scholarly journals The Cancer Genome Atlas Predicts the Prognosis of Lung Carcinoma Based on Genes Associated with m6A Methylation

2021 ◽  
Author(s):  
Huanqing Liu ◽  
Tingting Li ◽  
Chunsheng Dong ◽  
Jun Lyu
Keyword(s):  
Lung Cancer ◽  
Survival Rate ◽  
Lung Carcinoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Predictive Values ◽  
Rna Methylation ◽  
Cancer Genome Atlas ◽  
M6a Rna Methylation ◽  
Genome Atlas

Abstract Lung cancer has become a predominant cause of death in relation with carcinoma worldwide. N6-methylladenosine (m6A) is a common mRNA that is internally modified, which has a pivotal role in mRNA splicing, outputting, localizing, and translating and in identifying stable features. This study evaluated the expression pattern and prognostic value of m6A-related genes in lung cancer. Expression data of lung cancer samples with related clinical information were obtained from The Cancer Genome Atlas (TCGA). Then, R software was used in combination with several corresponding software packages to identify the regulatory factors of m6A RNA methylation with differential expression. Three genes (METTL3, YTHDF1, and FTO) were overexpressed in lung cancer. METTL3 had a low survival rate (P < 0.05). Significant differences in survival rate were observed among the subgroups, which possessed differently expressed m6A levels. Two latent predicting factors (METTL3 and KIAA1429) that met the independent predictive values were selected. M6A RNA methylation modulators may be involved with the malignant progression of lung cancer, and the two selected risk characteristics of m6A RNA methylation regulators may be a potential prognostic biological marker for guiding customized therapies in patients with lung carcinoma.

scholarly journals Role of INSL4 Signaling in Sustaining the Growth and Viability of LKB1-Inactivated Lung Cancer

2018 ◽  
Vol 111 (7) ◽  
pp. 664-674 ◽  
Author(s):  
Rongqiang Yang ◽  
Steven W Li ◽  
Zirong Chen ◽  
Xin Zhou ◽  
Wei Ni ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Transcriptome Profiling ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Growth And Survival ◽  
Cancer Genome Atlas ◽  
Lung Adenocarcinomas ◽  
Genome Atlas

Abstract Background The LKB1 tumor suppressor gene is commonly inactivated in non-small cell lung carcinomas (NSCLC), a major form of lung cancer. Targeted therapies for LKB1-inactivated lung cancer are currently unavailable. Identification of critical signaling components downstream of LKB1 inactivation has the potential to uncover rational therapeutic targets. Here we investigated the role of INSL4, a member of the insulin/IGF/relaxin superfamily, in LKB1-inactivated NSCLCs. Methods INSL4 expression was analyzed using global transcriptome profiling, quantitative reverse transcription PCR, western blotting, enzyme-linked immunosorbent assay, and RNA in situ hybridization in human NSCLC cell lines and tumor specimens. INSL4 gene expression and clinical data from The Cancer Genome Atlas lung adenocarcinomas (n = 515) were analyzed using log-rank and Fisher exact tests. INSL4 functions were studied using short hairpin RNA (shRNA) knockdown, overexpression, transcriptome profiling, cell growth, and survival assays in vitro and in vivo. All statistical tests were two-sided. Results INSL4 was identified as a novel downstream target of LKB1 deficiency and its expression was induced through aberrant CRTC-CREB activation. INSL4 was highly induced in LKB1-deficient NSCLC cells (up to 543-fold) and 9 of 41 primary tumors, although undetectable in all normal tissues except the placenta. Lung adenocarcinomas from The Cancer Genome Atlas with high and low INSL4 expression (with the top 10th percentile as cutoff) showed statistically significant differences for advanced tumor stage (P < .001), lymph node metastasis (P = .001), and tumor size (P = .01). The INSL4-high group showed worse survival than the INSL4-low group (P < .001). Sustained INSL4 expression was required for the growth and viability of LKB1-inactivated NSCLC cells in vitro and in a mouse xenograft model (n = 5 mice per group). Expression profiling revealed INSL4 as a critical regulator of cell cycle, growth, and survival. Conclusions LKB1 deficiency induces an autocrine INSL4 signaling that critically supports the growth and survival of lung cancer cells. Therefore, aberrant INSL4 signaling is a promising therapeutic target for LKB1-deficient lung cancers.

scholarly journals Asians and Other Races Express Similar Levels of and Share the Same Genetic Polymorphisms of the SARS-CoV-2 Cell-Entry Receptor

2020 ◽  
Author(s):  
Ying Chen ◽  
Kejia Shan ◽  
Wenfeng Qian
Keyword(s):  
Lung Cancer ◽  
Genetic Polymorphisms ◽  
Cancer Genome ◽  
Cell Entry ◽  
The Cancer Genome Atlas ◽  
Transcriptome Data ◽  
Personal Protection ◽  
Cancer Genome Atlas ◽  
Entry Receptor ◽  
Genome Atlas

The recurrent coronavirus outbreaks in China (SARS-CoV and its relative, SARS-CoV-2) raise the possibility that Asians are more susceptible to coronavirus. Here, we test this possibility with the lung expression of ACE2, which encodes the cell-entry receptor of both SARS-CoV and SARS-CoV-2. We show that ACE2 expression is not affected during tumorigenesis, suggesting that the transcriptome data from the more than 1000 lung cancer samples in The Cancer Genome Atlas (TCGA) can be used to study ACE2 expression among people without cancer. The expression of ACE2 increases with age, but is not associated with sex. Asians show a similar ACE2 expression to other races. Furthermore, the frequencies of ACE2 alleles in Asians are not significantly deviated from those in other races. These observations indicate that individuals of all races need the same level of personal protection against SARS-CoV-2.

