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Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients.

ABSTRACT: Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients is an unsolved issue. Here, we systematically analyzed the aberrant expression patterns of cervical cancer using RNA-Seq data from The Cancer Genome Atlas (TCGA). We incorporated gene profiling, molecular signatures, functional and pathway information with gene set enrichment and protein-protein interaction (PPI) network analysis, to identify sub-networks of genes. Those identified genes relating to DNA replication and DNA repair-mediated signaling pathways associated with systemic lupus erythematosus (SLE). Next, we combined cross-validated prognostic scores to build an integrated prognostic model for survival prediction. The combined approach revealed that the DNA repair-mediated including the functional interaction module of 18 histone genes (Histone cluster 1 H2A, B and H4), were significantly correlated with the survival rate. Furthermore, five of these histone genes were highly expressed in three cervical cancer cohorts from the Oncomine database. Comparison of high and low histone variant-expressing human cervical cancer cell lines revealed different responses to DNA damage, suggesting protective functions of histone genes against DNA damage. Collectively, we provide evidence that two SLE-associated gene sets (HIST1H2BD and HIST1H2BJ; and HIST1H2BD, HIST1H2BJ, HIST1H2BH, HIST1H2AM and HIST1H4K) can be used as prognostic factors for survival prediction among cervical cancer patients.

SUBMITTER: Li X

PROVIDER: S-EPMC5705706 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients.

Li Xiaofang X Tian Run R Gao Hugh H Yang Yongkang Y Williams Bryan R G BRG Williams Bryan R G BRG Gantier Michael P MP McMillan Nigel A J NAJ Xu Dakang D Hu Yiqun Y Gao Yan'e Y

Scientific reports 20171128 1

Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients is an unsolved issue. Here, we systematically analyzed the aberrant expression patterns of cervical cancer using RNA-Seq data from The Cancer Genome Atlas (TCGA). We incorporated gene profiling, molecular signatures, functional and pathway information with gene set enrichment and protein-protein interaction (PPI) network analysis, to identify sub-networks of genes. Those identified genes relating to DN ...[more]

PMID: 29184082

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Project description:BackgroundAn accurate and robust gene signature is of the utmost importance in assisting oncologists to make a more accurate evaluation in clinical practice. In our study, we extracted key mRNAs significantly related to colorectal cancer (CRC) prognosis and we constructed an expression-based gene signature to predict CRC patients' survival.MethodsmRNA expression profiles and clinicopathological data of colon adenocarcinoma (COAD) cases and rectum adenocarcinoma (READ) were collected from The Cancer Genome Atlas database to investigate gene expression alteration associated to the prognosis of CRC. Differentially expressed mRNAs (DEMs) were detected between COAD/READ and normal tissue samples. Relying on a univariate and multivariate Cox regression analyses, a mRNA panel signature was established and used for predicting the overall survival (OS) in CRC patients. Receiver operating characteristic curve was used to evaluate the prognosis performance of our model through calculating the AUC values corresponding to the 3-year and 5-year survival. To assess the performance of gene signature in the given cancer subgroups (CRC entire cohort, COAD cohort, and READ cohort), a stratified analysis was carried out according to clinical factors.ResultsA total of 5341 and 5594 DEMs were collected from COAD vs. normal tissue samples, and READ vs. normal samples respectively. A univariate regression analysis for the common DEMs between COAD and READ cohorts resulted in 14 common mRNAs related to OS. The multivariate Cox regression analysis revealed that 6 of these mRNAs (EPHA6, TIMP1, IRX6, ART5, HIST3H2BB, and FOXD1) had significant prognostic value allowing the discrimination between high- and low-risk patients, implying poor and good outcomes, respectively. The stratified analysis identified 6-gene signature as an independent prognostic signature in predicting CRC patients' survival.ConclusionsThe 6-gene signature could act as an independent biomarker for survival prediction of CRC patients.

Project description:ObjectiveTo identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma.MethodsBased on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma.Results34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease.ConclusionThe prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.

Project description:BackgroundThe exosome is of vital importance throughout the entire progression of cancer. Because of the lack of effective biomarkers in ovarian cancer (OV), we intend to investigate the connection between exosomes and tumor immune microenvironment to verify that exosome-related genes (ERGs) can precisely forecast the prognosis of OV patients.MethodsFirst, 117 ERGs in The Cancer Genome Atlas (TCGA) dataset were recognized. Afterwards, the risk signature consisting of four ERGs with prognostic significance was built by univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. We also validated the risk signature by Kaplan-Meier analysis, receiver operating characteristic curve analysis and principal component analysis. Furthermore, gene set enrichment analysis was performed to compare the enrichment patterns between the two risk subgroups. The connections between the exosome-related gene risk score (ERGRS) and clinical features, immune infiltration, immune checkpoint-related genes, copy number variation, and drug sensitivity were explored. We also assessed the function of the ERGRS to forecast immunotherapeutic efficacy by immunophenoscore (IPS).ResultsThe high-risk group had a worse prognosis than the group with low risk. We verified that the established model possessed a relatively good prognostic value. Pathway enrichment analysis indicated that the genome-wide group with low risk was enriched in immune-related pathways. We discovered that resting dendritic cells and stromal scores were upregulated in patients with high risk in the TCGA and Gene Expression Omnibus (GEO) cohorts. Moreover, the expression of six common immune checkpoint inhibitor targets was assessed, which revealed that the expression levels of CD274 (PD-L1), PDCD1 (PD-1), and IDO1 in patients with high risk were lower than those in patients with low risk. Afterwards, the low-risk group had higher IPS across the four immunotherapies, implying that it had better effects of immunotherapies.ConclusionOur study demonstrates that the exosome-related gene risk model is closely associated with immune infiltration. It can well forecast the prognosis of OV patients and guide the selection of immunotherapeutic strategies.

Dataset Information

Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients.

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Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients.

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