Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: Meng J
PROVIDER: S-EPMC8596225 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeMost lung adenocarcinoma (LUAD) patients are diagnosed at the advanced stage and have poor prognosis. DNA methylation plays an important role in the prognosis prediction of cancers. The objective of this study was to identify new DNA methylation sites as biomarkers for LUAD prognosis.Materials and methodsWe downloaded DNA methylation data from The Cancer Genome Atlas data portal. Cox proportional hazard regression model and random survival forest algorithm were applied to identify the DNA-methylation sites. Methylation of sites were validated in the Gene Expression Omnibus cohorts. Function annotation were done to explore the biological function of DNA methylated sites signature.ResultsSix DNA methylation sites were identified as prognosis signature. The signature yielded acceptable discrimination between the high-risk group and low-risk group. The discrimination effect of this DNA methylation signature for the OS was obvious, with a median OS of 21.89 months vs. 17.74 months for high-risk vs. low-risk groups. This prognostic prediction model was validated by the test group and GEO dataset. The predictive survival value was higher for the prognostic prediction model than that for the tumor node metastasis stage. Adjuvant hemotherapy could not affect the prediction of the signature. Functional analysis indicated that these signature genes were involved in protein binding and cytoplasm.ConclusionWe identified the prognostic signature for LUAD by combining six DNA methylation sites. This could service as potential robust and specificity signature in the prognosis prediction of LUAD.
| S-EPMC7700873 | biostudies-literature
Project description:Molecular analysis facilitates the prediction of overall survival (OS) of breast cancer and decision-making of the treatment plan. The current study was designed to identify new prognostic genes for breast cancer and construct an effective prognostic signature with integrated bioinformatics analysis. Differentially expressed genes in breast cancer samples from The Cancer Genome Atlas (TCGA) dataset were filtered by univariate Cox regression analysis. The prognostic model was optimized by the Akaike information criterion and further validated using the TCGA dataset (n = 1014) and Gene Expression Omnibus (GEO) dataset (n = 307). The correlation between the risk score and clinical information was assessed by univariate and multivariate Cox regression analyses. Functional pathways in relation to high-risk and low-risk groups were analyzed using gene set enrichment analysis (GSEA). Four prognostic genes (EXOC6, GPC6, PCK2, and NFATC2) were screened and used to construct a prognostic model, which showed robust performance in classifying the high-risk and low-risk groups. The risk score was significantly related to clinical features and OS. We identified 19 functional pathways significantly associated with the risk score. This study constructed a new prognostic model with a high prediction performance for breast cancer. The four-gene prognostic signature could serve as an effective tool to predict prognosis and assist the management of breast cancer patients.
| S-EPMC8898848 | biostudies-literature
Project description:BackgroundLung cancer is one of the most common malignant tumors. Despite advances in lung cancer therapies, prognosis of non-small-cell lung cancer is still unfavorable. The aim of this study was to identify the prognostic value of key genes in lung tumorigenesis.MethodsDifferentially expressed genes (DEGs) were screened out by GEO2R from three Gene Expression Omnibus cohorts. Common DEGs were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein- protein interaction networks were constructed by the STRING database and visualized by Cytoscape software. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database, and their genomic alterations were identified by performing the cBioportal. Finally, overall survival analysis of hub genes was performed using Kaplan-Meier Plotter.ResultsFrom three datasets, 169 DEGs (70 upregulated and 99 downregulated) were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that upregulated DEGs were significantly enriched in cell cycle, p53 pathway, and extracellular matrix-receptor interactions; the downregulated DEGs were significantly enriched in PPAR pathway and tyrosine metabolism. The protein-protein interaction network consisted of 71 nodes and 305 edges, including 49 upregulated and 22 downregulated genes. The hub genes, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, were overexpressed compared with the normal group by Gene Expression Profiling Interactive Analysis analysis, and associated with reduced overall survival in lung cancer patients. In the genomic alterations analysis, two hotspot mutations (S2021C/F and E314K/V) were identified in Pfam protein domains.ConclusionDEGs, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, might be potential biomarkers for the prognosis and treatment of lung adenocarcinoma.
| S-EPMC6252781 | biostudies-literature
Project description:BackgroundOne of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively.Methods and findingsOur results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER- patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome.ConclusionsThe 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer.
| S-EPMC2500166 | biostudies-literature
Project description:The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival?related genes were selected from the DEGs using the Cox regression method. A co?expression network of survival?related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan?Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival?related genes selected. The co?expression network of survival?related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator?activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine?cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65?gene signature was established using this co?expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e?12) and the GSE17538 dataset (P=1.67e?6). The 65?gene signature included kallikrein?related peptidase 6 (KLK6), collagen type XI ?1 (COL11A1), cartilage oligomeric matrix protein, wingless?type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65?gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.
