RPS13, a potential universal reference gene for normalisation of gene expression in multiple human normal and cancer tissue samples

AimsHistopathological tissue samples are being increasingly used as sources of nucleic acids in molecular pathology translational research. This study investigated the suitability of glioblastoma and control central nervous system (CNS) formalin-fixed paraffin embedded (FFPE) tissue-derived RNA for gene expression analyses.MethodsTotal RNA was extracted from control (temporal lobe resection tissue) and glioblastoma FFPE tissue samples. RNA purity (260/280 ratios) was determined and RNA integrity number (RIN) analysis was performed. RNA was subsequently used for RT-qPCR for two reference genes,18SandGAPDH.ResultsReference gene expression was equivalent between control and glioblastoma tissue when using RNA extracted from FFPE tissue, which has key implications for biological normalisation for CNS gene expression studies. There was a significant difference between the mean RIN values of control and glioblastoma FFPE tissue. There was no significant correlation between 260/280 or RIN values versus total RNA yield. The age of the tissue blocks did not influence RNA yield, fragmentation or purity. There was no significant correlation between RIN or 260/280 ratios and mean qPCR cycle threshold for either reference gene.ConclusionsThis study showed that routinely available CNS FFPE tissue is suitable for RNA extraction and downstream gene expression studies, even after 60 months of storage. Substantial RNA fragmentation associated with glioblastoma and control FFPE tissue blocks did not preclude downstream RT-qPCR gene expression analyses. Cross validation with both archival and prospectively collated FFPE specimens is required to further demonstrate that CNS tissue blocks can be used in novel translational molecular biomarker studies.

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Evaluation of GNMT Gene Expression in Prostate Cancer Tissues using Real-Time PCR

The Journal of Tolooebehdasht ◽

10.18502/tbj.v19i5.5175 ◽

2021 ◽

Author(s):

Niloofar Dehghani ◽

Masoud Salehipour ◽

Babak Javanmard

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Benign Prostatic Hyperplasia ◽

Real Time ◽

Real Time Pcr ◽

Prostatic Hyperplasia ◽

Expression Level ◽

P Value ◽

Cancer Tissue ◽

Tissue Samples

Introduction: Prostate cancer is the second most common cancer and the leading cause of cancer-related deaths worldwide. In the present study, the expression level of glycine N-methyl transferase gene (GNMT) was investigated in prostate cancer tissue. The GNMT enzyme is encoded by the GNMT gene. Increased GNMT gene expression increases the conversion of glycine to sarcosine and results in the elevated levels of sarcosine in blood and urine. Methods: The expression level of GNMT gene in tissue samples of patients with prostate cancer was compared with those with benign prostatic hyperplasia using Real-Time PCR technique. Results: The GNMT gene expression level increased significantly in prostate cancer patients compared with those with benign prostatic hyperplasia (p-value <0.001). In addition, the expression level of GNMT gene was stage-dependent and significant increases were observed in all stages of prostate cancer compared with those with benign prostatic hyperplasia (p-value <0.001). Conclusion: The concentration of sarcosine is controlled by GNMT and it seems that increasing the expression level of GNMT gene increases the level of sarcosine concentration. Thus, it appears that increased levels of GNMT expression occur in the early stages of prostate cancer. Therefore, periodic measurement of GNMT expression levels can detect prostate cancer before it forms a cancer cell and invades other tissues.

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Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.633415 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chen Xue ◽

Yalei Zhao ◽

Ganglei Li ◽

Lanjuan Li

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Cancer Tissue ◽

Hub Genes ◽

Specific Expression ◽

Tissue Samples ◽

Expression Levels ◽

Advanced Tumor

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

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Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

10.21203/rs.2.11118/v2 ◽

2019 ◽

Author(s):

