Predictive biomarkers of colorectal cancer

Ding, Di; Han, Siyu; Zhang, Hui; He, Ye; Li, Ying

doi:10.1016/j.compbiolchem.2019.107106

Cited by 42 publications

(28 citation statements)

References 22 publications

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“…Another study also proved that knockdown of NDRG1 can promote EMT progress of CRC via NF‐κB signalling 52 . ESM1 that can be secreted into saliva, blood and urine is considered a candidate biomarker for CRC 53 . The ceRNA‐ and TF‐mediated regulatory networks further identified FGF2 , CCL28 and UCN in CRC progression.…”

Section: Discussionmentioning

confidence: 98%

“…14 We systematically evaluated the immu- ESM1 that can be secreted into saliva, blood and urine is considered a candidate biomarker for CRC. 53 The ceRNA-and TF-mediated regulatory networks further identified FGF2, CCL28 and UCN in CRC progression. FGF2 is associated with increased CRC responsiveness to irinotecan 54 and is also a member of a gene hub associated with cetuximab insensitivity in CRC.…”

Section: Discussionmentioning

confidence: 99%

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A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Wang

Chen

et al. 2020

J Cellular Molecular Medi

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Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2 , FGF2 , CCL28 , NDRG1 , ESM1 , UCN , UTS2 and TRDC . The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT‑qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Wang

Chen

et al. 2020

J Cellular Molecular Medi

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“…ESM-1 is a secreted proteoglycan dermatan produced by ECs, and as mentioned in the introduction, a high level of ESM-1 secretion has been found in various cancers. In addition, recent reports have suggested that ESM-1 is a biomarker of TNBC [ 19 ] and colorectal cancer [ 31 , 32 ]. However, the role of ESM-1 in tumorigenesis in BC, especially RT-R TNBC, is not well known.…”

Section: Discussionmentioning

confidence: 99%

“…The ERK pathway is also involved in tumorigenesis by promoting cell proliferation, survival, and angiogenesis, resulting in decreased life expectancy of TNBC patients [ 40 ]. Activation of the ERK pathway potentiates the activation of HIF-1α, NF-κB, and STAT-3, and these transcription factors are well known to be involved in the tumor growth and progression through the transcriptional regulation of various inflammatory genes, such as adhesion molecules, MMPs, and VEGF [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Accordingly, we investigated the effect of ESM-1 on the activation of these intracellular signaling molecules and transcription factors.…”

Section: Discussionmentioning

confidence: 99%

ESM-1 Overexpression is Involved in Increased Tumorigenesis of Radiotherapy-Resistant Breast Cancer Cells

Jin

Trojan

et al. 2020

Cancers

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The key barrier to the effectiveness of radiotherapy remains the radioresistance of breast cancer cells, resulting in increased tumor recurrence and metastasis. Thus, in this study, we aimed to clarify the difference between radiotherapy-resistant (RT-R) breast cancer (BC) and BC, and accordingly, analyzed gene expression levels between radiotherapy-resistant (RT-R) MDA-MB-231 cells and MDA-MB-231 cells. Gene expression array showed that ESM-1 was the most upregulated in RT-R-MDA-MB-231 cells compared to MDA-MB-231 cells. Then, we aimed to investigate the role of ESM-1 in the increased tumorigenesis of RT-R-BC cells. RT-R-MDA-MB-231, which showed an increased expression level of ESM1, exhibited significantly enhanced proliferation, colony forming ability, migration, and invasion compared to MDA-MB-231 cells, and ESM-1 knockdown effectively reversed these effects. In addition, compared to MDA-MB-231 cells, RT-R-MDA-MB-231 cells displayed improved adhesion to endothelial cells (ECs) due to the induction of adhesion molecules and increased MMP-9 activity and VEGF-A production, which were decreased by ESM-1 knockdown. Moreover, the expression of HIF-1α and activation of NF-κB and STAT-3 were increased in RT-R-MDA-MB-231 cells compared to MDA-MB-231 cells, and these effects were abolished by the knockdown of ESM-1. Finally, we confirmed the role of ESM-1 in tumorigenesis in an in vivo mouse model. Tumor volume, lung metastasis, and tumorigenic molecules (VEGF-A, HIF-1α, MMP-9, ICAM-1, VCAM-1, and phospho-NF-κB and phospho-STAT-3) were significantly induced in mice injected with ESM-1-overexpressing 4T1 cells and greatly enhanced in those injected with ESM-1-overexpressing RT-R-4T1 cells. Taken together, these results suggest for the first time that ESM-1 plays a critical role in tumorigenesis of breast cancer cells, especially RT-R-breast cancer cells, through the induction of cell proliferation and invasion.

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“…The role of genetic mutations in CRC has been extensively analysed and several publications are available in the literature on this topic [13][14][15][16]. Other authors have focused on identifying biomarkers with the aim of finding the subset with the highest predictive power [17][18][19][20]. Early identification could increase the likelihood of survival and dramatically reduce the mortality rate.…”

Section: Introductionmentioning

confidence: 99%

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Banegas-Luna

Peña‐García

Iftene

et al. 2021

IJMS

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Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

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Predictive biomarkers of colorectal cancer

Cited by 42 publications

References 22 publications

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

ESM-1 Overexpression is Involved in Increased Tumorigenesis of Radiotherapy-Resistant Breast Cancer Cells

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

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