New microRNA-based diagnostic test for lung cancer classification.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10528-10528
Author(s):  
Ranit Aharonov ◽  
Gila Lithwick Yanai ◽  
Hila Benjamin ◽  
Mats Olot Sanden ◽  
Marluce Bibbo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Carcinoma ◽  
Expression Profiles ◽  
Lung Tumors ◽  
Small Cell ◽  
Diagnostic Methods ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Cancer Types

10528 Background: Lung cancer is the leading cause of cancer deaths in the US. Treatment options are determined by tumor subtyping, for which there is lack of standardized, objective, and highly accurate techniques. In 20%-30% of cases significant limitations of tumor quantity and quality prevent full classification of the tumor using traditional diagnostic methods. Using microRNA microarray data generated from over a hundred formalin-fixed, paraffin-embedded (FFPE) primary lung cancer samples, we have identified microRNA expression profiles that differ significantly for the main lung cancer types. Based on these findings, we have developed and validated a microRNA-based qRT-PCR assay that differentiates primary lung cancers into four types: squamous cell carcinoma, non-squamous non-small cell lung cancer, carcinoid and small cell carcinoma. Methods: Over 700 primary tumor samples from different histological types of lung cancer were collected. Samples included FFPE blocks from resection or biopsies and cell blocks from cytology specimens including fine needle aspiration, bronchial brushing and bronchial washing. High-quality RNA was extracted from the samples using proprietary protocols. Expression levels of potential microRNA biomarkers were profiled using microarrays followed by a sensitive and specific qRT-PCR platform. An assay for lung tumors classification using 8 microRNAs on qRT-PCR was developed based on data from 261 samples. This assay was validated on an independent blinded set of 451 cytological and pathological samples. Results: Using the expression levels of 8 microRNAs measured in qRT-PCR, accurate classification of the primary lung tumors into the four main cancer types is obtained. The microRNA-based assay reached an accuracy of 94%. Moreover, cytological samples composed over 50% of the validation set and reached an accuracy of 95%. Conclusions: We present here a new microRNA-based assay for the classification of the four main types of lung cancer based only on the expression of 8 microRNAs. This assay displays very high levels of accuracy for both pathological and cytological samples. The assay comprises a standardized, well-tested and objective tool which can assist physicians in the diagnosis of lung cancer.

lncRNAs as Potential Targets in Small Cell Lung Cancer: MYC -dependent Regulation

2020 ◽  
Vol 20 (17) ◽  
pp. 2074-2081
Author(s):  
Onur Tokgun ◽  
Pervin E. Tokgun ◽  
Kubilay Inci ◽  
Hakan Akca
Keyword(s):  
Lung Cancer ◽  
Stem Cell ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Therapeutic Strategy ◽  
Small Cell ◽  
Small Cell Lung ◽  
Expression Levels ◽  
Qrt Pcr ◽  
Shrna Vector

Background: Small Cell Lung Cancer (SCLC) is a highly aggressive malignancy. MYC family oncogenes are amplified and overexpressed in 20% of SCLCs, showing that MYC oncogenes and MYC regulated genes are strong candidates as therapeutic targets for SCLC. c-MYC plays a fundamental role in cancer stem cell properties and malignant transformation. Several targets have been identified by the activation/repression of MYC. Deregulated expression levels of lncRNAs have also been observed in many cancers. Objective: The aim of the present study is to investigate the lncRNA profiles which depend on MYC expression levels in SCLC. Methods: Firstly, we constructed lentiviral vectors for MYC overexpression/inhibition. MYC expression is suppressed by lentiviral shRNA vector in MYC amplified H82 and N417 cells, and overexpressed by lentiviral inducible overexpression vector in MYC non-amplified H345 cells. LncRNA cDNA is transcribed from total RNA samples, and 91 lncRNAs are evaluated by qRT-PCR. Results: We observed that N417, H82 and H345 cells require MYC for their growth. Besides, MYC is not only found to regulate the expressions of genes related to invasion, stem cell properties, apoptosis and cell cycle (p21, Bcl2, cyclinD1, Sox2, Aldh1a1, and N-Cadherin), but also found to regulate lncRNAs. With this respect, expressions of AK23948, ANRIL, E2F4AS, GAS5, MEG3, H19, L1PA16, SFMBT2, ZEB2NAT, HOTAIR, Sox2OT, PVT1, and BC200 were observed to be in parallel with MYC expression, whereas expressions of Malat1, PTENP1, Neat1, UCA1, SNHG3, and SNHG6 were inversely correlated. Conclusion: Targeting MYC-regulated genes as a therapeutic strategy can be important for SCLC therapy. This study indicated the importance of identifying MYC-regulated lncRNAs and that these can be utilized to develop a therapeutic strategy for SCLC.

