estrogen and progesterone, acting through their cognate receptors the estrogen receptor α (ERα) and the progesterone receptor (PR) respectively, regulate uterine biology. Using rapid immunoprecipitation and mass spectrometry (RIME) and co-immunoprecipitation, we identified TRIM28 (Tripartite motif containing 28) as a protein which complexes with ERα and PR in the regulation of uterine function. Impairment of TRIM28 expression results in the inability of the uterus to support early pregnancy through altered PR and ERα action in the uterine epithelium and stroma by suppressing PR and ERα chromatin binding. Furthermore, TRIM28 ablation in PR-expressing uterine cells results in the enrichment of a subset of TRIM28 positive and PR negative pericytes and epithelial cells with progenitor potential. In summary, our study reveals the important roles of TRIM28 in regulating endometrial cell composition and function in women, and also implies its critical functions in other hormone regulated systems.

PPP1R14B is a diagnostic prognostic marker in patients with uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common malignancies of the female genital tract. A recently discovered protein-coding gene, PPP1R14B, can inhibit protein phosphatase 1 (PP1) as well as different PP1 holoenzymes, which are important proteins regulating cell growth, the cell cycle, and apoptosis. However, the association between PPP1R14B expression and UCEC remains undefined. The expression profiles of PPP1R14B in multiple cancers were analysed based on TCGA and GTE databases. Then, PPP1R14B expression in UCEC was investigated by gene differential analysis and single gene correlation analysis. In addition, we performed gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis, and Kaplan-Meier survival analysis to predict the potential function of PPP1R14B and its role in the prognosis of UCEC patients. Then, a tool for predicting the prognosis of UCEC, namely, a nomogram model, was constructed. PPP1R14B expression was higher in UCEC tumour tissues than in normal tissues. The results revealed that PPP1R14B expression was indeed closely associated with tumour development. The results of Kaplan-Meier plotter data indicated that patients with high PPP1R14b expression had poorer overall survival, disease-specific survival, and progression-free interval than those with low expression. A nomogram based on the results of multifactor Cox regression was generated. PPP1R14B is a key player in UCEC progression, is associated with a range of adverse outcomes, and can serve as a prognostic marker in the clinic.

CXCR3 predicts the prognosis of endometrial adenocarcinoma

OBJECTIVES

Currently, endometrial adenocarcinoma lacks an effective prognostic indicator. This study was to develop and validate a gene biomarker and a nomogram to predict the survival of endometrial adenocarcinoma, explore potential mechanisms and select sensitive drugs.

METHODS

425 endometrial adenocarcinoma cases with RNA sequencing data from TCGA were used to identify the most immune-related module by WGCNA. As an external test set, 103 cases from GSE17025 were used. Immune-related genes were downloaded from Innate DB. The three sets of data were used to identify the prognostic genes. Based on 397 cases with complete clinical data from TCGA, randomly divided into the training set (n = 199) and test set (n = 198), we identified CXCR3 as the prognostic gene biomarker. Age, grade, FIGO stage, and risk were used to develop and validate a predictive nomogram. AUC, C-index, calibration curve and K-M estimate evaluated the model's predictive performance. KEGG enrichment analysis, immune functions, TMB, the effectiveness of immunotherapy, and drug sensitivity between the high-risk and low-risk groups.

RESULTS

CXCR3 was identified as a prognostic biomarker. We calculated the risk score and divided the cases into the high-risk and low-risk groups by the median value of the risk score. The OS of the high-risk group was better than the low-risk group. The risk was the prognostic indicator independent of age, grade, and FIGO stage. We constructed the nomogram including age, grade, FIGO stage, and risk to predict the prognosis of endometrial adenocarcinoma. The top five KEGG pathways enriched by the DEGs between the high- and low-risk groups were viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interaction, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and cell adhesion molecules. We analyzed the difference in immune cells and found that CD8+ T cells, activated CD4+ T cells, T helper cells, monocytes, and M1 macrophages were infiltrated more in the low-risk group. However, M0 macrophages and activated dendritic cells were more in the high-risk group. The immune function including APC coinhibition, APC costimulation, CCR, checkpoint, cytolytic activity, HLA, inflammation-promoting, MHC-I, parainflammation, T cell coinhibition, T cell costimulation, type I-IFN-response, and type II-IFN-response were better in the low-risk group. TMB and TIDE scores were both better in the low-risk group. By 'the pRRophetic' package, we found 56 sensitive drugs for different risk groups.