Jiasheng Xu ◽

Kaili Liao ◽

ZHONGHUA FU ◽

ZHENFANG XIONG

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Gene Ontology ◽

Differentially Expressed Genes ◽

Microarray Analysis ◽

Differentially Expressed ◽

Cancer Tissue ◽

Pancreatic Cancer Tissue ◽

Tissue Samples ◽

Multiple Difference

Abstract Objective: To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University from March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results: Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients; while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene showed the highest increase in expression level (multiple difference: 31.76). The pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene microarray analysis. Conclusions: The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

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Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

10.21203/rs.2.11118/v1 ◽

2019 ◽

Author(s):

Jiasheng Xu ◽

Kaili Liao ◽

ZHONGHUA FU ◽

ZHENFANG XIONG

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Gene Ontology ◽

Differentially Expressed Genes ◽

Pathway Analysis ◽

Differentially Expressed ◽

Cancer Tissue ◽

Pancreatic Cancer Tissue ◽

Tissue Samples ◽

Multiple Difference

Abstract Objective To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University during March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients;while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene had the highest multiple of differential expression (difference multiple: 31.76). The Pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the highest and COL11A1 gene had the highest multiple difference (multiple difference: 5.02). The expressions of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene chip analysis. Conclusions The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples compared between Mongolian and Han populations. These genes are closely related to the proliferation, differentiation, invasion and metastasis and multi-drug resistance of pancreatic cancer and are involved in the regulation of multiple important signaling pathways in organisms.

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Validation of internal reference genes for relative quantitation studies of gene expression in human laryngeal cancer

PeerJ ◽

10.7717/peerj.2763 ◽

2016 ◽

Vol 4 ◽

pp. e2763 ◽

Cited By ~ 3

Author(s):

Xiaofeng Wang ◽

Jinting He ◽

Wei Wang ◽

Ming Ren ◽

Sujie Gao ◽

...

Keyword(s):

Gene Expression ◽

Reference Gene ◽

Laryngeal Cancer ◽

Reference Genes ◽

Target Gene ◽

Cancer Tissue ◽

Internal Reference ◽

Target Gene Expression ◽

Internal Reference Gene ◽

Relative Quantitation

BackgroundThe aim of this study was to determine the expression stabilities of 12 common internal reference genes for the relative quantitation analysis of target gene expression performed by reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) in human laryngeal cancer.MethodsHep-2 cells and 14 laryngeal cancer tissue samples were investigated. The expression characteristics of 12 internal reference gene candidates (18S rRNA, GAPDH, ACTB, HPRT1, RPL29, HMBS, PPIA, ALAS1, TBP, PUM1, GUSB, and B2M) were assessed by RT-qPCR. The data were analyzed by three commonly used software programs: geNorm, NormFinder, and BestKeeper.ResultsThe use of the combination of four internal reference genes was more appropriate than the use of a single internal reference gene. The optimal combination was PPIA + GUSB + RPL29 + HPRT1 for both the cell line and tissues; while the most appropriate combination was GUSB + RPL29 + HPRT1 + HMBS for the tissues.ConclusionsOur recommended internal reference genes may improve the accuracy of relative quantitation analysis of target gene expression performed by the RT-qPCR method in further gene expression research on laryngeal tumors.

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Screening differentially expressed genes of pancreatic cancer between Mongolian and Han people using bioinformatics technology

10.21203/rs.2.11118/v4 ◽

2020 ◽

Author(s):

