scholarly journals HER3 expression and MEK activation in non-small-cell lung carcinoma

2021 ◽  
pp. LMT48
Author(s):  
Thubeena Manickavasagar ◽  
Wei Yuan ◽  
Suzanne Carreira ◽  
Bora Gurel ◽  
Susana Miranda ◽  
...  
Keyword(s):  
Small Cell Lung Carcinoma ◽  
Bioinformatic Analysis ◽  
The Cancer Genome Atlas ◽  
Small Cell Lung ◽  
Potential Therapeutic Target ◽  
Cell Lung Carcinoma ◽  
Downstream Signaling ◽  
Egfr Expression ◽  
Her3 Expression ◽  
Cancer Genome Atlas

Aim: We explore HER3 expression in lung adenocarcinoma (adeno-NSCLC) and identify potential mechanisms of HER3 expression. Materials & methods: Tumor samples from 45 patients with adeno-NSCLC were analyzed. HER3 and HER2 expression were identified using immunohistochemistry and bioinformatic interrogation of The Cancer Genome Atlas (TCGA). Results: HER3 was highly expressed in 42.2% of cases. ERBB3 copy number did not account for HER3 overexpression. Bioinformatic analysis of TCGA demonstrated that MEK activity score (a surrogate of functional signaling) did not correlate with HER3 ligands. ERBB3 RNA expression levels were significantly correlated with MEK activity after adjusting for EGFR expression. Conclusion: HER3 expression is common and is a potential therapeutic target by virtue of frequent overexpression and functional downstream signaling.

Identification of expression signatures for non-small-cell lung carcinoma subtype classification

2019 ◽  
Author(s):  
Ran Su ◽  
Jiahang Zhang ◽  
Xiaofeng Liu ◽  
Leyi Wei
Keyword(s):  
Lung Carcinoma ◽  
Small Cell Lung Carcinoma ◽  
Lung Squamous Cell Carcinoma ◽  
Small Cell ◽  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Subtype Classification

Abstract Motivation Non-small-cell lung carcinoma (NSCLC) mainly consists of two subtypes: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). It has been reported that the genetic and epigenetic profiles vary strikingly between LUAD and LUSC in the process of tumorigenesis and development. Efficient and precise treatment can be made if subtypes can be identified correctly. Identification of discriminative expression signatures has been explored recently to aid the classification of NSCLC subtypes. Results In this study, we designed a classification model integrating both mRNA and long non-coding RNA (lncRNA) expression data to effectively classify the subtypes of NSCLC. A gene selection algorithm, named WGRFE, was proposed to identify the most discriminative gene signatures within the recursive feature elimination (RFE) framework. GeneRank scores considering both expression level and correlation, together with the importance generated by classifiers were all taken into account to improve the selection performance. Moreover, a module-based initial filtering of the genes was performed to reduce the computation cost of RFE. We validated the proposed algorithm on The Cancer Genome Atlas (TCGA) dataset. The results demonstrate that the developed approach identified a small number of expression signatures for accurate subtype classification and particularly, we here for the first time show the potential role of LncRNA in building computational NSCLC subtype classification models. Availability and implementation The R implementation for the proposed approach is available at https://github.com/RanSuLab/NSCLC-subtype-classification.

scholarly journals The Fusion Circular RNA F-circEA1 Promotes Non-small Cell Lung Carcinoma Progression through ALK Downstream Signaling

2021 ◽  
Author(s):  
yinping Huo ◽  
Tangfeng Lv ◽  
Mingxiang Ye ◽  
Suhua Zhu ◽  
Jiaxin Liu ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Lung Carcinoma ◽  
Small Cell Lung Carcinoma ◽  
Circular Rna ◽  
Small Cell ◽  
Rt Pcr ◽  
Transplanted Tumors ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Downstream Signaling

