High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis.
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ABSTRACT:
SUBMITTER: Qi SA
PROVIDER: S-EPMC8175557 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.
| S-EPMC6431770 | biostudies-literature
Project description:BackgroundLung cancer is the tumor with the highest morbidity and mortality, and has become a global public health problem. The incidence of lung cancer in men has declined in some countries and regions, while the incidence of lung cancer in women has been slowly increasing. Therefore, the aim is to explore whether estrogen-related genes are associated with the incidence and prognosis of lung cancer.MethodsWe obtained all estrogen receptor genes and estrogen signaling pathway genes in The Cancer Genome Atlas (TCGA), and then compared the expression of each gene in tumor tissues and adjacent normal tissues for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) separately. Survival analysis was performed of the differentially expressed genes in LUAD and LUSC patients separately. The diagnostic and prognostic values of the candidate genes were validated in the Gene Expression Omnibus (GEO) datasets.ResultsWe found 5 estrogen receptor genes and 66 estrogen pathway genes in TCGA. A total of 50 genes were differently expressed between tumor tissues and adjacent normal tissues and 6 of the 50 genes were related to the prognosis of LUAD in TCGA. 56 genes were differently expressed between tumor tissues and adjacent normal tissues and none of the 56 genes was related to the prognosis of LUSC in TCGA. GEO datasets validated that the 6 genes (SHC1, FKBP4, NRAS, PRKCD, KRAS, ADCY9) had different expression between tumor tissues and adjacent normal tissues in LUAD, and 3 genes (FKBP4, KRAS, ADCY9) were related to the prognosis of LUAD.ConclusionsThe expressions of FKBP4 and ADCY9 are related to the pathogenesis and prognosis of LUAD. FKBP4 and ADCY9 may serve as biomarkers in LUAD screening and prognosis prediction in clinical settings.
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Project description:Previous studies have reported that the aberrant expression of circulating microRNAs (miRNAs/miRs) can be used as diagnostic and prognostic markers in non-small cell lung cancer (NSCLC). The present study aimed to assess the diagnostic and prognostic predictive values of four plasma miRNAs for NSCLC. A total of 12 candidate miRNAs were selected that have previously been reported to be aberrantly expressed in NSCLC, and their plasma levels in the training set were detected via reverse transcription-quantitative PCR analysis. The screened out miRNAs were further validated in the testing set. The area under the curve (AUC) of the receiver operating characteristic curve was constructed to evaluate diagnostic performance. Kaplan-Meier survival analysis was performed to assess the association between the plasma miRNA levels and disease-free survival (DFS) time. The results demonstrated that 4/12 plasma miRNAs (miR-210, miR-1290, miR-150 and miR-21-5p) were highly expressed in patients with NSCLC compared with their expression levels in patients with benign lung disease (BLD) and healthy controls in the training and testing sets, respectively. The AUC values of the four-miRNA panel were 0.96 and 0.93 in the training and testing sets, respectively, for distinguishing patients with NSCLC from healthy controls, which were similar to the AUC values for distinguishing patients with NSCLC from patients with BLD (0.96 and 0.94). The AUC values of the four-miRNA panel in patients with stage I NSCLC were comparable to that of patients with stage II-III NSCLC (0.942 and 0.965). Patients with high plasma levels of miR-210 and miR-150 had worse DFS than those with low plasma levels of these miRNAs. In addition, patients whose plasma levels of the four miRNAs decreased by >50% after surgery exhibited a good DFS. Taken together, the results of the present study suggest that these four miRNAs (miR-210, miR-1290, miR-150 and miR-21-5p) act as useful biomarkers for early diagnosis and prognosis of NSCLC.
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Project description:MicroRNAs (miRNAs) are one abundant class of small, endogenous non-coding RNAs, which regulate various biological processes by inhibiting expression of target genes. miRNAs have important functional roles in carcinogenesis and development of colorectal cancer (CRC), and emerging evidence has indicated the feasibility of miRNAs as robust cancer biomarkers. This review summarizes the progress in miRNA-related research, including study of its oncogene or tumour-suppressor roles and the advantages of miRNA biomarkers for CRC diagnosis, treatment and recurrence prediction. Along with analytical technique improvements in miRNA research, use of the emerging extracellular miRNAs is feasible for CRC diagnosis and prognosis.
