Exon Level Expression Profiling analysis of early stage NSCLC suffering rapidly relapse
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE121841 | GEO | 2018/10/27
REPOSITORIES: GEO
Similar Datasets
Project description:Background: Circular RNAs (circRNAs) have attracted increasing attention in recent years for their potential application as disease biomarkers due to their high abundance and stability. In this study, we attempted to screen circRNAs that can be used to predict postoperative recurrence and survival in patients with gastric cancer (GC). Methods: High-throughput RNA sequencing was used to identify differentially expressed circRNAs in GC patients with different prognoses. The expression level of circRNAs in the training set (n=136) and validation set (n=167) was detected by quantitative real-time PCR (qRT-PCR). Kaplan–Meier estimator, receiver operating characteristic (ROC) curve and cox regression analysis were used to evaluate the prognostic value of circRNAs on recurrence-free survival (RFS) and overall survival (OS) in GC patients. CeRNA network prediction, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the circRNAs with prognostic significance. Results: A total of 259 differentially expressed circRNAs were identified in GC patients with different RFS. We found two circRNAs (hsa_circ_0005092 and hsa_circ_0002647) that highly expressed in GC patients with good prognoses, and subsequently established a predictive model for postoperative recurrence and prognosis evaluation, named circPanel. Patients with circPanellow might have shorter recurrence-free survival (RFS) and overall survival (OS). We also performed circRNA-miRNA-mRNA network prediction and functional analysis for hsa_circ_0005092 and hsa_circ_0002647. Conclusions: CircPanel has the potential to be a prognostic biomarker in GC patients with greater accuracy than a single circRNA and certain traditional tumor markers (e.g., CEA, CA19-9 and CA724).
2021-08-12 | GSE181769 | GEO
Project description:There are no widely-accepted prognostic markers currently available to predict outcomes in patients with triple-negative breast cancer (TNBC), and no targeted therapies with confirmed benefit. We have used MALDI mass spectrometry imaging (MSI) of tryptic peptides to compare regions of cancer and benign tissue in 10 formalin-fixed, paraffin-embedded sections of TNBC tumors. Proteins were identified by reference to a peptide library constructed by LC-MALDI-MS/MS analyses of the same tissues. The prognostic significance of proteins that distinguished between cancer and benign regions was estimated by Kaplan-Meier analysis of their gene expression from public databases. Among peptides that distinguished between cancer and benign tissue in at least 3 tissues with a ROC area under the curve >0.7, 14 represented proteins identified from the reference library, including proteins not previously associated with breast cancer. Initial network analysis using the STRING database showed no obvious functional relationships except among collagen subunits COL1A1, COL1A2, and COL63A, but manual curation, including the addition of EGFR to the analysis, revealed a unique network connecting 10 of the 14 proteins. Kaplan-Meier survival analysis to examine the relationship between tumor expression of genes encoding the 14 proteins, and recurrence-free survival (RFS) in patients with basal-like TNBC showed that, compared to low expression, high expression of nine of the genes was associated with significantly worse RFS, most with hazard ratios >2. In contrast, in estrogen receptor-positive tumors, high expression of these genes showed only low, or no, association with worse RFS. These proteins are proposed as putative markers of RFS in TNBC, and some may also be considered as possible targets for future therapies.
2019-05-06 | PXD013397 | Pride
Project description:Unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of Amyotrophic Lateral Sclerosis (ALS). However, high abundance proteins mask the presence of lower abundance proteins that may hold valuable diagnostic and prognostic use. Developments in mass spectrometry proteomic data acquisition methods therefore hold promise for improved protein depth and more sensitive biomarker discovery. In this study, mass spectrometry with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of 40 people with ALS, 15 healthy controls and 8 people with mimicking conditions. Qthen quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins significantly upregulated in ALS vs healthy controls, and 9 with altered abundance in ALS vs mimicking conditions (FDR <0.1). Elevated Chitotriosidase 1 (CHIT1) was common to both comparisons and showed strong correlation with disease progression rate (Pearson r =0.41, FDR-adjusted p = 0.035), but no association with survival (Pearson correlation and Kaplan-Meier survival analysis). Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS vs healthy controls) showed a stronger correlation with disease progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and significant association with survival in Kaplan Meier analysis (log rank p = 0.013) but no independent effect in proportional hazards models. Weighted correlation network analysis was then used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was strongly associated with age at disease onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, negatively correlated with disease progression rate (r = -0.42, FDR-adjusted p =0.109). This study confirms that UCHL1 is strongly associated with disease progression and survival, and highlights potential of inflammatory and endoplasmic reticulum proteins to provide prognostic biomarker potential.
2024-05-21 | PXD044274 | Pride
Project description:Most treatment failures of glioblastoma (GBM) occur within high-dose radiation fields, in part due to increased capacity for DNA damage repair. We retrospectively quantified the expression levels of key repair genes GBM tissue samples and correlated those expression levels with overall survival (OS). Differential expression analysis identified 7 repair genes that were significantly upregulated in GBM tissues relative to the controls (> 4-fold difference and all adjusted p values < 0.001). Of these 7 genes, Cox proportional hazards models identified RAD51 as being associated with an increased risk of death (HR = 3.49; p = 0.03). Kaplan-Meier (KM) analysis also demonstrated that patients with high levels of RAD51 had significantly shorter OS compared to patients with low levels (median OS of 10.6 months vs 20.1 months, log-rank p = 0.03). These findings were then validated in a larger external dataset of 162 patients using publicly available gene expression data quantified by the same NanoString technology (median OS of 13.8 months vs. 17.4 months; log-rank p = 0.006)
2022-02-08 | GSE186057 | GEO
Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy.
2010-04-14 | GSE17856 | GEO
Project description:Long noncoding RNAs (lncRNAs) are a class of non-coding RNAs longer than 200 nt that function in endogenous gene regulation and tumorigenesis. Hepatocellular carcinoma (HCC) is a heterogeneous disease with different treatment outcome. It is a challenge to develop a prognostic marker to identify HCC patients who are at greatest risk for recurrence or death. In this study, we try to screen lncRNAs whose expression levels are associated with recurrence or death of HCC patients through an extensive lncRNA profiling study on a cohort of 59 HCC patients. For these experiments, we used RNA extracted from 59 HCC tissues and 20 normal livers. Total RNAs from the 20 normal livers were pooled and used as a reference for all microarray experiments. For each microarray experiment, Cy5-labeled probes derived from the DNase-treated total RNA from each HCC sample was hybridized against Cy3-labeled probes derived from common reference on Arraystar Human LncRNA Microarray (Arraystar, Rockville, USA). LncRNAs whose expression was significantly associated with disease-specific survival and time to recurrence were selected based on microarray data. The univariate Cox proportional hazards model was used to assess the association of lncRNAs with survival. We computed a statistical significance level (P value) for two endpointsM-bM-^@M-^Tthe time to cancer-related death and time to recurrence, based on univariate Cox proportional hazards models in BRB-ArrayTools version 4.2.0.
2012-08-16 | E-GEOD-40144 | biostudies-arrayexpress
Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy. 43 tumor (JT) and 44 non-tumor (JNT) liver tissues surgically resected from patients with HCV-associated hepatocellular carcinoma; 8 non-tumor liver tissues (control samples, JC) surgically resected from HCV- or HBV-free patients with metastatic liver tumor. Inter-batch normalization was carried out using Distance Weighted Discrimination procedure. The supplementary file 'GSE17856_Readme.txt' contains a description of the replicates used for normalization. The 'GSE17856_US14702406_2514850*' files are the raw data files for the replicates.
