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Zhou DL, Liu Q, Xu BH, Li Y, Su X, Ye ZL, Zhang X, Peng JL, Deng L, Tang T, Shao Q, Ma JJ, Yang XH, He CY. lncRNA GAS8-AS1 genetic alterations in papillary thyroid carcinoma and their clinical significance. Cancer Biomark 2021; 29:255-264. [PMID: 32675393 DOI: 10.3233/cbm-191071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The long non-coding RNA (lncRNA) GAS8-AS1 is the second-most frequently altered gene, following the BRAF gene, in papillary thyroid carcinoma (PTC). We aimed to study the specificity and significance of genetic alterations in GAS8-AS1 in PTC. In this study, we reported the prevalence of genetic alterations of GAS8-AS1 in tissues of 48 nodular goiter, 573 papillary thyroid cancer, 95 colorectal cancer, 101 non-small cell lung cancer, 92 glioma, and 69 gastrointestinal stromal tumor patients, and in peripheral white blood cells of 286 healthy volunteers. We observed that the genomic sequence of GAS8-AS1 had a high frequency of genetic alterations in addition to the previously reported c.713A>G/714T>C substitution. Substitution of c.713A>G was completely linked with four other loci at c.714T>C, c.728A>G, c.737G>A, and c.752G>A. Two novel substitutions at c.749G>A and c.826A>G were also found. Interestingly, evidence from different samples indicated that these variations were not unique variants for PTC; they were also found in other malignant tissues and white blood cells of healthy volunteers. The c.713A>G substitution was associated with the T stage of PTC, while c.749G>A was more likely to occur in younger patients with PTC. PTC patients carrying heterozygous variants at the c.749 and c.826 loci had a higher risk of developing multiple lesions. These associations were also observed in patients with PTC and concomitant benign thyroid disease. Notably, the rare homozygous GG at the c.826 site conferred a higher risk of developing T2 PTC without benign thyroid disease, and a lower risk of developing T2 PTC with benign thyroid disease. Alterations of c.749G>A and c.826A>G had higher levels of serum TSH (thyroid stimulating hormone) in PTC subjects. Our study provides evidence that the detection of GAS8-AS1 genetic alterations would be useful in diagnostic screening and prognostic assessment of PTC.
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Affiliation(s)
- Da-Lei Zhou
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.,Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Qing Liu
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.,Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Bo-Heng Xu
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.,Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yue Li
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xuan Su
- Department of Head and Neck, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Zu-Lu Ye
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xiao Zhang
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jun-Ling Peng
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Ling Deng
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Tao Tang
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Qiong Shao
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jiang-Jun Ma
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xin-Hua Yang
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Cai-Yun He
- Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
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Brhane Y, Yang P, Christiani DC, Liu G, McLaughlin JR, Brennan P, Shete S, Field JK, Tardón A, Kohno T, Shiraishi K, Matsuo K, Bossé Y, Amos CI, Hung RJ. Genetic Determinants of Lung Cancer Prognosis in Never Smokers: A Pooled Analysis in the International Lung Cancer Consortium. Cancer Epidemiol Biomarkers Prev 2020; 29:1983-1992. [PMID: 32699080 PMCID: PMC7541720 DOI: 10.1158/1055-9965.epi-20-0248] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 05/12/2020] [Accepted: 07/15/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Lung cancer remains the leading cause of cancer death worldwide, with 15% to 20% occurring in never smokers. To assess genetic determinants for prognosis among never smokers, we conducted a genome-wide investigation in the International Lung Cancer Consortium (ILCCO). METHODS Genomic and clinical data from 1,569 never-smoking patients with lung cancer of European ancestry from 10 ILCCO studies were included. HRs and 95% confidence intervals of overall survival were estimated. We assessed whether the associations were mediated through mRNA expression-based 1,553 normal lung tissues from the lung expression quantitative trait loci (eQTL) dataset and Genotype-Tissue Expression (GTEx). For cross-ethnicity generalization, we assessed the associations in a Japanese study (N = 887). RESULTS One locus at 13q22.2 was associated with lung adenocarcinoma survival at genome-wide level, with carriers of rs12875562-T allele exhibiting poor prognosis [HR = 1.71 (1.41-2.07), P = 3.60 × 10-8], and altered mRNA expression of LMO7DN in lung tissue (GTEx, P = 9.40 × 10-7; Lung eQTL dataset, P = 0.003). Furthermore, 2 of 11 independent loci that reached the suggestive significance level (P < 10-6) were significant eQTL affecting mRNA expression of nearby genes in lung tissues, including CAPZB at 1p36.13 and UBAC1 at 9q34.3. One locus encoding NWD2/KIAA1239 at 4p14 showed associations in both European [HR = 0.50 (0.38-0.66), P = 6.92 × 10-7] and Japanese populations [HR = 0.79 (0.67-0.94), P = 0.007]. CONCLUSIONS Based on the largest genomic investigation on the lung cancer prognosis of never smokers to date, we observed that lung cancer prognosis is affected by inherited genetic variants. IMPACT We identified one locus near LMO7DN at genome-wide level and several potential prognostic genes with cis-effect on mRNA expression. Further functional genomics work is required to understand their role in tumor progression.