| S-EPMC5810222 | biostudies-literature
Project description:BACKGROUND Colon adenocarcinoma (COAD) is one of the most common and fatal malignant tumors, which increases the difficulty of prognostic predictions. Thus, new biomarkers for the diagnosis and prognosis of COAD should be explored. Ferroptosis is a recently identified programmed cell death process that has the characteristics of iron-dependent lipid peroxide accumulation. However, the predictive value of ferroptosis-related genes (FRGs) for COAD still needs to be further clarified. AIM To identify some critical FRGs and construct a COAD patient prognostic signature for clinical utilization. METHODS The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus databases were the data sources for mRNA expression and corresponding COAD patient clinical information. Differentially expressed FRGs were recognized using R and Perl software. We constructed a multi-FRG signature of the TCGA-COAD cohort by performing a univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis. COAD patients from the Gene Expression Omnibus cohort were utilized for verification. RESULTS Our research showed that most of the FRGs (85%) were differentially expressed between the corresponding adjacent normal tissues and cancer tissues in the TCGA-COAD cohort. Seven FRGs were related to overall survival (OS) in the univariate Cox analysis (all P < 0.05). A model with five FRGs (AKR1C1, AKR1C3, ALOX12, CRYAB, and FDFT1) was constructed to divide patients into high- and low-risk groups. The OS of patients in the high-risk group was significantly lower than that of the low-risk group (all P < 0.01 in the TCGA and Gene Expression Omnibus cohorts). The risk score was an independent prognosticator of OS in the multivariate Cox analysis (hazard ratio > 1, P < 0.01). The predictive capacity of the model was verified by a receiver operating characteristic curve analysis. In addition, a nomogram based on the expression of five hub FRGs and risk score can precisely predict the OS of individual COAD cancer patients. Immune correlation analysis and functional enrichment analysis results revealed that immunology-related pathways were abundant, and the immune states of the high-risk group and the low-risk group were different. CONCLUSION In conclusion, a novel five FRG model can be utilized for predicting prognosis in COAD. Targeting ferroptosis may be a treatment option for COAD.
| S-EPMC8546824 | biostudies-literature
Project description:BackgroundLung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. The main obstacle to early diagnosis or monitoring of patients at high risk of poor survival has been the lack of essential predictive biomarkers.MethodsRNA-sequencing was performed on LUAD affected tissue and paired adjacent to noncancerous tissue samples and Gene Expression Omnibus dataset GSE19188 and GSE33532 were used to obtain an intersection of differential expressed genes and construct a protein-protein interaction network to get hub genes. Then corresponding overall survival information of two cohorts of LUAD patients from our hospital and The Cancer Genome Atlas project-LUAD were included in the present study. An analysis of the Kyoto Encyclopedia of Genes and Genomes database and Gene Ontology were carried out to study the signature mechanism.ResultsIn our study, we identified eight candidate genes (DLGAP5, KIF11, RAD51AP1, CCNB1, AURKA, CDC6, OIP5 and NCAPG) closely related to survival in LUAD. A linear prognostic model of the eight genes was constructed and weighted by the regression coefficient (β) from the multivariate Cox regression analysis of The Cancer Genome Atlas-LUAD cohort to divide patients into low- and high-risk groups. The prognostic ability of the signature was validated in LUAD patients at our hospital. Patients assigned to the high-risk group exhibited poor overall survival compared to patients in the low-risk group. Finally, functional enrichment analysis showed that cell division played a vital role in the development of LUAD.ConclusionThe study identified an mRNA signature including eight genes, which may serve as a potential prognostic marker of LUAD.
| S-EPMC6138967 | biostudies-literature
Project description:The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.
| S-EPMC4527782 | biostudies-literature
Project description:Lung adenocarcinoma is frequently occurring type of lung cancer with high metastatic risk. We performed a DNA methylation profiling analysis to identify possible prognostic markers involved in lung adenocarcinoma. DNA methylation profiling data (GSE66386) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially methylated genes were identified using a limma package. GO enrichment analysis was performed to identify vital functions related to differential gene methylation, and pathway analysis was performed to assess the associations between different proteins with regard to regulation of cell function and metabolism. The screening results showed a total of 112,662 differentially methylated genes in lung adenocarcinoma patients compared with those of the normal controls. These CpGs were involved in 16,705 genes. The skeletal system development (P=9.46E-27) and embryonic organ morphogenesis (P=8.67E-24) were found to be involved in lung adenocarcinoma. The cancer (P=3.64E-07), Rap1 signaling (P=9.21E-05) and calcium signaling (P=9.21E-05) pathways constituted the important pathways associated with lung adenocarcinoma. In conclusion, methylated PTPRF, HOXD3, HOXD13 and CACNA1A are potential markers and may be utilized for the diagnosis and therapy of lung adenocarcinoma.
| S-EPMC5796271 | biostudies-literature
Project description:Programmed cell death (PCD) refers to a molecularly regulated form of cell death that functions as an essential anticancer defense mechanism and serves as a target of anticancer therapies. Multiple types of PCD comprehensively regulate tumorigenesis and tumor progression and metastasis. However, a systemic exploration of the multiple types of PCD in cancers, especially bladder cancer, is lacking. In this study, we evaluated the expression pattern of genes associated with multiple types of PCD in bladder cancer using the "ssGSEA" method and conceptualized the multiple types of PCD as being collectively involved in "Pan-PCD". Based on the differentially expressed genes related to Pan-PCD, we developed a Pan-PCD-related prognostic signature (PPRPS) to predict patient prognosis via univariate and multivariate Cox regression analysis. The PPRPS is an independent prognostic factor, and the AUC (Area Under Curve) for 3-year overall survival was 0.748. Combined with age and stage, PPRPS displayed excellent predictive ability. Based on the PPRPS, higher levels of immune cell infiltration, tumor microenvironment, and immune checkpoint molecules were observed in the high-PPRPS group. Furthermore, PPRPS enabled accurate risk prediction for metastatic urothelial carcinoma after anti-PD-L1 monoclonal antibody treatment. Patients in the high-PPRPS group had poor prognoses. Docetaxel, staurosporine, and luminespib were identified as potentially effective drugs for high-PPRPS bladder cancer patients. In summary, we developed the Pan-PCD signature to improve the accuracy of bladder cancer prognostic predictions and to provide a novel classification method to guide treatment selection.
| S-EPMC9728529 | biostudies-literature