Abstract Background Circular RNA has a stable closed structure, which plays an important role in the occurrence and development of tumors. However, there are no reports on the relationship between fusion circular RNA and EML4-ALK variant 1, or their underlying molecular mechanisms in non-small cell lung carcinoma (NSCLC). Methods To test F-circEA1 in NCI-H3122 cells (carrying the EML4-ALK variant 1 gene) by RT-PCR, FISH and Sanger sequencing; To determine cell proliferation with a CCK-8 assay. Transwell experiments were used to detect cell migration and invasion. Cell cycle stage and apoptosis were detected by flow cytometry. The sensitivity of cells to crizotinib was tested using a CCK-8 assay. RT-PCR and western blots were used to measure the expression of EML4-ALK1 and downstream signaling factors associated with ALK. Subcutaneously transplanted tumors were used in nude mice to determine the effect of F-circEA1 on tumor formation and the expression of EML4-ALK1 by immunohistochemistry. Statistical analyses were carried by GraphPad Prism 8.0 and differences between groups were compared using Student's t test. Difference with p value <0.05 is statistically significant.Results F-circEA1 was present both in the cytoplasm and nucleus of NCI-H3122 cells. F-circEA1 was contributed to cell proliferation, metastasis, invasion. F-circEA1 induced cell arrest, promoted cell apoptosis and improved drug sensitivity to crizotinib. After knockdown with F-circEA1, subcutaneously transplanted tumors in nude mice grew slowly, the expression of EML4-ALK1 in tumor tissues decreased significantly. The mRNA and protein levels of EML4-ALK1 decreased after knockdown of F-circEA1 but increased after its overexpression. The protein expression of downstream ALK signaling factors increased after overexpression of F-circEA1, but not at the mRNA level.Conclusions We have confirmed a potential carcinogenenic and therapeutic role for F-circEA1 in NSCLC. Our experimental results may provide a new biomarker, treatment method, and prognostic monitoring index for use in the clinic.

scholarly journals Prognostic Significance of TLR2, SMAD3 and Localization-dependent SATB1 in Stage I and II Non–Small-Cell Lung Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482110566
Author(s):  
Justyna Durślewicz ◽  
Anna Klimaszewska-Wiśniewska ◽  
Jakub Jóźwicki ◽  
Paulina Antosik ◽  
Marta Smolińska-Świtała ◽  
...  
Keyword(s):  
Small Cell Lung Carcinoma ◽  
Prognostic Significance ◽  
Small Cell ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Inverse Association ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Clinical Stages ◽  
Nsclc Patients

This study aimed to explore the prognostic value of SATB1, SMAD3, and TLR2 expression in non–small-cell lung carcinoma patients with clinical stages I-II. To investigate, we evaluated immunohistochemical staining to each of these markers using tissue sections from 69 patients from our cohort and gene expression data for The Cancer Genome Atlas (TCGA) cohort. We found that, in our cohort, high expression levels of nuclear SATB1n and SMAD3 were independent prognostic markers for better overall survival (OS) in NSCLC patients. Interestingly, expression of cytoplasmic SATB1c exhibited a significant but inverse association with survival rate, and it was an independent predictor of unfavorable prognosis. Likewise, TLR2 was a negative outcome biomarker for NSCLC even when adjusting for covariates. Importantly, stratification of NSCLCs with respect to combined expression of the three biomarkers allowed us to identify subgroups of patients with the greatest difference in duration of survival. Specifically, expression profile of SATB1n-high/SMAD3high/TLR2low was associated with the best OS, and it was superior to each single protein alone in predicting patient prognosis. Furthermore, based on the TCGA dataset, we found that overexpression of SATB1 mRNA was significantly associated with better OS, whereas high mRNA levels of SMAD3 and TLR2 with poor OS. In conclusion, the present study identified a set of proteins that may play a significant role in predicting prognosis of NSCLC patients with clinical stages I-II.

scholarly journals Loss of EGFR Mutation Expression Induced by NF1 Mutation in Non-Small Cell Lung Cancer: Non-Canonical Pathway of Tyrosine Kinase Inhibitors’ Resistance

2021 ◽  
Vol 18 (01) ◽  
Author(s):  
Jose Gabriel Negron Rodriguez ◽  
Luis Orrego Poma ◽  
Manuel Leiva Galvez ◽  
Maria Claudia Rodriguez Zavaleta ◽  
Wuilbert Rodriguez Pantigoso
Keyword(s):  
Cellular Proliferation ◽  
Egfr Mutation ◽  
Small Cell Lung Carcinoma ◽  
Small Cell ◽  
Canonical Pathway ◽  
Loss Of Function ◽  
Small Cell Lung ◽  
Apoptosis Resistance ◽  
Cell Lung Carcinoma ◽  
Egfr Expression