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Affiliation(s)
- Yonathan Brhane
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | | | | | - Geoffrey Liu
- Princess Margaret Cancer Centre, Toronto, Canada
| | - John R McLaughlin
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Sanjay Shete
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Institute of Translational Medicine, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Adonina Tardón
- University of Oviedo, ISPA and CIBERESP, Faculty of Medicine, Campus del Cristo, Oviedo, Spain
| | - Takashi Kohno
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Kouya Shiraishi
- Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Department of Preventive Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Rayjean J Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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Dehghan-Nayeri N, Eshghi P, Pour KG, Rezaei-Tavirani M, Omrani MD, Gharehbaghian A. Differential expression pattern of protein markers for predicting chemosensitivity of dexamethasone-based chemotherapy of B cell acute lymphoblastic leukemia. Cancer Chemother Pharmacol 2017; 80:177-185. [PMID: 28585036 DOI: 10.1007/s00280-017-3347-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 05/29/2017] [Indexed: 02/06/2023]
Abstract
Dexamethasone is considered as a direct chemotherapeutic agent in the treatment of pediatric acute lymphoblastic leukemia (ALL). Beside the advantages of the drug, some problems arising from the dose-related side effects are challenging issues during the treatment. Accordingly, the classification of patients to dexamethasone sensitive and resistance groups can help to select optimizing the therapeutic dose with the lowest adverse effects particularly in sensitive cases. For this purpose, we investigated inhibited proliferation and induced cytotoxicity in NALM-6 cells, as sensitive cells, after dexamethasone treatment. In addition, comparative protein expression analysis using the 2DE-MALDI-TOF MS technique was performed to identify the specific altered proteins. In addition, we evaluated mRNA expression levels of the identified proteins in bone-marrow samples from pediatric ALL patients using the real-time q-PCR method. Eventually, proteomic analysis revealed a combination of biomarkers, including capping proteins (CAPZA1 and CAPZB), chloride channel (CLIC1), purine nucleoside phosphorylase (PNP), and proteasome activator (PSME1), in response to the dexamethasone treatment. In addition, our results indicated low expression of identified proteins at both the mRNA and protein expression levels after drug treatment. Moreover, quantitative real-time PCR data analysis indicated that independent of the molecular subtypes of the leukemia, CAPZA1, CAPZB, CLIC1, and PNP expression levels were lower in ALL samples than normal samples, although PSME1 expression level was higher in ALL samples than normal samples. Furthermore, the expression level of all proteins (except PSME1) was different between high-risk and standard-risk patients that suggesting the prognostic value of them. In conclusion, our study suggests a panel of biomarkers comprising CAPZA1, CAPZB, CLIC1, PNP, and PSME1 as early diagnosis and treatment evaluation markers that may differentiate cancer cells which are presumably to benefit from dexamethasone-based chemotherapy and may facilitate the prediction of clinical outcome.
