CENPL, ISG20L2, LSM4, MRPL3 are four novel hub genes and may serve as diagnostic and prognostic markers in breast cancer.
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ABSTRACT:
SUBMITTER: Yin J
PROVIDER: S-EPMC8328991 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Piwi-interacting RNAs (piRNAs), whose role in germline maintenance has been established, are now also being classified as post-transcriptional regulators of gene expression in somatic cells. PIWI proteins, central to piRNA biogenesis, have been identified as genetic and epigenetic regulators of gene expression. piRNAs/PIWIs have emerged as potential biomarkers for cancer but their relevance to breast cancer has not been comprehensively studied. piRNAs and mRNAs were profiled from normal and breast tumor tissues using next generation sequencing and Agilent platforms, respectively. Gene targets for differentially expressed piRNAs were identified from mRNA expression dataset. piRNAs and PIWI genes were independently assessed for their prognostic significance (outcomes: Overall Survival, OS and Recurrence Free Survival, RFS). We discovered eight piRNAs as novel independent prognostic markers and their association with OS was confirmed in an external dataset (The Cancer Genome Atlas). Further, PIWIL3 and PIWIL4 genes showed prognostic relevance. 306 gene targets exhibited reciprocal relationship with piRNA expression. Cancer cell pathways such as apoptosis and cell signaling were the key Gene Ontology terms associated with the regulated gene targets. Overall, we have captured the entire cascade of events in a dysregulated piRNA pathway and have identified novel markers for breast cancer prognostication.
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Project description:BackgroundDendritic cells (DCs) are most potent antigen-processing cells and play key roles in host defense against Mycobacterium tuberculosis (MTB) infection. In this study, hub genes in DCs during MTB infection were first investigated using bioinformatics approaches and further validated in Monocyte-derived DCs.MethodsMicroarray datasets were obtained from Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) and immune infiltration analysis were performed to select suitable samples for further analysis. Differential analysis and functional enrichment analysis were conducted on DC samples, comparing live MTB-infected and non-infected (NI) groups. The CytoHubba plugin in Cytoscape was used to identify hub genes from the differentially expressed genes (DEGs). The expression of the hub genes was validated using two datasets and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in human monocyte-derived DCs. Enzyme-linked immunosorbent assay (ELISA) was used to validate interferon (IFN) secretion. Transcription factors (TFs) and microRNAs (miRNAs) that interact with the hub genes were predicted using prediction databases. The diagnostic value of the hub genes was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values.ResultsA total of 1835 common DEGs among three comparison groups (18 h, 48 h, 72 h after MTB infection) were identified. Six DEGs (IFIT1, IFIT2, IFIT3, ISG15, MX1, and RSAD2) were determined as hub genes. Functions enrichment analysis revealed that all hub genes all related to IFN response. RT-qPCR showed that the expression levels of six hub genes were significantly increased after DC stimulated by live MTB. According to the results of ELISA, the secretion of IFN-γ, but not IFN-α/β, was upregulated in MTB-stimulated DCs. AUC values of six hub genes ranged from 84 to 94% and AUC values of 5 joint indicators of two hub genes were higher than the two hub genes alone.ConclusionThe study identified 6 hub genes associated with IFN response pathway. These genes may serve as potential diagnostic biomarkers in tuberculosis (TB). The findings provide insights into the molecular mechanisms involved in the host immune response to MTB infection and highlight the diagnostic potential of these hub genes in TB.
