Dataset Information

Non-small cell lung cancer associated microRNA expression signature: integrated bioinformatics analysis, validation and clinical significance.

ABSTRACT: Recently, increasing studies of miRNA expression profiling has confirmed that miRNA plays an essential role in non-small cell lung cancer (NSCLC). However, inconsistent or discrepant results exist in these researches. In present study, we performed an integrative analysis of 32 miRNA profiling studies compared the differentially expressed miRNA between NSCLC tissue and non-cancerous lung tissue to identify candidate miRNAs associated with NSCLC. 7 upregulated and 10 downregulated miRNAs were identified as miRNA integrated-signature using Robust Rank Aggregation (RRA) method. qRT-PCR demonstrated that miR-21-5p, miR-210, miR-205-5p, miR-182-5p, miR-31-5p, miR-183-5p and miR-96-5p were up-regulated, whereas miR-126-3p, miR-30a-5p, miR-451a, miR-143-3p and miR-30d-5p were down-regulated more than 2 folds in the NSCLC, which was further validated in Tumor Cancer Genome Atlas (TCGA) database. Receiver operating characteristic (ROC) curve analysis confirmed that 9 miRNAs had good predictive performance (AUC > 0.9). Cox regression analysis revealed that miR-21-5p (hazard ratio [HR]: 1.616, 95% CI: 1.114-2.342, p = 0.011) and miR-30d-5p (HR: 0.578, 95% CI: 0.400-0.835, p = 0.003) were independent prognostic factors in NSCLC for overall survival. The accumulative effects of the two miRNAs on the prognosis of NSCLC were further estimated. The results showed that patients with two positive markers had a worse prognosis than those with one or none positive marker. In conclusion, this study contributes to the comprehension of the role of miRNAs in NSCLC and provides a basis for further clinical application.

SUBMITTER: Li C

PROVIDER: S-EPMC5421870 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Non-small cell lung cancer associated microRNA expression signature: integrated bioinformatics analysis, validation and clinical significance.

Li Chunyu C Yin Yunhong Y Liu Xiao X Xi Xuejiao X Xue Weixiao W Qu Yiqing Y

Oncotarget 20170401 15

Recently, increasing studies of miRNA expression profiling has confirmed that miRNA plays an essential role in non-small cell lung cancer (NSCLC). However, inconsistent or discrepant results exist in these researches. In present study, we performed an integrative analysis of 32 miRNA profiling studies compared the differentially expressed miRNA between NSCLC tissue and non-cancerous lung tissue to identify candidate miRNAs associated with NSCLC. 7 upregulated and 10 downregulated miRNAs were ide ...[more]

PMID: 28445945

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Project description:BackgroundGastric cancer (GC) is one of the common gastrointestinal malignancy worldwide and exhibits a poor prognosis. Increasing studies have indicated that microRNAs play critical roles in the cancer progression and have shown great potential as useful biomarkers. The search for potential diagnostic and prognostic biomarkers of gastric cancer (GC) with integrated bioinformatics analyses has been undertaken in previous studies.MethodsIn this study, the robust rank aggregation (RRA) method was used to perform an integrated analysis of differentially expressed miRNAs (DEMs) from five microarray datasets in the Gene Expression Omnibus (GEO) database to find robust biomarkers for GC. Ultimately, seven miRNAs were filtered from fourteen primary miRNAs using the validation set of The Cancer Genome Atlas (TCGA) database. Based on these results, diagnostic and survival analyses were performed, and logistic regression and Cox regression were used to determine the clinicopathological characteristics of the DEM expression and overall survival.ResultsNine eligible miRNA datasets related to GC were selected from the GEO database for integrated analysis in this study. Diagnostic analysis implied that these miRNAs could be regarded as promising candidate diagnostic biomarkers in GC tissues, but whether the results of the tissue analysis are consistent with those of peripheral blood analysis requires further validation. The logistic regression indicated that the ectopic expression of these DEMs was relevant to the histological type, anatomical region, and pathological grade of GC. However, the survival and Cox regression analyses suggested that the poor prognosis of GC patients was not strongly dependent on the ectopic expression of the seven miRNAs, but rather, a poor prognosis was associated with age, metastasis, and histological grade.ConclusionsBased on the results presented in this study it can be concluded that these miRNAs (miR-455-3p, miR-135b-5p, let-7a-3p, miR-195-5p, miR-204-5p, miR-149-5p, and miR-143-3p) might be potential biomarkers for the early diagnosis of GC patients, but this finding should be regarded with caution. A large-scale, prospective, and multicenter cohort study should be performed.

Project description:BackgroundCT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.ResultsBlood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n?=?363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n?=?138 in the first validation and n?=?135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.ConclusionsSelecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.

Dataset Information

Non-small cell lung cancer associated microRNA expression signature: integrated bioinformatics analysis, validation and clinical significance.

Publications

Non-small cell lung cancer associated microRNA expression signature: integrated bioinformatics analysis, validation and clinical significance.

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