CONCLUSION

We identified CXCR3 as the prognostic biomarker. We also developed and validated a predictive nomogram model combining CXCR3, age, histological grade, and FIGO stage for endometrial adenocarcinoma, which could help explore the precise treatment.

Solute Carrier Family 7 Member 11 (SLC7A11) is a Potential Prognostic Biomarker in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a common type of gynecological cancers, second only to cervical cancer in incidence. Thus, it is necessary to develop effective therapies and identify biomarkers for its prognosis. Solute carrier family 7 member 11 (SLC7A11) is well known for its role in maintaining the intracellular glutathione level and preventing oxidative-stress-induced cell death. However, the association between SLC7A11 expression and prognosis as well as the correlation between tumor-infiltrating immune cells (TIICs) and immunotherapy of UCEC has rarely been reported. This study aims to evaluate the prognostic significance and immune cell infiltration level of SLC7A11 in UCEC.

Methods

Bioinformatics analysis tools and databases, including R software, National Center for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), GEPIA2, Sangerbox, Kaplan-Meier (K-M) Plotter, TISIDB, and TIMER2, were utilized to measure the expression level and clarify the clinical significance of SLC7A11 in UCEC.

Results

SLC7A11 expression was dramatically up-regulated in UCEC patients and associated with prognosis. DNA methylation levels in the SLC7A11-promoter region were significantly higher in normal participants than in patients with UCEC. We also showed that SLC7A11 overexpression was associated with TIICs, immune checkpoint blockers (ICBs), and immunotherapy response in UCEC. The half-maximal inhibitory concentration (IC50) results obtained with the cohort from TCGA showed that Z-VAD-FMK (Caspase inhibitor), S-Triphenylmethyl-L-cysteine (S-Trityl-L-cysteine), and TAE684 (ALK inhibitor) had higher IC50 values in low-expression patient (p < 0.05).

Conclusion

SLC7A11 overexpression is associated with favorable prognosis of patients with UCEC and is associated with TIICs and the responses to immunotherapy.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

KNL1 is a prognostic and diagnostic biomarker related to immune infiltration in patients with uterine corpus endometrial carcinoma

Background

The incidence and mortality of uterine corpus endometrial carcinoma (UCEC) are increasing yearly. There is currently no screening test for UCEC, and progress in its treatment is limited. It is important to identify new biomarkers for screening, diagnosing and predicting the outcomes of UCEC. A large number of previous studies have proven that KNL1 is crucial in the development of lung cancer, colorectal cancer and cervical cancer, but there is a lack of studies about the role of KNL1 in the development of UCEC.

Methods

The mRNA and protein expression data of KNL1 in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and UALCAN databases and related clinical data were used to analyze the expression differences and clinical correlations of KNL1 in UCEC. A total of 108 clinical samples were collected, and the results of bioinformatics analysis were verified by immunohistochemistry. KNL1 and its related differentially expressed genes were used to draw a volcano map, construct a PPI protein interaction network, and perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA) and immune infiltration analysis to predict the function of KNL1 during UCEC progression. The prognostic data of TCGA and 108 clinical patients were used to analyze the correlation of KNL1 expression with the survival of patients, and KM survival curves were drawn. The UCEC cell lines Ishikawa and Hec-1-A were used to verify the function of KNL1.

Results

KNL1 is significantly overexpressed in UCEC and is associated with a poor prognosis. KNL1 overexpression is closely related to cell mitosis, the cell cycle and other functions and is correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage, histological grade and other characteristics of UCEC patients. Knockdown of KNL1 expression in UCEC cell lines can inhibit their proliferation, invasion, metastasis and other phenotypes.

Conclusion

KNL1 is a prognostic and diagnostic biomarker associated with immune evasion in patients with UCEC.

BCHE as a Prognostic Biomarker in Endometrial Cancer and Its Correlation with Immunity

Background

In developed countries, the most common gynecologic malignancy is endometrial carcinoma (EC), making the identification of EC biomarkers extremely essential. As a natural enzyme, butyrylcholinesterase (BCHE) is found in hepatocytes and plasma. There is a strong correlation between BCHE gene mutations and cancers and other diseases. The aim of this study was to analyze the role of BCHE in patients with EC.

Methods

A variety of analyses were conducted on The Cancer Genome Atlas (TCGA) data, including differential expression analysis, enrichment analysis, immunity, clinicopathology, and survival analysis. The Gene Expression Omnibus (GEO) database was used to validate outcomes. Using R tools, Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) analyses revealed the potential mechanisms of BCHE in EC. Sangerbox tools were used to delve into the relations between BCHE expression and tumor microenvironment, including microsatellite instability (MSI), tumor neoantigen count (TNC), and tumor mutation burden (TMB). BCHE's genetic alteration analysis was conducted by cBioPortal. In addition, the Human Protein Atlas (HPA) was used to validate the outcomes by immunohistochemistry, and an analysis of the protein-protein interaction network (PPI) was performed with the help of the STRING database.

Results

Based on our results, BCHE was a significant independent prognostic factor for patients with EC. The prognosis with EC was affected by age, stage, grade, histological type, and BCHE. GSEA showed that BCHE was closely related to pathways regulating immune response, including transforming growth factor-β (TGF-β) signaling pathways and cancer immunotherapy through PD1 blockade pathways. The immune analysis revealed that CD4+ regulatory T cells (Tregs) were negatively correlated with BCHE expression and the immune checkpoint molecules CD28, ADORA2A, BTNL2, and TNFRSF18 were all significantly related to BCHE. BCHE expression was also associated with TMB by genetic alteration analysis.

Conclusions

Identifying BCHE as a biomarker for EC might help predict its prognosis and could have important implications for immunotherapy.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

Human placental gene sets improve analysis of placental pathologies and link trophoblast and cancer invasion genes

INTRODUCTION

Interpretation of gene expression uses set enrichment or overrepresentation methods that depend on sets of annotated genes, such as the popular Gene Ontology. The placenta is understudied relative to other major organs creating a deficit of molecular and functional knowledge about this organ. The lack of placental and trophoblast research significantly impacts our ability to interpret the results of high throughput experiments.

METHODS

Gene sets were generated by a semi-automated re-analysis of 330 microarray and 91 RNA sequencing experiments involving placental and trophoblast samples, excluding those related to pathology. Microarray data was obtained from the Gene Expression Omnibus and processed using the R package limma. RNA-sequencing data was extracted from the short read archive and processed using Kallisto and limma. The workflow consisted of quality control for experimental design and data. Sets were generated by pairwise differential expression with a maximum of 200 genes per set.

RESULTS

We created 235 human placenta and trophoblast specific gene sets and found unique subnetworks relative to Gene Ontology. We applied these new placental gene sets to the investigation of preeclampsia and fetal growth restriction as well as invasive tumors and cell models finding matching terms related to cell types and oxygen tension (hypoxia).

DISCUSSION

The human placental gene sets provide an improved context for interpretation of high throughput gene expression studies on placental pathologies beyond the Gene Ontology. Significant enrichment of placental gene sets to cancer samples and cell models indicates a utility beyond applications to placental and trophoblast cells.

Visual Life Sciences Workflow Design Using Distributed and Heterogeneous Resources

Programming or querying usually presupposes some degree of technical familiarity with the syntax of a language and the peculiarity of the objects it manipulates to produce useful information. The degree of abstractions supported in a language helps lessen the depth of such familiarity needed, and aids in improving access to and usability of these resources. To help biologists concentrate more on their science questions and not on how to compute it, several successful workflow orchestration languages and systems have been proposed. Despite their popularity, significant limitations reduce their usability and limit applicability in novel applications. In this paper, we present a visual language, called VisFlow, for workflow orchestration using heterogeneous and distributed resources. We advance the idea that once resources are minimally described and abstracted, arbitrary workflows can be designed solely using query primitives supported in VisFlow. Its capabilities can be augmented by including computational artifacts in the form of library functions written in R, Python, and Java, or even in SQL and XQuery, making it a truly extensible system. We discuss its salient features and illustrate its capabilities using a substantial set of examples.

Improving Cancer Gene Expression Data Quality through a TCGA Data-Driven Evaluation of Identifier Filtering

Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data sets creates a collection of feature pairs, with correlations calculated for each pair. To evaluate a filtering strategy, we estimate posterior probabilities for the correctness of probesets accepted by the method. An analyst can set expected utilities that represent the trade-off between the quality and quantity of accepted features. We tested nine published probeset filtering methods and combination strategies. We used two test-beds from cancer studies providing transcriptomic and proteomic data. For reasonable utility settings, the Jetset filtering method was optimal for probeset filtering on both test-beds, even though both assay platforms were different. Further intersection with a second filtering method was indicated on one test-bed but not the other.