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Project description:BackgroundIt must be admitted that the incidence of colorectal cancer (CRC) was on the rise all over the world, but the related treatment had not caught up. Further research on the underlying pathogenesis of CRC was conducive to improving the survival status of current CRC patients.MethodsDifferentially expressed genes (DEGs) screening were conducted based on "limma" and "RobustRankAggreg" package of R software. Weighted gene co-expression network analysis (WGCNA) was performed in the integrated DEGs that from The Cancer Genome Atlas (TCGA), and all samples of validation were from Gene Expression Omnlbus (GEO) dataset.ResultsThe terms obtained in the functional annotation for primary DEGs indicated that they were associated with CRC. The MEyellow stand out whereby showed the significant correlation with clinical feature (disease), and 4 hub genes, including ABCC13, AMPD1, SCNN1B and TMIGD1, were identified in yellow module. Nine datasets from Gene Expression Omnibus database confirmed these four genes were significantly down-regulated and the survival estimates for the low-expression group of these genes were lower than for the high-expression group in Kaplan-Meier survival analysis section. MEXPRESS suggested that down-regulation of some top hub genes may be caused by hypermethylation. Receiver operating characteristic curves indicated that these genes had certain diagnostic efficacy. Moreover, tumor-infiltrating immune cells and gene set enrichment analysis for hub genes suggested that there were some associations between these genes and the pathogenesis of CRC.ConclusionThis study identified modules that were significantly associated with CRC, four novel hub genes, and further analysis of these genes. This may provide a little new insights and directions into the potential pathogenesis of CRC.
| S-EPMC8801129 | biostudies-literature
Project description:BACKGROUND Late diagnosis and metastasis are leading causes of the high mortality of colorectal cancer (CRC). Long noncoding RNAs (lncRNAs) have been reported to play a critical role in the development and progression of CRC. This study aimed to explore the clinical significance of 2 novel lncRNAs - RP11-296E3.2 and LEF1-AS1 - including their expression pattern, as well as diagnostic and prognostic values, for metastatic CRC patients. MATERIAL AND METHODS lncRNAs expression was examined in tissues (91 cases) and plasma (60 cases) from CRC patients by real-time quantitative PCR (qRT-PCR), and the correlations between its expression and clinicopathological features and diagnosis values in metastasis were analyzed. TCGA datasets were further used to analyze their utility in prediction of overall survival (OS) and disease-free survival (DFS). ATP-based tumor chemosensitivity assay (ATP-TCA) was used to evaluated tumor chemoresistance. RESULTS Compared with adjacent normal tissues, RP11-296E3.2 was significantly downregulated while LEF1-AS1 was significantly upregulated in cancer tissues (p=0.0143, p=0.0322, respectively). High levels of RP11-296E3.2 and LEF1-AS1 in tissues and plasma were correlated with tumor metastasis (p=0.0488, p=0.0252 in tissues, p=0.0331, p=0.1862 in plasma, respectively). Further analysis showed that RP11-296E3.2 sensitivity and specificity in diagnosis of CRC metastasis is better than CEA in plasma (0.690 and 0.621, and 0.621 and 0.500, respectively), and the OS of metastatic CRC patients with higher LEF1-AS1 expression levels in tissues was short (log-rank p<0.05). CONCLUSIONS Our findings suggest that RP11-296E3.2 and LEF1-AS1 could separately serve as potential novel diagnosis and prognostic markers for CRC metastasis.
| S-EPMC6765338 | biostudies-literature
Project description:BackgroundBreast cancer (BC) is the most frequent malignancy in women worldwide and the leading cause of female cancer-associated death in the world. Grainyhead-like 2 (GRHL2) is an important gene involved in human cancer progression. However, the role of GRHL2 in BC is unknown.MethodsIn this study, we used in vitro experiments to verify the role of GRHL2 expression in BC progression. We used 14 databases to analyse the expression level of GRHL2 in BC and its prognostic and diagnostic value. In addition, the correlation between GRHL2 expression and immune cell infiltration and DNA methylation was also analysed.ResultsAt the cellular level, overexpression of GRHL2 induced E-cadherin expression in BC cells with a mesenchymal phenotype and resulted in a hybrid epithelial/mesenchymal (E/M) phenotype, which is more strongly correlated with tumour aggressiveness than a pure mesenchymal phenotype. Through analysis of various databases, we found that tumour tissue had a higher expression level of GRHL2. High expression of GRHL2 was associated with worse prognosis of BC patients and indicated that GRHL2 had significant diagnostic value. Grainyhead-like 2 is also related to immune infiltration and regulated by DNA methylation. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses showed that GRHL2-related signalling pathways in BC were related to tumour cell proliferation, invasion, and angiogenesis.ConclusionsIn summary, evidence indicates that GRHL2 can be used as a prognostic and diagnostic biomarker for BC.