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MESH Headings
- Antineoplastic Agents, Hormonal/administration & dosage
- Antineoplastic Agents, Hormonal/pharmacology
- Biomarkers, Tumor/metabolism
- Cell Line, Tumor
- Cell Proliferation/drug effects
- Child
- Child, Preschool
- Dexamethasone/administration & dosage
- Dexamethasone/pharmacology
- Drug Resistance, Neoplasm
- Female
- Gene Expression Regulation, Neoplastic
- Humans
- Infant
- Male
- Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/drug therapy
- Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/genetics
- Precursor B-Cell Lymphoblastic Leukemia-Lymphoma/pathology
- Prognosis
- Proteomics
- RNA, Messenger/metabolism
- Real-Time Polymerase Chain Reaction
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
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Affiliation(s)
- Nasrin Dehghan-Nayeri
- Proteomics Research Center, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Peyman Eshghi
- Pediatric Congenital Hematologic Disorders Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kourosh Goudarzi Pour
- Pediatric Congenital Hematologic Disorders Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Rezaei-Tavirani
- Proteomics Research Center, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mir Davood Omrani
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Gharehbaghian
- Pediatric Congenital Hematologic Disorders Research Center, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Department of Hematology and Blood Bank, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Abstract
Thyroid hormones (TH) are endocrine messengers essential for normal development and function of virtually every vertebrate. The hypothalamic-pituitary-thyroid axis is exquisitely modulated to maintain nearly constant TH (T4 and T3) levels in circulation. However peripheral tissues and the CNS control the intracellular availability of TH, suggesting that circulating concentrations of TH are not fully representative of what each cell type sees. Indeed, recent work in the field has identified that TH transporters, deiodinases and thyroid hormone receptor coregulators can strongly control tissue-specific sensitivity to a set amount of TH. Furthermore, the mechanism by which the thyroid hormone receptors regulate target gene expression can vary by gene, tissue and cellular context. This review will highlight novel insights into the machinery that controls the cellular response to TH, which include unique signaling cascades. These findings shed new light into the pathophysiology of human diseases caused by abnormal TH signaling.
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Affiliation(s)
- Arturo Mendoza
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Anthony N Hollenberg
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
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Beebe K, Kennedy AD. Sharpening Precision Medicine by a Thorough Interrogation of Metabolic Individuality. Comput Struct Biotechnol J 2016; 14:97-105. [PMID: 26929792 PMCID: PMC4744241 DOI: 10.1016/j.csbj.2016.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 01/07/2016] [Accepted: 01/10/2016] [Indexed: 12/24/2022] Open
Abstract
Precision medicine is an active component of medical practice today, but aspirations are to both broaden its reach to a greater diversity of individuals and improve its “precision” by enhancing the ability to define even more disease states in combination with associated treatments. Given complexity of human phenotypes, much work is required. In this review, we deconstruct this challenge at a high level to define what is needed to move closer toward these aspirations. In the context of the variables that influence the diverse array of phenotypes across human health and disease – genetics, epigenetics, environmental influences, and the microbiome – we detail the factors behind why an individual's biochemical (metabolite) composition is increasingly regarded as a key element to precisely defining phenotypes. Although an individual's biochemical (metabolite) composition is generally regarded, and frequently shown, to be a surrogate to the phenotypic state, we review how metabolites (and therefore an individual's metabolic profile) are also functionally related to the myriad of phenotypic influencers like genetics and the microbiota. We describe how using the technology to comprehensively measure an individual's biochemical profile – metabolomics – is integrative to defining individual phenotypes and how it is currently being deployed in efforts to continue to elaborate on human health and disease in large population studies. Finally, we summarize instances where metabolomics is being used to assess individual health in instances where signatures (i.e. biomarkers) have been defined. Untargeted biochemical profiling has the potential to phenotype individuals where genetic associations do not seem to show penetrance Metabolomics can be leveraged with other ‘omics data to discern phenotype information that is driven by environmental, microbiota, or epigenetic factors. Tracking the biochemical profile of individuals may help discern effectiveness or response to treatment or disease progression.
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