scholarly journals Leptin, BMI, and a Metabolic Gene Expression Signature Associated with Clinical Outcome to VEGF Inhibition in Colorectal Cancer

2016 ◽  
Vol 23 (1) ◽  
pp. 77-93 ◽  
Author(s):  
Aurélien J.C. Pommier ◽  
Matthew Farren ◽  
Bhavika Patel ◽  
Mark Wappett ◽  
Filippos Michopoulos ◽  
...  
2019 ◽  
Vol 15 (3) ◽  
pp. e1006832 ◽  
Author(s):  
Bernardo P. de Almeida ◽  
André F. Vieira ◽  
Joana Paredes ◽  
Mónica Bettencourt-Dias ◽  
Nuno L. Barbosa-Morais

Gut ◽  
2011 ◽  
Vol 61 (9) ◽  
pp. 1291-1298 ◽  
Author(s):  
Sang Cheul Oh ◽  
Yun-Yong Park ◽  
Eun Sung Park ◽  
Jae Yun Lim ◽  
Soo Mi Kim ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Wen‐Jing Yang ◽  
Hai‐Bo Wang ◽  
Wen‐Da Wang ◽  
Peng‐Yu Bai ◽  
Hong‐Xia Lu ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Anna Pačínková ◽  
Vlad Popovici

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.


2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Nurul Ainin Abdul Aziz ◽  
Norfilza M. Mokhtar ◽  
Roslan Harun ◽  
Md Manir Hossain Mollah ◽  
Isa Mohamed Rose ◽  
...  

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