| S-EPMC9310218 | biostudies-literature
Project description:BackgroundWhile most differential coexpression (DC) methods are bound to quantify a single correlation value for a gene pair across multiple samples, a newly devised approach under the name Correlation by Individual Level Product (CILP) revolutionarily projects the summary correlation value to individual product correlation values for separate samples. CILP greatly widened DC analysis opportunities by allowing integration of non-compromised statistical methods.MethodsHere, we performed a study to verify our hypothesis that conditional relationships, i.e., gene pairs of remarkable differential coexpression, may be sought as quantitative prognostic markers for human cancers. Alongside the seeking of prognostic gene links in a pan-cancer setting, we also examined whether a trend of global expression correlation loss appeared in a wide panel of cancer types and revisited the controversial subject of mutual relationship between the DE approach and the DC approach.ResultsBy integrating CILP with classical univariate survival analysis, we identified up to 244 conditional gene links as potential prognostic markers in five cancer types. In particular, five prognostic gene links for kidney renal papillary cell carcinoma tended to condense around cancer gene ESPL1, and the transcriptional synchrony between ESPL1 and PTTG1 tended to be elevated in patients of adverse prognosis. In addition, we extended the observation of global trend of correlation loss in more than ten cancer types and empirically proved DC analysis results were independent of gene differential expression in five cancer types.ConclusionsCombining the power of CILP and the classical survival analysis, we successfully fetched conditional transcriptional relationships that conferred prognosis power for five cancer types. Despite a general trend of global correlation loss in tumor transcriptomes, most of these prognosis conditional links demonstrated stronger expression correlation in tumors, and their stronger coexpression was associated with poor survival.
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Project description:Introduction: Abnormal status of gene expression plays an important role in tumorigenesis, progression and metastasis of breast cancer. Mechanisms of gene silence or activation were varied. Methylation of genes may contribute to alteration of gene expression. This study aimed to identify differentially expressed hub genes which may be regulated by DNA methylation and evaluate their prognostic value in breast cancer by bioinformatic analysis. Methods: GEO2R was used to obtain expression microarray data from GSE54002, GSE65194 and methylation microarray data from GSE20713, GSE32393. Differentially expressed-aberrantly methylated genes were identified by FunRich. Biological function and pathway enrichment analysis were conducted by DAVID. PPI network was constructed by STRING and hub genes was sorted by Cytoscape. Expression and DNA methylation of hub genes was validated by UALCAN and MethHC. Clinical outcome analysis of hub genes was performed by Kaplan Meier-plotter database for breast cancer. IHC was performed to analyze protein levels of EXO1 and Kaplan-Meier was used for survival analysis. Results: 677 upregulated-hypomethylated and 361 downregulated-hypermethylated genes were obtained from GSE54002, GSE65194, GSE20713 and GSE32393 by GEO2R and FunRich. The most significant biological process, cellular component, molecular function enriched and pathway for upregulated-hypomethylated genes were viral process, cytoplasm, protein binding and cell cycle respectively. For downregulated-hypermethylated genes, the result was peptidyl-tyrosine phosphorylation, plasma membrane, transmembrane receptor protein tyrosine kinase activity and Rap1 signaling pathway (All p< 0.05). 12 hub genes (TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2) were sorted from 677 upregulated-hypomethylated genes. 4 hub genes (EGFR, FGF2, BCL2, PIK3R1) were sorted from 361 downregulated-hypermethylated genes. Differential expression of 16 hub genes was validated in UALCAN database (p<0.05). 7 in 12 upregulated-hypomethylated and 2 in 4 downregulated-hypermethylated hub genes were confirmed to be significantly hypomethylated or hypermethylated in breast cancer using MethHC database (p<0.05). Finally, 12 upregulated hub genes (TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2) and 3 downregulated genes (FGF2, BCL2, PIK3R1) contributed to significant unfavorable clinical outcome in breast cancer (p<0.05). High expression level of EXO1 protein was significantly associated with poor OS in breast cancer patients (p=0.03). Conclusion: Overexpression of TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2 and downregulation of FGF2, BCL2, PIK3R1 might serve as diagnosis and poor prognosis biomarkers in breast cancer by more research validation. EXO1 was identified as an individual unfavorable prognostic factor. Methylation might be one of the major causes leading to abnormal expression of those genes. Functional analysis and pathway enrichment analysis of those genes would provide novel ideas for breast cancer research.
