Dataset Information

Transcripts related to lung cancer disease in blood samples obtained from diagnostic and pre-diagnostic cohorts

ABSTRACT: Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The circulating transcriptome of LC patients represents a source of potential LC biomarkers. We used genome-wide RNA sequencing to identify LC candidate markers by comparing expression of >60,000 genes in whole blood specimens taken at LC diagnosis from cases (n=128) and controls (n=62). Further, we evaluated expression of these markers in two population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n=163 cases, 184 matched controls). We identified 14 candidate genes in whole blood associated with LC at diagnosis. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (adjusted p-values 0.009, 0.03, and 0.007, respectively). We observed strong association of ANXA3 and ARG1 expression with LC also in the pre-diagnostic blood specimens, and especially with late-stage LC within two years of diagnosis (odds ratios 3.47 and 5.00, respectively). Although blood neutrophils were elevated in LC cases both in the diagnostic and pre-diagnostic blood specimens, the observed associations of ANXA3, ARG1 and HP with LC were preserved also after adjusting for elevated blood neutrophils. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE198048 | GEO | 2023/11/29

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Bronchoscopy is a frequently used initial diagnostic procedure for patients with suspected lung cancer (LC). Cytological examinations of bronchial washing (BW) samples obtained during bronchoscopy often yield inconclusive results regarding LC diagnosis. The present study aimed to identify molecular biomarkers as a non-invasive method for LC diagnosis. Aberrant DNA methylation is used as a useful biomarker for LC. Therefore, microarray-based methylation profiling analyses on 13 patient-matched tumor tissues at stages I-III vs. non-tumor tissues were performed, and a group of highly differentially methylated genes was identified. A subsequent analysis using bisulfite-pyrosequencing with additional tissues and cell lines revealed six methylated genes [ADAM metallopeptidase with thrombospondin type 1 motif 20, forkhead box C2 (mesenchyme forkhead 1), NK2 transcription factor related, locus 5 (Drosophila), oligodendrocyte transcription factor 3, protocadherin γ subfamily A 12 (PCDHGA12) and paired related homeobox 1 (PRRX1)] associated with LC. Next, a highly sensitive and accurate detection method, linear target enrichment-quantitative methylation-specific PCR in a single closed tube, was applied for clinical validation using BW samples from patients with LC (n=68) and individuals with benign diseases (n=33). PCDHGA12 and PRRX1 methylation were identified as the best-performing biomarkers to detect LC. The two-marker combination showed a sensitivity of 82.4% and a specificity of 87.9%, with an area under the curve of 0.891. Notably, the sensitivity for small cell LC was 100%. The two-marker combination had a positive predictive value of 93.3% and a negative predictive value of 70.7%. The sensitivity was higher than that of cytology, which only had a sensitivity of 50%. The methylation status of the two-marker combination showed no association with sex, age or stage, but was associated with tumor location and histology. In conclusion, the present study showed that the regulatory regions of PCDHGA12 and PRRX1 are highly methylated in LC and can be used to detect LC in BW specimens as a diagnostic adjunct to cytology in clinical practice.

Project description:In cancer management, early and accurate diagnosis of hepatocellular carcinoma (HCC) is important for enhancing survival rate of patients. Currently, serum alpha-fetoprotein (AFP) is the only one biomarker for detection of HCC. However, serum AFP is not satisfactory for diagnosis of HCC due to its low accuracy (about 60-70%). In this study, we collected 109 serum samples (discovery set) from healthy control (HC) and patients with chronic hepatitis B (CHB), liver cirrhosis (LC) and HCC, and analyzed them with custom lncRNA microarray. Profiling analysis shows 181 differentially expressed lncRNAs between HCs and patients with CHB, LC and HCC. Then a 48-lncRNA diagnostic signature was identified with 100% predictive accuracy for all subjects in the discovery set. This diagnostic signature was verified with a cross-validation analysis in the discovery set. To further corroborate the signature, we gathered another 66 serum samples (validation set) and also analyzed them with microarray. The result indicates that the same signature has similar diagnostic accuracy for HC (100%), CHB (73%), LC (88%) and HCC (95%), implying a reproducible diagnostic biomarker for HCC. Receiver operating characteristic (ROC) analysis exhibits that this signature has significantly higher diagnostic accuracy for HCC and non-cancerous subjects (area under curve [AUC]: 0.994) than AFP (AUC: 0.773) in the discovery set and this was also verified in the validation set (0.964 vs 0.792). More importantly, the signature detected small HCC (<3cm) with 100% (13/13) accuracy while AFP with only 61.5% (8/13). Altogether, this study demonstrates that the serum 48-lncRNA signature is not only a powerful and sensitive biomarker for diagnosis of HCC but also a potential biomarker for LC. ***************************************************************** Submitter declares these data are subject to patent number ZL 2016 1 0397094. *****************************************************************

Dataset Information

Transcripts related to lung cancer disease in blood samples obtained from diagnostic and pre-diagnostic cohorts

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure