scholarly journals Gene Expression Levels as Predictive Markers of Outcome in Pancreatic Cancer after Gemcitabine-Based Adjuvant Chemotherapy

Neoplasia ◽  
2010 ◽  
Vol 12 (10) ◽  
pp. 807-IN8 ◽  
Author(s):  
Hayato Fujita ◽  
Kenoki Ohuchida ◽  
Kazuhiro Mizumoto ◽  
Soichi Itaba ◽  
Tetsuhide Ito ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Adjuvant Chemotherapy ◽  
Predictive Markers ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals Clinical immunology Glutamine abolishes the TLR4 gene expression levels in pancreatic cancer patients: a preliminary study

2012 ◽  
Vol 4 ◽  
pp. 350-354
Author(s):  
Sylwia Kędziora ◽  
Robert Słotwiński ◽  
Aleksandra Dąbrowska ◽  
Gustaw Lech ◽  
Maciej Słodkowski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cancer Patients ◽  
Clinical Immunology ◽  
Expression Levels ◽  
Tlr4 Gene ◽  
Preliminary Study ◽  
Gene Expression Levels

VEGF and VEGFR1 gene expression levels and tumor recurrence in adjuvant colon cancer

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 4040-4040 ◽  
Author(s):  
Y. Ning ◽  
G. Lurje ◽  
K. Danenberg ◽  
J. Cooc ◽  
D. Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Tumor Recurrence ◽  
Stage Ii ◽  
Tissue Samples ◽  
Expression Levels ◽  
Stage Iii Colon Cancer ◽  
Gene Expression Levels

4040 Background: Tumor recurrence after curative resection is still a major problem in the management of adjuvant colon cancer, with recurrence rate approximately 30–40%. Identifying molecular markers for tumor recurrence is critical for successfully selecting patients who are more likely to benefit from adjuvant chemotherapy. Our group previously showed that angiogenesis gene polymorphisms (VEGF and IL-8) may associated with tumor recurrence in adjuvant colon cancer (Lurje Ann Oncol, 2008). Here we tested the hypothesis whether gene expression levels of angiogenesis pathway (COX-2, EGFR, VEGF, VEGFR1, VEGFR2 and IL-8) could also predict the risk of tumor recurrence in stage II and III colon cancer patients treated with adjuvant chemotherapy. Methods: Tissue samples from 140 adjuvant colon cancer patients (69 females and 71 males with a median age of 59 years; range=28–86) were available for gene expression assays. These tissue samples were obtained at the University of Southern California/Norris Comprehensive Cancer Center (USC/NCCC) and LAC+USC medical center between 1999 and 2006. Sixty-three patients had stage II and 77 had stage III colon cancer. The median follow-up was 5.4 years (range=2.0–16.8). 51 of 140 patients (36.4%) developed tumor recurrence with a 5-year probability of 0.28 ± 0.06 for stage II and 0.40 ± 0.06 for stage III colon cancer patients. mRNA was extracted from laser-capture-microdissected tumor tissue. After cDNA was prepared by reverse transcription, quantitation of the candidate genes and an internal reference gene (ß-actin) was performed using a fluorescence-based real-time detection method (TaqMan). Results: We found VEGF and VEGFR1 gene expression levels independently significantly associated with time to tumor recurrence in adjuvant colon cancer patients. Patients with lower VEGF gene expression and lower VEGFR1 gene expression levels had significantly longer time to tumor recurrence compared to those with higher VEGF and higher VEGFR1 gene expression levels (p<0.05, log-rank test). Conclusions: VEGF and VEGFR1 gene expression levels may predict tumor recurrence risk in adjuvant colon cancer patients. Our exploratory data warrant future confirmatory trial. [Table: see text]

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Genome-Wide Expression and Alternative Splicing in Domesticated Sunflowers (Helianthus annuus L.) under Flooding Stress

Agronomy ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 92
Author(s):  
Joon Seon Lee ◽  
Lexuan Gao ◽  
Laura Melissa Guzman ◽  
Loren H. Rieseberg
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Mixed Model ◽  
Agricultural Land ◽  
Alcohol Fermentation ◽  
Expression Levels ◽  
Flooding Stress ◽  
Genome Wide ◽  
And Control ◽  
Gene Expression Levels

Approximately 10% of agricultural land is subject to periodic flooding, which reduces the growth, survivorship, and yield of most crops, reinforcing the need to understand and enhance flooding resistance in our crops. Here, we generated RNA-Seq data from leaf and root tissue of domesticated sunflower to explore differences in gene expression and alternative splicing (AS) between a resistant and susceptible cultivar under both flooding and control conditions and at three time points. Using a combination of mixed model and gene co-expression analyses, we were able to separate general responses of sunflower to flooding stress from those that contribute to the greater tolerance of the resistant line. Both cultivars responded to flooding stress by upregulating expression levels of known submergence responsive genes, such as alcohol dehydrogenases, and slowing metabolism-related activities. Differential AS reinforced expression differences, with reduced AS frequencies typically observed for genes with upregulated expression. Significant differences were found between the genotypes, including earlier and stronger upregulation of the alcohol fermentation pathway and a more rapid return to pre-flooding gene expression levels in the resistant genotype. Our results show how changes in the timing of gene expression following both the induction of flooding and release from flooding stress contribute to increased flooding tolerance.

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