Dataset Information

Villi-specific Gene Expression Reveals Novel Prognostic Biomarkers in Multiple Human Cancers.

ABSTRACT: Despite many striking connections, the biological similarities between embryonic development and tumorigenesis have not been well explored. Development of the placental villi is a crucial process involving many cellular activities, including immunity, proliferation, and cell adhesion. In this study, we designed a strategy to identify the gene expression pattern of villi development and to explore the corresponding features in tumors. We discovered villi-specific genes that are highly expressed in the villus as opposed to the mature placenta and then measured the expression levels of these genes in tumors. We found large changes in the expression of villi-specific genes in multiple types of cancer. These villi-specific genes showed distinct expression patterns and were primarily involved in three biological processes: immune-related (5), proliferation-related (6), and focal adhesion-related (8); these genes were extracted from the corresponding enriched Gene Ontology (GO) terms. We observed that these genes were also dysregulated at the transcriptional level across several tumor types. Moreover, the expression of these three gene groups was associated with poor prognosis in a subset of tumors. Based on villi-specific gene expression, this correlation study indicated the existence of common gene expression patterns between embryonic development and tumorigenesis. Therefore, a systematic analysis of villi-specific gene aberrations in various tumors could serve as an indicator for identifying novel prognostic biomarkers.

SUBMITTER: Zhang B

PROVIDER: S-EPMC5604211 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Villi-specific Gene Expression Reveals Novel Prognostic Biomarkers in Multiple Human Cancers.

Zhang Botao B Feng Lin L Guo Honglin H Li Hongxia H Wang Yuanjing Y Zhang Kaitai K Yu Xuexin X Cheng Shujun S

Journal of Cancer 20170823 14

Despite many striking connections, the biological similarities between embryonic development and tumorigenesis have not been well explored. Development of the placental villi is a crucial process involving many cellular activities, including immunity, proliferation, and cell adhesion. In this study, we designed a strategy to identify the gene expression pattern of villi development and to explore the corresponding features in tumors. We discovered villi-specific genes that are highly expressed i ...[more]

PMID: 28928868

Similar Datasets

Project description:BACKGROUND: Heart failure (HF) is the most common cause of morbidity and mortality in developed countries. Here, we identify biologically relevant transcripts that are significantly altered in the early phase of myocardial infarction and are associated with the development of post-myocardial infarction HF. METHODS: We collected peripheral blood samples from patients with ST-segment elevation myocardial infarction (STEMI): n?=?111 and n?=?41 patients from the study and validation groups, respectively. Control groups comprised patients with a stable coronary artery disease and without a history of myocardial infarction. Based on plasma NT-proBNP level and left ventricular ejection fraction parameters the STEMI patients were divided into HF and non-HF groups. Microarrays were used to analyze mRNA levels in peripheral blood mononuclear cells (PBMCs) isolated from the study group at four time points and control group. Microarray results were validated by RT-qPCR using whole blood RNA from the validation group. RESULTS: Samples from the first three time points (admission, discharge, and 1 month after AMI) were compared with the samples from the same patients collected 6 months after AMI (stable phase) and with the control group. The greatest differences in transcriptional profiles were observed on admission and they gradually stabilized during the follow-up. We have also identified a set of genes the expression of which on the first day of STEMI differed significantly between patients who developed HF after 6 months of observation and those who did not. RNASE1, FMN1, and JDP2 were selected for further analysis and their early up-regulation was confirmed in HF patients from both the study and validation groups. Significant correlations were found between expression levels of these biomarkers and clinical parameters. The receiver operating characteristic (ROC) curves indicated a good prognostic value of the genes chosen. CONCLUSIONS: This study demonstrates an altered gene expression profile in PBMCs during acute myocardial infarction and through the follow-up. The identified gene expression changes at the early phase of STEMI that differentiated the patients who developed HF from those who did not could serve as a convenient tool contributing to the prognosis of heart failure.