Non-small cell lung carcinoma (NSCLC) is considered the first cause of cancer-related death worldwide and many therapies have been developed against the presence of actionable mutations. For example, targeting the EGFR mutation has changed the overall prognosis in NSCLC. However, resistance to treatment has emerged and many canonical mechanisms have already been described. NF1 mutation causes partial or total loss of function of neurofibromin, which activates several intracellular pathways (MAPK / ERK, PI3K / AKT, TGF -β / Smad), producing cellular proliferation, migration, apoptosis resistance and genetic instability, leading to loss of EGFR expression. Herein, we describe a case of a novel activation of a non-canonical pathway that led to treatment resistance by NF1 mutation.

scholarly journals Exploring the prognostic value of SCARF1 in non-small cell lung cancers

2021 ◽  
Author(s):  
Daniel A. Patten ◽  
Shishir Shetty
Keyword(s):  
Endothelial Cells ◽  
Prognostic Value ◽  
Therapeutic Potential ◽  
Small Cell Lung Carcinoma ◽  
Lung Squamous Cell Carcinoma ◽  
Small Cell ◽  
The Cancer Genome Atlas ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Lung Cancers

AbstractScavenger receptor class F member 1 (SCARF1) has previously been shown to be highly expressed within the human liver, hold prognostic value in hepatocellular carcinoma and mediate the specific recruitment of leukocytes to liver sinusoidal endothelial cells; however, to date, the liver remains the only major organ in which SCARF1 has been explored in any detail. Here, we utilised publically-available RNA-sequencing data from The Cancer Genome Atlas (TGCA) to identify the lungs as a site of significant SCARF1 expression and attribute the majority of its expression to endothelial cell populations. Next, we show that SCARF1 expression is significantly reduced in two histologically distinct types of non-small cell lung carcinoma cancers (NSCLCs), lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), compared to non-tumoural tissues. Interestingly, loss of SCARF1 expression was associated with aggressive tumour biology in LUAD tissues, but not in LUSC. Furthermore, increased SCARF1 expression was highly prognostic of better overall survival in LUAD tumour tissues, but this was again in contrast to LUSC tumours, in which SCARF1 held no prognostic value. Finally, we showed that SCARF1 is widely expressed in tumour endothelial cells of non-small cell lung cancers and that its total expression in LUAD tumour tissues correlated with immune score and CD4+ T cell infiltration. This study represents the first detailed exploration of SCARF1 expression in normal and diseased human lung tissues and further highlights the prognostic value and therapeutic potential of SCARF1 in immunologically active cancers.

Semiparametric integrative interaction analysis for non-small-cell lung cancer

2020 ◽  
Vol 29 (10) ◽  
pp. 2865-2880
Author(s):  
Yang Li ◽  
Fan Wang ◽  
Rong Li ◽  
Yifan Sun
Keyword(s):  
Environmental Factors ◽  
Prediction Accuracy ◽  
Interaction Analysis ◽  
Small Cell Lung Carcinoma ◽  
Small Cell ◽  
The Cancer Genome Atlas ◽  
Alternative Methods ◽  
Cancer Outcomes ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma

In genomic analysis, it is significant though challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets, we propose a novel integrative interaction approach under a semiparametric model, in which genetic and environmental factors are included as the parametric and nonparametric components, respectively. The goal of this approach is to identify the genetic factors and gene–gene interactions associated with cancer outcomes, while estimating the nonlinear effects of environmental factors. The proposed approach is based on the threshold gradient-directed regularisation technique. Simulation studies indicate that the proposed approach outperforms alternative methods at identifying the main effects and interactions, and has favourable estimation and prediction accuracy. We analysed non-small-cell lung carcinoma datasets from the Cancer Genome Atlas, and the results demonstrate that the proposed approach can identify markers with important implications and that it performs favourably in terms of prediction accuracy, identification stability, and computation cost.

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