scholarly journals Differential Expression of miRNAs in Colorectal Cancer: Comparison of Paired Tumor Tissue and Adjacent Normal Mucosa Using High-Throughput Sequencing

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34150 ◽  
Author(s):  
Julian Hamfjord ◽  
Astrid M. Stangeland ◽  
Timothy Hughes ◽  
Martina L. Skrede ◽  
Kjell M. Tveit ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Differential Expression ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Tumor Tissue ◽  
Normal Mucosa ◽  
Adjacent Normal Mucosa

Reduced CDX2 Expression Predicts Poor Overall Survival in Patients with Colorectal Cancer

2013 ◽  
Vol 79 (4) ◽  
pp. 353-360 ◽  
Author(s):  
Kwang Dae Hong ◽  
Dooseok Lee ◽  
Youngseok Lee ◽  
Sun Il Lee ◽  
Hong Young Moon
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Western Blot ◽  
Tumor Tissue ◽  
Normal Mucosa ◽  
Poor Overall Survival ◽  
Ki 67 ◽  
Cdx2 Expression ◽  
Adjacent Normal Mucosa

The homeodomain transcription factor CDX2 directs development and maintenance of normal intestinal epithelium. However, the role of CDX2 in colorectal carcinogenesis is poorly understood. Hence, we investigated the CDX2 expression in patients with colorectal cancer and its relationship to tumor cell proliferation and differentiation and evaluated the role of this molecule as a biologic marker for the prediction of poor patient survival. We retrospectively reviewed 207 patients with colorectal cancer, with an available paraffin block, who underwent surgical resection between January 2002 and December 2004 at Korea University Guro Hospital. CDX2 expression was compared between tumor tissue and the adjacent normal mucosa using immunohistochemistry and Western blot analysis. Immunohistochemical staining for CDX2, Ki-67, and CK20 was performed in each tumor tissue. Immunohistochemistry revealed that CDX2 protein is overexpressed by colorectal cancer compared with adjacent normal mucosa (P < 0.001). In the Western blot analysis, tumor tissue showed a trend toward overexpression of CDX2 protein compared with normal mucosa (P = 0.09). CDX2 expression showed a significant direct correlation with the expression of Ki-67 and CK20 in tumor tissue (P = 0.028 and P = 0.042, respectively). Survival analysis showed that reduced CDX2 expression was statistically and significantly related to poor overall survival. Reduced CDX2 expression is associated with poor overall survival in patients with colorectal cancer and may be clinically useful as a marker for poor prognosis.

scholarly journals Association between colorectal cancer and Fusobacterium nucleatum and Bacteroides fragilis bacteria in Iranian patients: a preliminary study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Aref Shariati ◽  
Shabnam Razavi ◽  
Ehsanollah Ghaznavi-Rad ◽  
Behnaz Jahanbin ◽  
Abolfazl Akbari ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Relative Abundance ◽  
Normal Tissue ◽  
Bacteroides Fragilis ◽  
Fusobacterium Nucleatum ◽  
Normal Mucosa ◽  
Clinicopathologic Characteristics ◽  
Tissue Samples ◽  
Adjacent Normal Mucosa ◽  
Iranian Patients

Abstract Background and aim Recent studies have proposed that commensal bacteria might be involved in the development and progression of gastrointestinal disorders such as colorectal cancer (CRC). Therefore, in this study, the relative abundance of Fusobacterium nucleatum, Bacteroides fragilis, Streptococcus bovis/gallolyticus, and Enteropathogenic Escherichia coli (EPEC) in CRC tissues, and their association with clinicopathologic characteristics of CRC was investigated in Iranian patients. Moreover, the role of these bacteria in the CRC-associated mutations including PIK3CA, KRAS, and BRAF was studied. Method To these ends, the noted bacteria were quantified in paired tumors and normal tissue specimens of 30 CRC patients, by TaqMan quantitative Real-Time Polymerase Chain Reaction (qPCR). Next, possible correlations between clinicopathologic factors and mutations in PIK3CA, KRAS, and BRAF genes were analyzed. Results In studied samples, B. fragilis was the most abundant bacteria that was detected in 66 and 60% of paired tumor and normal samples, respectively. Furthermore, 15% of the B. fragilis-positive patients were infected with Enterotoxigenic B. fragilis (ETBF) in both adenocarcinoma and matched adjacent normal samples. F. nucleatum was also identified in 23% of tumors and 13% of adjacent normal tissue samples. Moreover, the relative abundance of these bacteria determined by 2-ΔCT was significantly higher in CRC samples than in adjacent normal mucosa (p < 0.05). On the other hand, our findings indicated that S. gallolyticus and EPEC, compared to adjacent normal mucosa, were not prevalent in CRC tissues. Finally, our results revealed a correlation between F. nucleatum-positive patients and the KRAS mutation (p = 0.02), while analyses did not show any association between bacteria and mutation in PIK3CA and BRAF genes. Conclusion The present study is the first report on the analysis of different bacteria in CRC tissue samples of Iranian patients. Our findings revealed that F. nucleatum and B. fragilis might be linked to CRC. However, any link between gut microbiome dysbiosis and CRC remains unknown.