Project description:Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequencing data could be a valuable new tool to understand differences between and within entities. Here we used machine learning analysis to identify novel diagnostic and prognostic markers and therapeutic targets for soft tissue sarcomas. Gene expression data was used from the Cancer Genome Atlas, the Genotype-Tissue Expression project and the French Sarcoma Group. We identified three groups of tumors that overlap in their molecular profiles as seen with unsupervised t-Distributed Stochastic Neighbor Embedding clustering and a deep neural network. The three groups corresponded to subtypes that are morphologically overlapping. Using a random forest algorithm, we identified novel diagnostic markers for soft tissue sarcoma that distinguished between synovial sarcoma and MPNST, and that we validated using qRT-PCR in an independent series. Next, we identified prognostic genes that are strong predictors of disease outcome when used in a k-nearest neighbor algorithm. The prognostic genes were further validated in expression data from the French Sarcoma Group. One of these, HMMR, was validated in an independent series of leiomyosarcomas using immunohistochemistry on tissue micro array as a prognostic gene for disease-free interval. Furthermore, reconstruction of regulatory networks combined with data from the Connectivity Map showed, amongst others, that HDAC inhibitors could be a potential effective therapy for multiple soft tissue sarcoma subtypes. A viability assay with two HDAC inhibitors confirmed that both leiomyosarcoma and synovial sarcoma are sensitive to HDAC inhibition. In this study we identified novel diagnostic markers, prognostic markers and therapeutic leads from multiple soft tissue sarcoma gene expression datasets. Thus, machine learning algorithms are powerful new tools to improve our understanding of rare tumor entities.

Project description:Background Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers. Aims To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes. Results Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups. Conclusions Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Project description:INTRODUCTION: Male breast cancer (MBC) is a rare and inadequately characterized disease. The aim of the present study was to characterize MBC tumors transcriptionally, to classify them into comprehensive subgroups, and to compare them with female breast cancer (FBC). METHODS: A total of 66 clinicopathologically well-annotated fresh frozen MBC tumors were analyzed using Illumina Human HT-12 bead arrays, and a tissue microarray with 220 MBC tumors was constructed for validation using immunohistochemistry. Two external gene expression datasets were used for comparison purposes: 37 MBCs and 359 FBCs. RESULTS: Using an unsupervised approach, we classified the MBC tumors into two subgroups, luminal M1 and luminal M2, respectively, with differences in tumor biological features and outcome, and which differed from the intrinsic subgroups described in FBC. The two subgroups were recapitulated in the external MBC dataset. Luminal M2 tumors were characterized by high expression of immune response genes and genes associated with estrogen receptor (ER) signaling. Luminal M1 tumors, on the other hand, despite being ER positive by immunohistochemistry showed a lower correlation to genes associated with ER signaling and displayed a more aggressive phenotype and worse prognosis. Validation of two of the most differentially expressed genes, class 1 human leukocyte antigen (HLA) and the metabolizing gene N-acetyltransferase-1 (NAT1), respectively, revealed significantly better survival associated with high expression of both markers (HLA, hazard ratio (HR) 3.6, P = 0.002; NAT1, HR 2.5, P = 0.033). Importantly, NAT1 remained significant in a multivariate analysis (HR 2.8, P = 0.040) and may thus be a novel prognostic marker in MBC. CONCLUSIONS: We have detected two unique and stable subgroups of MBC with differences in tumor biological features and outcome. They differ from the widely acknowledged intrinsic subgroups of FBC. As such, they may constitute two novel subgroups of breast cancer, occurring exclusively in men, and which may consequently require novel treatment approaches. Finally, we identified NAT1 as a possible prognostic biomarker for MBC, as suggested by NAT1 positivity corresponding to better outcome.

Dataset Information

Villi-specific Gene Expression Reveals Novel Prognostic Biomarkers in Multiple Human Cancers.

Publications

Villi-specific Gene Expression Reveals Novel Prognostic Biomarkers in Multiple Human Cancers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets