A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

Li, Hui; Jiang, Zhengran; Leng, Qixin; Bai, Fan; Wang, Juan; Ding, Xiaosong; Li, Yuehong; Zhang, Xianghong; Fang, Hong-Bin; Yfantis, Harris G.; Xing, Lingxiao; Jiang, Feng

doi:10.18632/oncotarget.17038

Cited by 22 publications

(25 citation statements)

References 58 publications

(96 reference statements)

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“…To avoid the contamination, we prepared plasma from blood within 2 h after the collection as previously described . 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 .…”

Section: Methodsmentioning

confidence: 99%

“…The number of positive reactions, together with Poisson's distribution, were used to produce a straight and high‐confidence measurement of the original target concentration . Therefore, ddPCR could absolutely quantify targeted nucleic acid sequences without requiring external calibrators or endogenous controls (genes) . The plate was loaded on Droplet Reader (Bio‐Rad), by which copy number of each miRNA per µL PCR reaction mixture was directly determined.…”

Section: Methodsmentioning

confidence: 99%

“…11,26,27 Moreover, we used RBC lysis solution to maximally reduce the possible contamination from RBCs in plasma. 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.…”

Section: Blood Collection Plasma Preparation and Rna Isolationmentioning

confidence: 99%

“…28 Therefore, ddPCR could absolutely quantify targeted nucleic acid sequences without requiring external calibrators or endogenous controls (genes). 26,28,29 The plate was loaded on Droplet Reader (Bio-Rad), by which copy number of each miRNA per mL PCR reaction mixture was directly determined. All assays were performed in triplicates.…”

Section: Droplet Digital Pcr (Ddpcr)mentioning

confidence: 99%

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Blood Collection Plasma Preparation and Rna Isolationmentioning

confidence: 99%

Section: Droplet Digital Pcr (Ddpcr)mentioning

confidence: 99%

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

et al. 2017

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“…MiRs are considered as promising molecular markers for the non-invasive early diagnosis of NSCLC (18,21) and can be assessed in an inexpensive and patients-friendly way in the peripheral blood exosomes (23). Up to date, miRs have been described as potential biomarkers detecting early stages of NSCLC (miR-182, miR-183, miR-210, and miR-126) (24) or distinguishing SCC from AC (miR-26a; small miR panel-205-5p, 944) (15,25). In our study, we focused on the miR-17 and miR-20a targeting MMP2 and TIMP3; these were selected based on TCGA data with further miRTarBase and literature validation (see section Selection of microRNA Molecules).…”

Section: Introductionmentioning

confidence: 99%

A Strong Decrease in TIMP3 Expression Mediated by the Presence of miR-17 and 20a Enables Extracellular Matrix Remodeling in the NSCLC Lesion Surroundings

et al. 2019

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Background: Lung cancer is one of the most common causes of death worldwide with a relatively high fatality rate and a mean 5-years survival of about 18%. One of the hallmarks of cancer is the extracellular matrix (ECM) remodeling, which is crucial for metastasis. This process may be regulated by miRs targeting metalloproteinases (MMPs) associated with the ECM breakdown and metastatic process or blocking the action of tissue inhibitors of metalloproteinases (TIMPs). Search for early biomarkers is essential in detecting non-small cell lung cancer (NSCLC) and distinguishing its subtypes: Adenocarcinoma (AC) from Squamous Cell Carcinoma (SCC), enabling targeted chemotherapy.Methods: MiR-17 and miR-20a targeting MMP2 and TIMP3 were selected by TCGA data analysis with further validation using miRTarBase and literature. The study group comprised 47 patients with primary NSCLC (AC and SCC subtypes). RNA was isolated from the tumor and normal-looking neighboring tissue (NLNT) free of cancer cells. MiRs from peripheral blood exosomes were extracted on admission and 5-7 days after surgery. Gene and miRs expression were assessed in qPCR using TaqMan probes. Results:The MMP2 has been expressed on a similar level in NLNT, as in cancer. While, TIMP3 expression was decreased both in cancer tissue and NLNT, with significantly lower expression in cancer. TIMP3 downregulation in NLNT and in SCC subtype correlated negatively with miR-20a. The preoperative miR-17 expression was significantly higher among patients with SCC compared to AC. Receiver operating characteristic (ROC) analysis of miR-17 as AC subtype classifier revealed 90% specificity and 48% sensitivity in optimal cut-off point with area under ROC curve (AUC): 0.71 (95%CI: 0.55-0.87). Within NSCLC subtypes: a strong negative correlation between pack-years (PY) and TIMP3 expression was observed for NLNT in the SCC group. Czarnecka et al. ECM Remodeling in NSCLC Conclusion:The TIMP3 silencing observed in the NLNT and its negative correlation with presurgical expression of miR-20a (from serum exosomes), suggest that miRs can influence ECM remodeling at a distance from the center of the lesion. The miRs expression pattern in serum obtained before surgery significantly differs between AC and SCC subtypes. Moreover, decreased TIMP3 expression in NLNT (in SCC group) negatively correlates with the amount of tobacco smoked in a lifetime in PY.Keywords: NSCLC molecular diagnostic markers, miRNA regulation, microRNA, extracellular matrix remodeling, metalloproteinases, tissue inhibitors of metalloproteinases, exosomes absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Current and Emerging Applications of Droplet Digital PCR in Oncology: An Updated Review

et al. 2021

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A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

Abstract: ABSTRACT

Cited by 22 publications

References 58 publications

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

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

A Strong Decrease in TIMP3 Expression Mediated by the Presence of miR-17 and 20a Enables Extracellular Matrix Remodeling in the NSCLC Lesion Surroundings

Current and Emerging Applications of Droplet Digital PCR in Oncology: An Updated Review

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