Small non-coding RNA biomarkers in sputum for lung cancer diagnosis

Guan

J of Cellular Biochemistry

2019

Objectives Current methods for diagnosing lung cancer (LC) have varying degrees of risks and complications. MicroRNA (miRNA) is a small molecule noncoding RNA with gene regulation functions. Many studies have shown that miRNA can be used for the diagnosis of LC, but there are differences in diagnostic accuracy. Therefore, we aim to systematically review and meta‐analyze published articles to comprehensively evaluate the diagnostic value of miRNA for LC. Materials and methods We searched the PubMed, Embase, and Cochrane databases, and calculated the area under the curve (AUC) by plotting the summary receiver operator characteristic curve using the sensitivity and specificity of each included study. The AUC was calculated and the likelihood ratio was plotted to assess the diagnostic accuracy of miRNA. We used QUADAS‐2 in Review Manager 5.3 to evaluate the quality of all the articles. The other analyses were performed using the STATA 12.0 software. Results We included a total of 29 articles, 98 studies, and the qualities of all the articles were satisfactory. The overall pooled parameters calculated from all studies were as follows: sensitivity = 0.77, specificity = 0.83, positive likelihood ratio (PLR) = 4.6, negative likelihood ratio (NLR) = 0.28, and AUC = 0.87 for miRNA diagnosis. It had significant advantages over other biomarkers. Subgroup analysis showed that when combined four or more miRNA for the diagnosis of LC, the parameters were as follows: sensitivity = 0.90, specificity = 0.93, PLR = 13.2, NLR = 0.11, and AUC = 0.97. Conclusion Four or more miRNA combination could be used for the diagnosis of LC. Besides this, we also found that miRNA showed a greater advantage in distinguishing LC from benign lung diseases than distinguishing between LC and normal people. Our findings provided a new way of thinking about the clinical diagnosis of LC from a nonmorphological aspect.

Section: Resultsmentioning

confidence: 99%

Could microRNA be used as a diagnostic tool for lung cancer?

Guan

J of Cellular Biochemistry

2019

“…Furthermore, contingency table and logistic regression analysis were applied to determine the associations between the expression levels of the miRNAs and both clinicopathologic and demographic characteristics of the cases and controls. In addition, we analyzed the significantly associated miRNAs by using logistic regression models with constrained parameters as least absolute shrinkage and selection operator (LASSO) to eliminate the less relevant variables . We estimated functions of the combined miRNA biomarkers by logistic regression with or without adjustment for known risk factors for NSCLC.…”

Section: Methodsmentioning

confidence: 99%

MicroRNA‐based biomarkers for diagnosis of non‐small cell lung cancer (NSCLC)

et al. 2020

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Background The development of biomarkers for the early detection of non‐small cell lung cancer (NSCLC) is clinically important. We have developed miRNA biomarkers in sputum and plasma, respectively, for NSCLC. Herein, we evaluate whether integrated analysis of the miRNAs across the different types of specimens could improve the early detection of NSCLC. Methods Using reverse transcription PCR, we determined expressions of two miRNAs (miRs‐31‐5p and 210‐3p) in sputum and three miRNAs (miRs‐21‐5p, 210‐3p, and 486‐5p) in plasma of a training cohort of 76 NSCLC patients and 72 cancer‐free smokers. The results were validated in a testing cohort of 56 NSCLC patients and 55 cancer‐free smokers. Results The panels of two sputum miRNAs and three plasma miRNAs had 65.8–75.0% sensitivities and 83.3–87.5% specificities for diagnosis of NSCLC in the training cohort. The individual sputum or plasma miRNA panel had a higher sensitivity for squamous cell carcinoma or adenocarcinoma of the lung, respectively. From the miRNAs, we optimized an integrated panel of biomarkers consisting of two sputum miRNAs (miRs‐31‐5p and 210‐3p) and one plasma miRNA (miR‐21‐5p) that had higher sensitivity (85.5%) and specificity (91.7%) for diagnosis of NSCLC compared with the individual panels alone. Furthermore, the performance of the integrated panel of biomarkers was independent of histology and stage of NSCLC, and patients' age, sex, and ethnicity. The performance of the integrated panel of biomarkers was confirmed in the testing cohort. Conclusions Integrating biomarkers across different body fluids would synergistically improve the early detection of NSCLC. Key points Lung cancer is a heterogeneous disease and develops from complex aberrations. Integrating sputum and plasma miRNAs has higher accuracy than when they are used alone

“…Moreover, we used RBC lysis solution to maximally reduce the possible contamination from RBCs in plasma . We extracted RNA from plasma by using a protocol with miRNeasy Mini Kit spin column as described in our published work . In addition, we analyzed expression levels of RBC‐related miRNA (mir‐451), myeloid‐related miRNA (miR‐223), and lymphoid‐associated miRNA (miR‐150) in all the RNA samples.…”

Section: Methodsmentioning

confidence: 99%

“…13,[28][29][30][31][32][33] We extracted RNA from plasma by using a protocol with miR-Neasy Mini Kit spin column as described in our published work. 8,[11][12][13]26,27,[34][35][36][37][38][39][40][41][42][43][44][45][46][47] In addition, we analyzed expression levels of RBC-related miRNA (mir-451), myeloid-related miRNA (miR-223), and lymphoid-associated miRNA (miR- 150) in all the RNA samples. The samples that were positive to these blood cells-related miRNAs were excluded from the study.…”

Section: Blood Collection Plasma Preparation and Rna Isolationmentioning

confidence: 99%

A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules

et al. 2017

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Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (All P <0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.