scholarly journals Identification of LOC338963 and AP3B2 as Potential Biomarkers for Lambert-Eaton Myasthenic Syndrome

2021 ◽  
Author(s):  
Fei Yang ◽  
Feng Jing ◽  
Yang Li ◽  
Shanshan Kong ◽  
Shimin Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Expression Profiles ◽  
Small Cell ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Small Cell Lung ◽  
Qrt Pcr ◽  
Myasthenic Syndrome

Abstract Background: Lambert-Eaton myasthenic syndrome (LEMS) is a rare neuromuscular junction disorder associated with muscle weakness and small-cell lung cancer. Here, we used microarray analysis to identify long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) that might serve as biomarkers for LEMS.Methods: Plasma lncRNA and mRNA expression profiles of three patients with paraneoplastic LEMS and three healthy controls were analyzed using Arraystar Human lncRNA Microarray v4.0. Differentially expressed lncRNAs and adjacent mRNAs were analyzed jointly, and candidates were verified in individual samples by quantitative real-time polymerase chain reaction (qRT-PCR). The identified lncRNAs and mRNAs were evaluated in nine patients with paraneoplastic LEMS, eight patients with non-tumor LEMS, and four patients with small cell lung cancer (SCLC). Results: A total of 320 lncRNAs were differentially expressed in patients with paraneoplastic LEMS compared to healthy controls (fold change >1.5, P < 0.05), and nine were further evaluated. One of the identified lncRNAS, LOC338963 (NR_031439), is known to regulated the expression of the mRNA AP3B2, and both were upregulated more than 2-fold in patients with paraneoplastic LEMS compared to healthy controls. Furthermore, qRT-PCR analysis revealed significant upregulation of LOC338963 (NR_031439) and AP3B2 expression in patients with paraneoplastic LEMS compared to those with either non-tumor LEMS (2.37- and 5.06-fold, respectively) or SCLC (4.36- and 14.97-fold, respectively).Conclusions: Plasma LOC338963 (NR_031439) and AP3B2 were found to be upregulated in LEMS and might be used as diagnostic biomarkers for this disease.

scholarly journals Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Atsushi Teramoto ◽  
Tetsuya Tsukamoto ◽  
Yuka Kiriyama ◽  
Hiroshi Fujita
Keyword(s):  
Lung Cancer ◽  
Cell Carcinoma ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
Lung Cancers ◽  
Microscopic Images ◽  
Cancer Types

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma

2020 ◽  
pp. 1-12
Author(s):  
Jiangqing Yu ◽  
Fen Du ◽  
Liping Yang ◽  
Ling Chen ◽  
Yuanxiang He ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Small Cell Carcinoma ◽  
Squamous Cell ◽  
Small Cell ◽  
Serum Biomarkers ◽  
Stepwise Discriminant Analysis ◽  
Mlp Model

BACKGROUND: Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC. PATIENTS AND METHODS: A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptronMLPwere employed to screen the most efficient combinations of markers for classification. RESULTS: The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-Adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters. CONCLUSION: Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.

Use of microRNAs to distinguish small from non-small lung cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22061-e22061
Author(s):  
Y. Spector ◽  
E. Meiri ◽  
A. Faerman ◽  
M. Ben David ◽  
M. Zepeniuk ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Differential Diagnosis ◽  
Lung Tumors ◽  
Small Cell ◽  
Microrna Expression ◽  
Small Cell Lung ◽  
Primary Tumors ◽  
Cell Lung Carcinoma ◽  
Lung Carcinoid