2010-04-14 | E-GEOD-17856 | biostudies-arrayexpress
Project description:Background: Long noncoding RNAs (lncRNAs) are pervasively transcribed and play a key role in tumorigenesis. The aim of the present study was to determine the lncRNA expression profile in astrocytoma patients and to assess the potential clinical value of the findings. Methods: The global lncRNA expression profile in astrocytoma was measured by lncRNA microarray. Next, the differentially expressed lncRNAs were validated using a Taqman probe-based quantitative real time-PCR (qRT-PCR) assay in 130 astrocytoma and 60 normal adjacent tissue (NAT) samples, which were randomly divided into a training set and a validation set. Finally, the correlation of the lncRNA levels with survival of the astrocytoma patients was estimated by performing a Kaplan-Meier survival analysis and a univariate/multivariate Cox proportional hazard regression analysis. Results: After a two-phase selection and validation process, 7 lncRNAs were found to have a significantly different expression profile in astrocytoma samples compared to the NAT samples. Unsupervised clustering analysis further revealed the potential of the 7-lncRNA profile to differentiate between tumors and NAT samples. Using Kaplan-Meier survival analysis, we found that the low expression of XLOC_005285 and XLOC_010967 and the high expression of HIT000260843.11 were significantly associated with poor patient survival. Moreover, Cox proportional hazard regression analysis revealed that this prognostic impact was independent of other clinicopathological factors. The lncRNA expression profiles of 3 astrocytoma tissues and 3 normal adjacent tissues were studied by microarray.
2014-06-07 | E-GEOD-58276 | biostudies-arrayexpress
Project description:Background: Long noncoding RNAs (lncRNAs) are pervasively transcribed and play a key role in tumorigenesis. The aim of the present study was to determine the lncRNA expression profile in astrocytoma patients and to assess the potential clinical value of the findings. Methods: The global lncRNA expression profile in astrocytoma was measured by lncRNA microarray. Next, the differentially expressed lncRNAs were validated using a Taqman probe-based quantitative real time-PCR (qRT-PCR) assay in 130 astrocytoma and 60 normal adjacent tissue (NAT) samples, which were randomly divided into a training set and a validation set. Finally, the correlation of the lncRNA levels with survival of the astrocytoma patients was estimated by performing a Kaplan-Meier survival analysis and a univariate/multivariate Cox proportional hazard regression analysis. Results: After a two-phase selection and validation process, 7 lncRNAs were found to have a significantly different expression profile in astrocytoma samples compared to the NAT samples. Unsupervised clustering analysis further revealed the potential of the 7-lncRNA profile to differentiate between tumors and NAT samples. Using Kaplan-Meier survival analysis, we found that the low expression of XLOC_005285 and XLOC_010967 and the high expression of HIT000260843.11 were significantly associated with poor patient survival. Moreover, Cox proportional hazard regression analysis revealed that this prognostic impact was independent of other clinicopathological factors.
2014-06-07 | GSE58276 | GEO
Project description:Prognostication of Breast Cancer (BC) relies largely on traditional markers such as hormone or growth factors but due to their suboptimal specificities, it is challenging to identify the subset of patients who are likely to undergo recurrence and markers of higher specificity are sought to guide therapies. MicroRNAs (miRNAs) are small non-coding RNAs which function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC. In our study, we sequenced 104 breast tumor tissue samples and 11 apparently healthy normal breast tissues for miRNAs. Two widely used approaches were adopted to identify prognostic markers â Case-control and Case-only. For both the approaches, Cox-proportional hazards regression model and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene ontology terms were identified for miRNAs significant in survival analysis. A total of 1,423 miRNAs were captured from the tissues, of which 126 were retained with predetermined criteria for good read counts. In the case-control approach, 80 miRNAs were differentially expressed, from which four and two miRNAs were significant for OS and RFS respectively. In the case-only approach, from 147 miRNAs, 11 and four miRNAs were significant for OS and RFS respectively, in which miR-574-3p and miR-660-5p were novel and were not previously found to be associated with BC prognosis. Multivariate Cox analysis indicated that the risk scores calculated in both the approaches were potential independent prognostic factors for BC. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration. Identification of miRNAs as prognostic markers for breast cancer
2016-03-02 | E-GEOD-68085 | biostudies-arrayexpress