scholarly journals A Comparison of Methods: Normalizing High-Throughput RNA Sequencing Data

2015 ◽  
Author(s):  
Rahul Reddy
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Differential Expression Analysis ◽  
Simulated Data ◽  
Sequencing Data ◽  
Technical Variability ◽  
Expression Studies ◽  
Normalization Methods

As RNA-Seq and other high-throughput sequencing grow in use and remain critical for gene expression studies, technical variability in counts data impedes studies of differential expression studies, data across samples and experiments, or reproducing results. Studies like Dillies et al. (2013) compare several between-lane normalization methods involving scaling factors, while Hansen et al. (2012) and Risso et al. (2014) propose methods that correct for sample-specific bias or use sets of control genes to isolate and remove technical variability. This paper evaluates four normalization methods in terms of reducing intra-group, technical variability and facilitating differential expression analysis or other research where the biological, inter-group variability is of interest. To this end, the four methods were evaluated in differential expression analysis between data from Pickrell et al. (2010) and Montgomery et al. (2010) and between simulated data modeled on these two datasets. Though the between-lane scaling factor methods perform worse on real data sets, they are much stronger for simulated data. We cannot reject the recommendation of Dillies et al. to use TMM and DESeq normalization, but further study of power to detect effects of different size under each normalization method is merited.

scholarly journals Impact of human gene annotations on RNA-seq differential expression analysis

2021 ◽  
Author(s):  
Yu Hamaguchi ◽  
Chao Zeng ◽  
Michiaki Hamada
Keyword(s):  
Differential Expression ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Human Gene ◽  
Gene Annotation ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Gene Annotations ◽  
Sequencing Technologies ◽  
The Impact

Abstract Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

scholarly journals MicroRNA-Related Prognosis Biomarkers from High-Throughput Sequencing Data of Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiao-Liang Xing ◽  
Zhi-Yong Yao ◽  
Ti Zhang ◽  
Ning Zhu ◽  
Yuan-Wu Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Differential Expression ◽  
High Throughput Sequencing ◽  
Survival Rates ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Differential Expression Genes ◽  
Cancer Genome Atlas

Background. Colorectal cancer (CRC) is the third most common cancer in the world, and most of them are adenocarcinomas. CRC could be classified as colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) according to the original tumorigenesis position. Increasing evidences indicated that microRNAs (miRNAs) play an important role in the occurrence of multiple tumors. Methods. In this study, we firstly downloaded miRNA (COAD, 8 controls vs. 455 tumors; READ, 3 controls vs. 161 tumors) and mRNA (COAD, 41 controls vs. 478 tumors; READ, 10 controls vs. 166 tumors) data from The Cancer Genome Atlas (TCGA) database and then used DESeq2, RegParallel, miRDB, TargetScanHuman 7.2, DAVID 6.8, STRING, and Cytoscape software to identify the potential prognosis biomarkers. Results. We identified 175 differential expression miRNAs (DEMs) and 3747 differential expression genes (DEGs) in COAD and 184 DEMs and 3928 DEGs in READ. And then, we obtained 21 (13 in COAD and 8 in READ) DEMs associated with the survival rates, which correlated with 440 (217 in COAD and 223 in READ) overlapping DEGs. Through survival analysis for those overlapping DEGs, we found 11 (8 in COAD and 3 in READ) overlapping DGEs associated with survival rates of patients, which were correlated with 9 (7 in COAD and 2 in READ) DEMs significantly. Conclusion. In this study, we found several candidate prognostic biomarkers which have been identified in various cancers and also found several new prognosis biomarkers of COAD and READ. In conclusion, this analysis based on theoretical knowledge and clinical outcomes we have done needs further confirmation by more researches.

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