Dataset Information

ARMCX Family Gene Expression Analysis and Potential Prognostic Biomarkers for Prediction of Clinical Outcome in Patients with Gastric Carcinoma.

ABSTRACT: Armadillo gene subfamily members (ARMCX1-6) are well-known to regulate protein-protein interaction involved in nuclear transport, cellular connection, and transcription activation. Moreover, ARMCX signals on cell pathways also implicated in carcinogenesis and tumor progression. However, little is known about the associations of the ARMCX subfamily members with gastric carcinoma. This study investigated the prognostic value of ARMCX subfamily mRNA expression levels with the prognosis of gastric carcinoma (GC). We retrieved the data of a total of 351 GC patients from TCGA database. Survival and gene set enrichment analyses were employed to explore the predictive value and underlying mechanism of ARMCX genes in GC. The multivariate survival analysis revealed that individually low expressions of ARMCX1 (adjusted P = 0.006, HR = 0.620, CI = 0.440 - 0.874) and ARMCX2 (adjusted P = 0.005, HR = 0.610, 95%CI = 0.432-0.861) were related to preferable overall survival (OS). The joint-effects analysis shown that combinations of low level expression of ARMCX1 and ARMCX2 were correlated with favorable OS (adjusted P = 0.003, HR = 0.563, 95%CI = 0.384-0.825). ARMCX1 and ARMCX2 were implicated in WNT and NF-kappaB pathways, and biological processes including cell cycle, apoptosis, RNA modification, DNA replication, and damage response. Our results suggest that mRNA expression levels of ARMCX subfamily are potential prognostic markers of GC.

SUBMITTER: Wang T

PROVIDER: S-EPMC7345962 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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ARMCX Family Gene Expression Analysis and Potential Prognostic Biomarkers for Prediction of Clinical Outcome in Patients with Gastric Carcinoma.

Wang TingAn T Zhong HuaGe H Qin YuZhou Y Wei WeiYuan W Li Zhao Z Huang MingWei M Luo XiaoLing X

BioMed research international 20200630

Armadillo gene subfamily members (ARMCX1-6) are well-known to regulate protein-protein interaction involved in nuclear transport, cellular connection, and transcription activation. Moreover, ARMCX signals on cell pathways also implicated in carcinogenesis and tumor progression. However, little is known about the associations of the ARMCX subfamily members with gastric carcinoma. This study investigated the prognostic value of ARMCX subfamily mRNA expression levels with the prognosis of gastric c ...[more]

PMID: 32685472

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Project description:BackgroundGastric carcinoma is a very diverse disease. The progression of gastric carcinoma is influenced by complicated gene networks. This study aims to investigate the actual and potential prognostic biomarkers related to survival in gastric carcinoma patients to further our understanding of tumor biology.MethodsA weighted gene co-expression network analysis was performed with a transcriptome dataset to identify networks and hub genes relevant to gastric carcinoma prognosis. Data was obtained from 300 primary gastric carcinomas (GSE62254). A validation dataset (GSE34942 and GSE15459) and TCGA dataset confirmed the results. Gene ontology, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were performed to identify the clusters responsible for the biological processes and pathways of this disease.ResultsA brown transcriptional module enriched in the organizational process of the extracellular matrix was significantly correlated with overall survival (HR = 1.586, p = 0.005, 95% CI [1.149-2.189]) and disease-free survival (HR = 1.544, p = 0.008, 95% CI [1.119-2.131]). These observations were confirmed in the validation dataset (HR = 1.664, p = 0.006, 95% CI [1.155-2.398] in overall survival). Ten hub genes were identified and confirmed in the validation dataset from this brown module; five key biomarkers (COL8A1, FRMD6, TIMP2, CNRIP1 and GPR124 (ADGRA2)) were identified for further research in microsatellite instability (MSI) and epithelial-tomesenchymal transition (MSS/EMT) gastric carcinoma molecular subtypes. A high expression of these genes indicated a poor prognosis.ConclusionA transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma. This method may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.

Project description:BACKGROUND: Hepatocellular carcinoma (HCC) most commonly develops in patients who have a viral infection, especially in the case of hepatitis B virus (HBV), and in patients with a chronic liver disease. HCC patients with bone metastasis (BM) suffer from pain and other symptoms that significantly reduce their quality of life. Identification of patients who are at high risk for BM after undergoing potentially curative treatment for HCC remains challenging. Here, we aimed to identify HCC BM-related genes and proteins to establish prediction biomarkers. METHODS: RNA was extracted from 48 pairs of intratumoral and peritumoral formalin-fixed, paraffin-embedded tissue from HCC patients with and without BM. A cDNA-mediated annealing, selection, extension and ligation assay containing 502 cancer-related genes was used to identify novel BM-associated genes. An additional independent study with 350 HCC patients who had undergone hepatectomy was conducted to evaluate the expression of candidate genes at the protein level using immunohistochemistry on tissue microarrays (TMAs). Of the 350 patients, 273 (78.0%) were infected with HBV. RESULTS: Seven intratumoral genes and 17 peritumoral genes were overexpressed in patients with BM, whereas 15 intratumoral genes and 28 peritumoral genes were underexpressed in patients with BM. We selected the following four genes for further analysis because they were differentially expressed in the cancer gene-specific microarray and were previously reported to be associated with BM: connective tissue growth factor (CTGF), matrix metalloproteinase-1 (MMP-1), transforming growth factor ?1 (TGF-?1), and interleukin-11 (IL-11). We assessed the protein expression of these selected genes using immunohistochemistry on TMAs including 350 HCC patient specimens. We determined that expression of intratumoral CTGF, intratumoral IL-11, and peritumoral MMP-1 were independent prognostic factors for developing BM in HCC patients. Combining intratumoral CTGF and IL-11 expression was also an independent risk factor for BM development. CONCLUSIONS: Sixty-seven genes were differentially expressed in HCC patients with and without BM. High intratumoral CTGF, positive IL-11, and high peritumoral MMP-1 expression were associated with BM after hepatectomy. Intratumoral CTGF expression combined with IL-11 expression may serve as a useful predictive biomarker for HCC BM.

Project description:Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein-protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan-Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Dataset Information

ARMCX Family Gene Expression Analysis and Potential Prognostic Biomarkers for Prediction of Clinical Outcome in Patients with Gastric Carcinoma.

Publications

ARMCX Family Gene Expression Analysis and Potential Prognostic Biomarkers for Prediction of Clinical Outcome in Patients with Gastric Carcinoma.

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