e22061 Background: Lung tumors are divided to two main classes: non-small cell lung cancer (NSCLC) that accounts for ∼80–85% of all lung primary tumors, and tumors from neuroendocrine origin - mainly small cell lung carcinoma and lung carcinoid. The classification of lung tumors can present a diagnostic challenge. New markers for different subtypes may aid in diagnosing difficult cases, can improve the accuracy of classification and could be important for selecting proper treatment. Here we studied the utility of microRNA as biomarkers for this differential diagnosis. MicroRNAs, a family of short non-coding regulatory RNAs, are highly tissue-specific and are well preserved in routinely prepared formalin-fixed, paraffin-embedded (FFPE) specimens, making them promising candidates as biomarkers for tissue and tumor classification. Methods: We used proprietary protocols for extracting high-quality RNA from FFPE samples. We used microRNA microarrays to profile more than a hundred samples from different histological subtypes of lung cancer including small cell, lung carcinoid and various types of NSCLC. Differential microRNA expression was verified using a microRNA qRT- PCR platform. Results: We found that several microRNAs are significantly differentially expressed between different subtypes of lung cancers. Specifically, using combinations of few microRNAs, we were able to accurately differentiate between neuroendocrine and NSCLC. Small cell and carcinoid tumors can be further distinguished using the signals of additional microRNAs, with very high sensitivity and specificity. Conclusions: Our results underscore the potential of microRNA expression for classification of tumor subtypes. We found that combinations of small numbers of microRNAs can successfully aid in the differential diagnosis of lung tumors, and provide a basis for the development of simple and reliable assays for clinical oncology. [Table: see text]

scholarly journals Expression and Significance of HPV16 E6/E7 mRNAs in the Bronchial Brush and TBNA Cells of Patients With Small Cell Lung Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110195
Author(s):  
Di-Jia Zou ◽  
Ya-Bin Zhao ◽  
Jing-Hua Yang ◽  
Hong-Tao Xu ◽  
Qing-Chang Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Squamous Cell Carcinoma ◽  
Small Cell Lung Cancer ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Central Type ◽  
Expression Levels

Background and Objective: Small cell lung cancer (SCLC) is characterized by rapid growth, strong invasion, and early metastasis. However, the cause of its occurrence remains unclear. High-risk HPV infection is closely related to the occurrence of non-small cell lung cancer and cervical small cell neuroendocrine carcinoma. Methods: The expression levels of E6 mRNA and E7 mRNA in HPV16 were detected by qRT-PCR in the bronchial brushing and transbronchial needle aspiration (TBNA) of 310 patients with lung cancer and with benign lung diseases. To make the design of this experiment scientific and reasonable, the expression levels in lung squamous cell carcinoma were taken as positive controls, while those in benign cells were taken as negative controls. Results: The expression levels of E6 mRNA and E7 mRNA in SCLC group were significantly higher than those in benign cell group and slight higher than those in squamous cell carcinoma group. The expression levels of E6 mRNA and E7 mRNA in the central type of SCLC were significantly higher than those in the peripheral type of SCLC. Conclusions: We speculate that the occurrence of some small cell carcinoma is the same as that of some squamous cell carcinoma, which is closely related to HPV16 infection. The overexpression of E6 mRNA and E7 mRNA is in some benign lesion cells, which may be related to HPV transient infection.

Concise genetic profile of lung carcinoma

2021 ◽  
pp. postgradmedj-2021-139860
Author(s):  
Srikanth Umakanthan ◽  
Maryann M Bukelo
Keyword(s):  
Lung Cancer ◽  
Cell Carcinoma ◽  
Microscopic Analysis ◽  
Genetic Alterations ◽  
Small Cell ◽  
The Cancer Genome Atlas ◽  
Genetic Profile ◽  
Cancer Evolution ◽  
Lung Cancers

The WHO classification of lung cancer (2015) is based on immunohistochemistry and molecular evaluation. This also includes microscopic analysis of morphological patterns that aids in the pathological diagnosis and classification of lung cancers. Lung cancers are the leading cause of cancer deaths worldwide. Recent advancements in identifying the etiopathogenesis are majorly driven by gene mutation studies. This has been explained by The Cancer Genome Atlas, next-generation sequencer and TRAcking non-small cell lung cancer evolution through therapy [Rx]. This article reviews the genetic profile of adenocarcinoma, squamous cell carcinoma, small cell carcinoma, large cell neuroendocrine carcinoma and pulmonary carcinoids. This includes the prolific genetic alterations and novel molecular changes seen in these tumours. In addition, target- specific drugs that have shown promising effects in clinical use and trials are also briefly discussed.

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