| S-EPMC6367136 | biostudies-literature
Project description:ObjectivesThe COVID-19 pandemic has been a serious worldwide public health crisis since 2020 and is still challenging healthcare systems. New tools for the prognosis and diagnosis of COVID-19 patients remain important issues.DesignHere, we studied the metabolome of plasma samples of COVID-19 patients for the identification of prognosis biomarkers.PatientsPlasma samples of eighty-six SARS-CoV-2-infected subjects and 24 healthy controls were collected during the first peak of the COVID-19 pandemic in France in 2020.Main resultsPlasma metabolome fingerprinting allowed the successful discrimination of healthy controls, mild SARS-CoV-2 subjects, and moderate and severe COVID-19 patients at hospital admission. We found a strong effect of SARS-CoV-2 infection on the plasma metabolome in mild cases. Our results revealed that plasma lipids and alterations in their saturation level are important biomarkers for the detection of the infection. We also identified deoxy-fructosyl-amino acids as new putative plasma biomarkers for SARS-CoV-2 infection and COVID-19 severity. Finally, our results highlight a key role for plasma levels of tryptophan and kynurenine in the symptoms of COVID-19 patients.ConclusionOur results showed that plasma metabolome profiling is an efficient tool for the diagnosis and prognosis of SARS-CoV-2 infection.
| S-EPMC9556858 | biostudies-literature
Project description:Non-small cell lung cancer (NSCLC) is the main histologic form of lung cancer that affects human health, but biomarkers for therapeutic diagnosis and prognosis of the disease are currently lacking. The gene expression profile GSE18842 was downloaded from the Gene Expression Omnibus database in this prospective study, which consisted of 46 tumors and 45 controls. After screening differentially expressed genes (DEGs), we conducted functional enrichment analysis and KEGG analysis with upregulated differentially expressed genes (uDEGs) and downregulated differentially expressed genes (dDEGs), respectively. Protein-protein interaction (PPI) networks among DEGs and corresponding coding protein complexes, constructed using the STRING database, were analyzed using Cytoscape. Kaplan-Meier method was used to verify survival associated with hub genes. The GEPIA webserver was used to plot the gene expression level heat map of hub genes between NSCLC and adjacent lung tissues in the TCGA database. We identified 368 DEGs (168 uDEGs and 200 dDEGs) in NSCLC samples relative to control samples after gene integration. We established a PPI network for the DEGs, which had 249 nodes and 1472 edges protein pairs. Ten undefined hub genes with the highest connectivity degree (CDK1, UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11) were verified by survival analysis, and 9 of them were associated with poorer overall survival in NSCLC. The expression reliability of hub genes was verified by use of the GEPIA web tool. The results suggested that UBE2C, AURKA, CCNA2, CDC20, CCNB1, TOP2A, ASPM, MAD2L1, and KIF11 are inherent key biomarkers for diagnosis and prognosis, while KEGG analysis results showed the mitotic cell cycle pathway is a probable signaling pathway contributing to NSCLC progression. These genes could be promising biomarkers for diagnosis and provide a new approach for developing targeted therapeutic NSCLC drugs.
| S-EPMC6911305 | biostudies-literature
Project description:The development of cancer is driven by the accumulation of scores of alterations affecting the structure and function of the genome. Equally important in this process are genetic alterations and epigenetic changes. Whereas the former disrupt normal patterns of gene expression, sometimes leading to the expression of abnormal, constitutively active proteins, the latter deregulate the mechanisms such as transcriptional control leading to the inappropriate silencing or activation of cancer-associated genes. Both types of changes are inheritable at the cellular level, thus contributing to the clonal expansion of cancer cells. In this review, we summarize current knowledge on how genetic alterations in oncogenes or tumour suppressor genes, as well as epigenetic changes, can be exploited in the clinics as biomarkers for cancer detection, diagnosis and prognosis. We propose a rationale for identifying alterations that may have a functional impact within a background of "passenger" alterations that may occur solely as the consequence of deregulated genetic and epigenetic stability. Such functional alterations may represent candidates for targeted therapeutic approaches.