Causal inferencing of -omics data from The Cancer Genome Atlas: Lung adenocarcinoma tumors for mechanistic disease characterization and feature engineering.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21016-e21016
Author(s):  
Nimisha Schneider ◽  
Sergey Korkhov ◽  
Alexis Foroozan ◽  
Scott Marshall ◽  
Renee Deehan
Keyword(s):  
Lung Cancer ◽  
Stem Cell ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Tumor Stem Cell ◽  
Complex Data ◽  
Omics Data ◽  
Oncology Research ◽  
Cancer Genome Atlas ◽  
Genome Atlas

e21016 Background: Advances in high throughput measurement technologies (-omics data) have made it possible to generate high complexity, high volume data for oncology research. Researchers are often confronted many more measurements than samples (p > > > n), which poses challenges for both modeling the complexity of disease at the molecular mechanism level, and overfitting when generating predictive models with complex data. Here, we applied a prior knowledge-driven approach to characterize and classify heavy versus light smokers with lung cancer from The Cancer Genome Atlas, an open source repository that catalogs, harmonizes and hosts -omics data collected from samples generously donated from cancer patients. Methods: We applied a reverse inferencing approach to systematically interrogate RNAseq measurements from tumor and control biopsies against a knowledgebase of directed gene networks curated from published experiments. If patterns observed in the data are significantly similar to those in a network, an inference about the directional activity of that network can be made; e.g., the increased transcriptional activity of NFKB. Our library was nucleated through an open sourced knowledge graph and enhanced with updated and relevant knowledge using the open sourced Biological Expression Language framework. Directed networks were either qualitatively scored and used to build disease models, or semi-quantitatively scored and used as classification features. Results: In LUAD tumors, we detected a pattern of gene signatures which indicated a tumor stem cell-like phenotype characterized by predicted decreases in the activity of pro-differentiation factors and an increased response to hypoxia. Analysis of patients with heavy ( > 40) versus light ( < 10) pack-year burden suggested an augmented dedifferentiation profile in heavy smokers. In this example, improved classification was observed through features compression through directed network scoring compared to using individual RNA measurements selected by filtration methods. Conclusions: In-silico analysis of lung cancer patient biopsies generated hypotheses implicating stem cell signaling in tumors, and a further stratification of this signal based on patient pack year burden. Mechanistic modeling may be a useful application to the overfitting problem often encountered with -omics data in translational studies. Data from other TCGA indications can be used to evaluate the consistency of this type of approach

scholarly journals The impact of the Cancer Genome Atlas on lung cancer

2015 ◽  
Vol 166 (6) ◽  
pp. 568-585 ◽  
Author(s):  
Jeremy T.-H. Chang ◽  
Yee Ming Lee ◽  
R. Stephanie Huang
Keyword(s):  
Lung Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
The Impact ◽  
Genome Atlas

scholarly journals Exceptional Chemotherapy Response in Metastatic Colorectal Cancer Associated With Hyper-Indel–Hypermutated Cancer Genome and Comutation of POLD1 and MLH1

2017 ◽  
pp. 1-12
Author(s):  
Manish R. Sharma ◽  
James T. Auman ◽  
Nirali M. Patel ◽  
Juneko E. Grilley-Olson ◽  
Xiaobei Zhao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Somatic Mutation ◽  
Mutation Rate ◽  
Germ Line ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Response To Chemotherapy ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Purpose A 73-year-old woman with metastatic colon cancer experienced a complete response to chemotherapy with dose-intensified irinotecan that has been durable for 5 years. We sequenced her tumor and germ line DNA and looked for similar patterns in publicly available genomic data from patients with colorectal cancer. Patients and Methods Tumor DNA was obtained from a biopsy before therapy, and germ line DNA was obtained from blood. Tumor and germline DNA were sequenced using a commercial panel with approximately 250 genes. Whole-genome amplification and exome sequencing were performed for POLE and POLD1. A POLD1 mutation was confirmed by Sanger sequencing. The somatic mutation and clinical annotation data files from the colon (n = 461) and rectal (n = 171) adenocarcinoma data sets were downloaded from The Cancer Genome Atlas data portal and analyzed for patterns of mutations and clinical outcomes in patients with POLE- and/or POLD1-mutated tumors. Results The pattern of alterations included APC biallelic inactivation and microsatellite instability high (MSI-H) phenotype, with somatic inactivation of MLH1 and hypermutation (estimated mutation rate > 200 per megabase). The extremely high mutation rate led us to investigate additional mechanisms for hypermutation, including loss of function of POLE. POLE was unaltered, but a related gene not typically associated with somatic mutation in colon cancer, POLD1, had a somatic mutation c.2171G>A [p.Gly724Glu]. Additionally, we noted that the high mutation rate was largely composed of dinucleotide deletions. A similar pattern of hypermutation (dinucleotide deletions, POLD1 mutations, MSI-H) was found in tumors from The Cancer Genome Atlas. Conclusion POLD1 mutation with associated MSI-H and hyper-indel–hypermutated cancer genome characterizes a previously unrecognized variant of colon cancer that was found in this patient with an exceptional response to chemotherapy.

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