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Expression and potential prognostic value of histone family gene signature in breast cancer.

ABSTRACT: Breast cancer (BC) is the most common type of malignancy among females worldwide. Histone modifications, which are the major post-translational modifications, have a significant role in cancer development and prognosis. However, whether histone family genes may serve as potential prognostic biomarkers for BC patients has remained elusive. In the present study, RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes were identified and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway functional enrichment analysis was performed. As histone family genes have been reported to be associated with cervical cancer, the present study hypothesized that histone family genes are associated with gynecological tumors. Histone family genes, including histone cluster 1 H1A family member B (HIST1H1B), HIST1H2AJ, HIST1H2AM, HIST1H2BI, HIST1H2BO, HIST1H3B, HIST1H3F, HIST1H3H, HIST1H4C and HIST1H4D, were upregulated and identified as hub genes in the protein-protein interaction network. In addition, Oncomine and the Human Protein Atlas were used to further verify the expression levels of histone gene sets. The PrognoScan database was then used to investigate the association between expression and prognostic value regarding cancer patient survival. The present results indicated that higher expression of histone gene sets was associated with poor overall survival, relapse-free survival and distant metastasis-free survival of BC patients. The differential expression of histone family genes between BC and normal samples was validated by reverse transcription-quantitative PCR. Finally, to determine the clinical role of histone family genes in BC, the correlations between histone family genes expression and clinical characteristics were investigated through data collected from TCGA. Therefore, the present study indicates that histone gene sets may be used as prognostic factors for survival prediction for BC patients.

SUBMITTER: Xie W

PROVIDER: S-EPMC6861870 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Expression and potential prognostic value of histone family gene signature in breast cancer.

Xie Wenting W Zhang Jiajia J Zhong Peng P Qin Shanshan S Zhang Han H Fan Xin X Yin Yuzhen Y Liang Ruipeng R Han Yali Y Liao Yina Y Yu Xiaqing X Long Huideng H Lv Zhongwei Z Ma Chao C Yu Fei F

Experimental and therapeutic medicine 20191025 6

Breast cancer (BC) is the most common type of malignancy among females worldwide. Histone modifications, which are the major post-translational modifications, have a significant role in cancer development and prognosis. However, whether histone family genes may serve as potential prognostic biomarkers for BC patients has remained elusive. In the present study, RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes were identified and Gene Ontology a ...[more]

PMID: 31772649

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Project description:BackgroundBreast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.MethodsGene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.ResultsWe constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).ConclusionOur study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Project description:BackgroundAutophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.MethodsFirst, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.ResultsA total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.ConclusionsBased on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

Project description:IntroductionCervical cancer has high mortality, high recurrence and poor prognosis. Although prognostic biomarkers such as clinicopathological features have been proposed, their accuracy and precision are far from satisfactory. Therefore, novel biomarkers are urgently needed for disease surveillance, prognosis prediction and treatment selection.MaterialsDifferentially expressed genes (DEGs) between cervical cancer and normal tissues from three microarray datasets extracted from the Gene Expression Omnibus platform were identified and screened. Based on these DEGs, a six-gene prognostic signature was constructed using cervical squamous cell carcinoma and endocervical adenocarcinoma data from The Cancer Genome Atlas. Next, the molecular functions and related pathways of the six genes were investigated through gene set enrichment analysis and co-expression analysis. Additionally, immunophenoscore analysis and the QuartataWeb Server were employed to explore the therapeutic value of the six-gene signature.ResultsWe discovered 178 overlapping DEGs in three microarray datasets and established a six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) prognostic signature with stable and excellent performance in predicting overall survival in different subgroups. Intriguingly, the six-gene signature was closely associated with the immune response and tumour immune microenvironment. The six-gene signature might be used for predicting response to immune checkpoint inhibitors (ICIs) and the six genes may serve as new drug targets for cervical cancer.ConclusionOur study established a novel six-gene (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) signature that was closely associated with the immune response and tumour immune microenvironment. The six-gene signature was indicative of aggressive features of cervical cancer and therefore might serve as a promising biomarker for predicting not only overall survival but also ICI treatment effectiveness. Moreover, three genes (UPP1, ISG20 and GLTP) within the six-gene signature have the potential to become novel drug targets.

Project description:Background Currently, no gene-expression signature (GES) established from node-positive cohorts, able to predict breast cancer evolution after systemic adjuvant chemotherapy, exists. Methods Gene-expression profiles of 252 node-positive patients (median follow-up: 7.7 years), mostly included in a randomized clinical trial (PACS01), receiving systemic adjuvant regimen, were determined by means of cDNA custom array representing 5,776 distinct genes. Findings In the training cohort, we established a 38-GES for the purpose of predicting time to distant metastasis. The 38-GES yielded unadjusted hazard ratio of 4.86 (95% CI=2.76-8.56). Even when adjusted with the two best clinicopathological prognostic scoring: NPI and Adjuvant!, 38-GES HR were 3.30 (1.81-5.99) and 3.40 (1.85-6.24), respectively. Furthermore, 38-GES improved mostly NPI and Adjuvant! intermediate-risk classified patients. NPI intermediate-risk patients (7-year MFS=87.2%) were divided into 2/3 (7-year MFS=95.5%) close to NPI low-risk group and 1/3 (7-year MFS=69.3%) close to NPI high-risk group (HR=6.97 [2.51-19.36]). Adjuvant! intermediate-risk patients (7-year MFS=88.0%) were divided into 2/3 (7-year MFS=94.8%) close to Adjuvant! low-risk group and 1/3 (7-year MFS=71.7%) close to Adjuvant! high-risk group (HR=5.31 [5.38-11.87]). The 38-GES was validated on gene-expression datasets from three external node-positive breast cancer subcohorts (n=224) generated from different microarray platforms. The 38-GES yielded unadjusted HR=2.95 (1.74-5.01). Furthermore, 38-GES showed performance in supplementary cohorts with different lymph-node status and endpoint (1,031 new patients). Interpretation The 38-GES represents a robust tool able to type systemic adjuvant treated node-positive patients at high risk of metastatic relapse, and especially powerful to separate NPI or Adjuvant! intermediate-risk node-positive patients. Keywords: disease-state analysis 252 breast cancer patients at diagnosis examined with spotted cDNA nylon membrane. Patient details: PACS01x are 2 parts of a clinical trial : PACS01A : patients had received 6 cycles of FEC100 PACS01B : patients had received 3 cycles of FEC100 then 3 cycles of Docetaxel CCRG are patients from our local cancer center (Cancer Center René Gauducheau) included with the same criteria as PACS01A (6 cycles of FEC100).

Dataset Information

Expression and potential prognostic value of histone family gene signature in breast cancer.

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Expression and potential prognostic value of histone family gene signature in breast cancer.

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