Jiasheng Xu ◽

Kaili Liao ◽

Zhonghua Fu ◽

Zhenfang Xiong

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Gene Ontology ◽

Differentially Expressed Genes ◽

Microarray Analysis ◽

Differentially Expressed ◽

Cancer Tissue ◽

Pancreatic Cancer Tissue ◽

Tissue Samples ◽

Multiple Difference

Abstract Objective: To screen and analyze differentially expressed genes in pancreatic carcinoma tissues taken from Mongolian and Han patients by Affymetrix Genechip. Methods: Pancreatic ductal cell carcinoma tissues were collected from the Mongolian and Han patients undergoing resection in the Second Affiliated Hospital of Nanchang University from March 2015 to May 2018 and the total RNA was extracted. Differentially expressed genes were selected from the total RNA qualified by Nanodrop 2000 and Agilent 2100 using Affymetrix and a cartogram was drawn; The gene ontology (GO) analysis and Pathway analysis were used for the collection and analysis of biological information of these differentially expressed genes. Finally, some differentially expressed genes were verified by real-time PCR. Results: Through the microarray analysis of gene expression, 970 differentially expressed genes were detected by comparing pancreatic cancer tissue samples between Mongolian and Han patients. A total of 257 genes were significantly up-regulated in pancreatic cancer tissue samples in Mongolian patients; while a total of 713 genes were down-regulated. In the Gene Ontology database, 815 differentially expressed genes were identified with clear GO classification, and CPB1 gene showed the highest increase in expression level (multiple difference: 31.76). The pathway analysis detected 28 signaling pathways that included these differentially expressed genes, involving a total of 178 genes. Among these pathways, the enrichment of differentially expressed genes in the FAK signaling pathway was the strongest and COL11A1 gene showed the highest multiple difference (multiple difference: 5.02). The expression of differentially expressed genes CPB1, COL11A1、ITGA4、BIRC3、PAK4、CPA1、CLPS、PIK3CG and HLA-DPA1 determined by real-time PCR were consistent with the results of gene microarray analysis. Conclusions: The results of microarray analysis of gene expression profiles showed that there are a large number of differentially expressed genes in pancreatic cancer tissue samples comparing Mongolian and Han population. These genes are closely related to the cell proliferation, differentiation, invasion, metastasis and multi-drug resistance in pancreatic cancer. They are also involved in the regulation of multiple important signaling pathways in organisms.

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Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

10.21203/rs.3.rs-157951/v1 ◽

2021 ◽

Author(s):

Margarita Kirienko ◽

Martina Sollini ◽

Marinella Corbetta ◽

Emanuele Voulaz ◽

Noemi Gozzi ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Cancer Biology ◽

Area Under The Curve ◽

Standardized Uptake Value ◽

Gene Expression Signature ◽

Genomic Analysis ◽

Genomic Data ◽

Cancer Tissue ◽

Tissue Samples

Abstract Objectives The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F]-FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n=74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalized linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.Results Standardized Uptake Value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule in predicting the outcome resulted in an Area Under the Curve (AUC) of 0.87.Conclusions: Radiogenomic data provided clinically relevant information in NCSCL patients, regarding the histotype, aggressiveness, and progression. Gene expression may provide additional valuable information to guide patient management. The application of ML allows to increase the efficacy of radiogenomic analysis and provide novel insights into cancer biology.

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Study of KMT2B (MLL2) gene expression changes in patients with breast cancer

Breast Cancer Management ◽

10.2217/bmt-2018-0016 ◽

2019 ◽

Vol 8 (2) ◽

pp. BMT24

Author(s):

Mohammad Ghanbari ◽

Mohammadali Hosseinpour-Feizi ◽

Reza Safaralizadeh ◽

Aida Aghazadeh ◽

Vahid Montazeri

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Tumor Tissue ◽

Rna Extraction ◽

Cancer Tissue ◽

Cdna Synthesis ◽

Tissue Samples ◽

Spss Software ◽

Analyze Data ◽

Mll2 Gene

Aim: This study aimed to demonstrate misregulation of KMT2B gene expression in breast cancer tissue. Materials & methods: Cancerous and marginal tissue samples were collected from 43 female patients. After RNA extraction and cDNA synthesis, quantitative-PCR was used to evaluate the expression level of the KMT2B gene. REST, Sigma plot and SPSS software were used to analyze data. Results: KMT2B gene expression was significantly decreased in tumor tissue compared with marginal tissue (p = 0.02). No significant correlation was found between expression levels of KMT2B and clinical parameters of patients (p > 0.05) Conclusion: Our study demonstrated that downregulation of KMT2B is associated with breast cancer and its misregulation may play an important role in tumorigenesis.

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