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Project description:Although patients with glioblastoma (GBM) have grave prognosis, significant variability in patient outcome is observed. This study aims to identify novel targets for GBM diagnosis and therapy. Microarray data (GSE4290, GSE7696, and GSE4412) obtained from the Gene Expression Omnibus was used to identify the differentially expressed genes (DEGs) by significant analysis of microarray (SAM). Intersection of the identified DEGs for each profile revealed 46 DEGs in GBM. A subset of common DEGs were validated by real-time reverse transcription quantitative PCR (qPCR). The prognostic value of some of the markers was also studied. We determined that RRM2 and COL3A1 were increased and directly correlated with glioma grade, while SH3GL2 and SNAP91 were decreased in GBM and inversely correlated with glioma grade. Kaplan-Meir analysis of GSE7696 revealed that COL3A1 and SNAP91 correlated with survival, suggesting that COL3A1 and SNAP91 may be suitable biomarkers for diagnostic or therapeutic strategies for GBM.
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Project description:Autophagy and immunity play critical roles in various cancers, but the prognostic impact of autophagy and immunity for uveal melanoma (UM) remains lacking. Therefore, the RNA sequencing of data in the TCGA-UVM dataset was downloaded from UCSC Xena database. The prognostic autophagy- and immunity-related genes (AIRGs) were selected via univariate Cox regression. Next, we applied LASSO method to construct four genes of signature in the TCGA-UVM and verified in another two GEO datasets (GSE84976 and GSE22138). This signature intimately associated with overall survival (OS) time and metastasis-free survival (MFS) time of UM, which could be considered as a prognostic indicator. Besides, by applying risk assessment, the patients of UM can be divided into two subgroups (high/low risk) with different survival time, distinct clinical outcomes, and immune microenvironments. Gene set enrichment analysis (GSEA) manifested that cancer hallmark epithelial-mesenchymal transition and KRAS pathways were positively activated in the high-risk group. Moreover, the high-risk group could be more sensitive to chemotherapies than the low-risk group. Thus, our finding suggested that the four genes of signature closely linked with UM risk and survival can afford more accurate survival prediction and potential therapeutic targets for clinical application.
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Project description:Triple Negative Breast Cancers (TNBC) is a heterogeneous disease at the molecular and clinical level with poor outcome. Molecular subclassification of TNBCs is essential for optimal use of current therapies and for development of new drugs. microRNAs (miRNA) are widely recognized as key players in cancer progression and drug resistance; investigation of their involvement in a TNBC cohort may reveal biomarkers for diagnosis and prognosis of TNBC. Here we stratified a large TNBC cohort into Core Basal (CB, EGFR and/or CK5, 6 positive) and five negative (5NP) if all markers are negative. We determined the complete miRNA expression profile and found a subset of miRNAs specifically deregulated in the two subclasses.We identified a 4-miRNA signature given by miR-155, miR-493, miR-30e and miR-27a expression levels, that allowed subdivision of TNBCs not only into CB and 5NP subgroups (sensitivity 0.75 and specificity 0.56; AUC=0.74) but also into high risk and low risk groups. We tested the diagnostic and prognostic performances of both the 5 IHC marker panel and the 4-miRNA expression signatures, which clearly identify worse outcome patients in the treated and untreated subcohorts. Both signatures have diagnostic and prognostic value, predicting outcomes of patient treatment with the two most commonly used chemotherapy regimens in TNBC: anthracycline or anthracycline plus taxanes. Further investigations of the patients’ overall survival treated with these regimens show that regardless of IHC group subdivision, taxanes addition did not benefit patients, possibly due to miRNA driven taxanes resistance. TNBC subclassification based on the 5 IHC markers and on the miR-155, miR-493, miR-30e, miR-27a expression levels are powerful diagnostic tools. Treatment choice and new drug development should consider this new subtyping and miRNA expression signature in planning low toxicity, maximum efficacy therapies.
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