| S-EPMC5543860 | biostudies-other
Project description:BackgroundUntargeted metabolomics was used in case-control studies of adenocarcinoma (ADC) lung cancer to develop and test metabolite classifiers in serum and plasma as potential biomarkers for diagnosing lung cancer.MethodsSerum and plasma were collected and used in two independent case-control studies (ADC1 and ADC2). Controls were frequency matched for gender, age, and smoking history. There were 52 adenocarcinoma cases and 31 controls in ADC1 and 43 adenocarcinoma cases and 43 controls in ADC2. Metabolomics was conducted using gas chromatography time-of-flight mass spectrometry. Differential analysis was performed on ADC1 and the top candidates (FDR < 0.05) for serum and plasma used to develop individual and multiplex classifiers that were then tested on an independent set of serum and plasma samples (ADC2).ResultsAspartate provided the best accuracy (81.4%) for an individual metabolite classifier in serum, whereas pyrophosphate had the best accuracy (77.9%) in plasma when independently tested. Multiplex classifiers of either 2 or 4 serum metabolites had an accuracy of 72.7% when independently tested. For plasma, a multimetabolite classifier consisting of 8 metabolites gave an accuracy of 77.3% when independently tested. Comparison of overall diagnostic performance between the two blood matrices yielded similar performances. However, serum is most ideal given higher sensitivity for low-abundant metabolites.ConclusionThis study shows the potential of metabolite-based diagnostic tests for detection of lung adenocarcinoma. Further validation in a larger pool of samples is warranted.ImpactThese biomarkers could improve early detection and diagnosis of lung cancer.
| S-EPMC4633344 | biostudies-literature
Project description:Detection of DNA hypermethylation has emerged as a novel molecular biomarker for the evaluation of prostate cancer diagnosis and prognosis, but the use of single gene hypermethylation could produce unspecific results. Defining the specific gene hypermethylation profile for prostate cancer could provide groups of genes that specifically discriminate the patients with indolent and aggressive tumors. To compare the methylation profile of normal and malignant prostate we performed Genome-wide methylation analysis on 83 tumor samples, that included all clinical stages of the disease, 10 normal prostate samples and LNCaP, DU145 and PC3 prostate cancer cell lines using the GoldenGate Methylation Cancer Panel I (Illumina, Inc.) that interrogate for 1505 CpGs. We found 41 genes significantly hypermethylated in more than 20% of the tumors analyzed (p<0.01). Of these we identified GSTM2 and PENK as novel hypermethylated genes in prostate cancer that were simultaneously methylated in 40.9% of the tumors analyzed. We also identified panels of genes more frequently methylated in tumor samples with clinico-pathological indicators of poor prognosis: high Gleason score, elevated Ki-67 or advanced disease. Of these, we found that simultaneous hypermethylation of CFTR and HTR1B is common in patients with high Gleason score and high ki-67 levels and might signal the population at higher risk of therapeutic failure. DNA hypermethylation profile was associated with cancer-specific mortality (log-rank, p=0.007) and biochemical recurrence-free survival (log-rank, p=0.0008). In conclusion, our data strongly indicate that epigenetic silencing of GSTM2 and PENK is a common event in prostate cancer that could be used as a molecular marker for prostate cancer diagnosis. In addition, simultaneous HTR1B and CFTR hypermethylation could help discriminate aggressive from indolent prostate tumors.A DNA hypermethylation profile associated with cancer-specific mortality and biochemical recurrence in patients receiving radical prostatectomy is presented. GST2 and PENK are new hipermethylated genes identified. Simultaneous hypermethylation of CFTR and HTR1B could help discriminate aggressive disease.
2014-12-31 | GSE46177 | GEO
Project description:BackgroundLung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. At present, most patients with LUAD are diagnosed at an advanced stage, and the prognosis of advanced LUAD is poor. Hence, we aimed to identify novel biomarkers for the diagnosis and treatment of early stage LUAD and to explore their predictive value.MethodsThe microarray datasets GSE63459, GSE27262, and GSE33532 were searched, and the differentially expressed genes (DEGs) were obtained using GEO2R. The DEGs were subjected to gene ontology (GO) and pathway enrichment analyses using METASCAPE. A protein-protein interaction (PPI) network was plotted with STRING and visualized by Cytoscape. Module analysis of the PPI network was performed using MCODE. Overall survival (OS) analysis and analysis of the mRNA expression levels of genes identified by MCODE were performed with UALCAN. Western blot analysis of hub genes in LUAD patients, MTS assays, and clonogenic assays were performed to test the effects of the hub genes on cell proliferation in vitro.ResultsA total of 341 DEGs were obtained, which were mainly enriched in terms related to blood vessel development, growth factor binding, and extracellular matrix organization. A PPI network consisting of 300 nodes and 1140 edges was constructed, and a significant module including 15 genes was identified. Elevated expression of ASPM, CCNB2, CDCA5, PRC1, KIAA0101, and UBE2T was associated with poor OS in LUAD patients. In the protein level, the hub gene was overexpressed in LUAD patients. In vitro experiments showed that knockdown of the hub genes in the LUAD cell lines could promote cell proliferation.ConclusionsDEGs are potential biomarkers for early stage lung adenocarcinoma and could have utility for the diagnosis and predicting treatment efficacy.
| S-EPMC8085413 | biostudies-literature