scholarly journals A Prognostic Signature Constructed by CTHRC1 and LRFN4 in Stomach Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Songling Han ◽  
Wei Zhu ◽  
Weili Yang ◽  
Qijie Guan ◽  
Chao Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Real Time ◽  
Real Time Pcr ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Quantitative Real Time Pcr ◽  
Stomach Adenocarcinoma ◽  
Geo Database

BackgroundStomach adenocarcinoma (STAD) is the most common histological type of stomach cancer, which causes a considerable number of deaths worldwide. This study aimed to identify its potential biomarkers with the notion of revealing the underlying molecular mechanisms.MethodsGene expression profile microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The “limma” R package was used to screen the differentially expressed genes (DEGs) between STAD and matched normal tissues. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for function enrichment analyses of DEGs. The STAD dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another STAD dataset from the GEO database. CIBERSORT algorithm was used to characterize the 22 human immune cell compositions. The expression of LRFN4 and CTHRC1 in tissues was determined by quantitative real-time PCR from the patients recruited to the present study.ResultsThree public datasets including 90 STAD patients and 43 healthy controls were analyzed, from which 44 genes were differentially expressed in all three datasets. These genes were implicated in biological processes including cell adhesion, wound healing, and extracellular matrix organization. Five out of 44 genes showed significant survival differences. Among them, CTHRC1 and LRFN4 were selected for construction of prognostic signature by univariate Cox regression and stepwise multivariate Cox regression in the TCGA-STAD dataset. The fidelity of the signature was evaluated in another independent dataset and showed a good classification effect. The infiltration levels of multiple immune cells between high-risk and low-risk groups had significant differences, as well as two immune checkpoints. TIM-3 and PD-L2 were highly correlated with the risk score. Multiple signaling pathways differed between the two groups of patients. At the same time, the expression level of LRFN4 and CTHRC1 in tissues analyzed by quantitative real-time PCR were consistent with the in silico findings.ConclusionThe present study constructed the prognostic signature by expression of CTHRC1 and LRFN4 for the first time via comprehensive bioinformatics analysis, which provided the potential therapeutic targets of STAD for clinical treatment.

scholarly journals A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zaisheng Ye ◽  
Miao Zheng ◽  
Yi Zeng ◽  
Shenghong Wei ◽  
He Huang ◽  
...  
Keyword(s):  
Risk Score ◽  
Molecular Mechanisms ◽  
Cancer Metabolism ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma ◽  
Prognostic Prediction

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.

scholarly journals Selection and validation of reference genes for gene expression studies in Klebsiella pneumoniae using Reverse Transcription Quantitative real-time PCR

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Ana Érika Inácio Gomes ◽  
Leonardo Prado Stuchi ◽  
Nathália Maria Gonçalves Siqueira ◽  
João Batista Henrique ◽  
Renato Vicentini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Klebsiella Pneumoniae ◽  
Real Time ◽  
Reverse Transcription ◽  
Real Time Pcr ◽  
Reference Genes ◽  
Quantitative Real Time Pcr ◽  
Expression Studies ◽  
Gene Expression Studies

scholarly journals Selecting and validating reference genes for quantitative real-time PCR in Plutella xylostella (L.)

Genome ◽  
2018 ◽  
Vol 61 (5) ◽  
pp. 349-358 ◽  
Author(s):  
Yanchun You ◽  
Miao Xie ◽  
Liette Vasseur ◽  
Minsheng You
Keyword(s):  
Gene Expression ◽  
Real Time ◽  
Plutella Xylostella ◽  
Real Time Pcr ◽  
Reference Genes ◽  
Developmental Stages ◽  
Quantitative Real Time Pcr ◽  
Experimental Conditions ◽  
Expression Studies ◽  
Gene Expression Studies

Gene expression analysis provides important clues regarding gene functions, and quantitative real-time PCR (qRT-PCR) is a widely used method in gene expression studies. Reference genes are essential for normalizing and accurately assessing gene expression. In the present study, 16 candidate reference genes (ACTB, CyPA, EF1-α, GAPDH, HSP90, NDPk, RPL13a, RPL18, RPL19, RPL32, RPL4, RPL8, RPS13, RPS4, α-TUB, and β-TUB) from Plutella xylostella were selected to evaluate gene expression stability across different experimental conditions using five statistical algorithms (geNorm, NormFinder, Delta Ct, BestKeeper, and RefFinder). The results suggest that different reference genes or combinations of reference genes are suitable for normalization in gene expression studies of P. xylostella according to the different developmental stages, strains, tissues, and insecticide treatments. Based on the given experimental sets, the most stable reference genes were RPS4 across different developmental stages, RPL8 across different strains and tissues, and EF1-α across different insecticide treatments. A comprehensive and systematic assessment of potential reference genes for gene expression normalization is essential for post-genomic functional research in P. xylostella, a notorious pest with worldwide distribution and a high capacity to adapt and develop resistance to insecticides.

Discovery of candidate gene expression signatures in peripheral blood for the screening of cervical cancer

2020 ◽  
Vol 14 (2) ◽  
pp. 109-118 ◽  
Author(s):  
Qiuling Ma ◽  
Yong Shao ◽  
Wei Chen ◽  
Cheng Quan ◽  
Yanhui Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Real Time ◽  
Real Time Pcr ◽  
Peripheral Blood ◽  
Area Under The Curve ◽  
Intraepithelial Neoplasia ◽  
Sequencing Analysis ◽  
Quantitative Real Time Pcr ◽  
Cervical Lesions

Aim: To investigate whether cervical cancer (CC) and cervical intraepithelial neoplasia (CIN) can be screened by analyzing gene expression profiling of peripheral blood. Methods: RNA-sequencing analysis of blood was performed on 11 CC patients, 21 CIN patients and 19 healthy controls (H). Fifty-nine genes were validated by quantitative real-time PCR using blood samples from 46 H, 83 CC and 32 CIN patients. Results: There were significant differences in the expression levels of six genes between CC and H, five genes between CIN and H and four genes between CC and CIN (p < 0.05). Four genes discriminated cervical lesions from H with a sensitivity of 82.61%, a specificity of 87.83% and an area under the curve of 0.8981. Three genes discriminated CC from CIN with a sensitivity of 53.13%, a specificity of 96.39% and an area under the curve of 0.7786. Conclusion: Our findings provided a promising noninvasive quantitative real-time PCR diagnostic assay of CC and CIN.

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