Computational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer Progression

Palaniappan, Ashok; Ramar, Karthick; Ramalingam, Satish

doi:10.1371/journal.pone.0156665

Cited by 63 publications

(47 citation statements)

References 92 publications

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“…[16]. There is a direct correlation between mortality rate and stages of cancer, and the stage progression could be checked by early detection and appropriate treatment strategies [17]. Although knowledge about genomic profiling has been identified in terms of varied molecular features associated with subtypes of cancer, its molecular mechanism of progression is poorly understood [18].…”

Section: Introductionmentioning

confidence: 99%

“…The incorporation of this staging system into molecular or genetic profiles can help in detecting prognostic groups that guide the disease intervention [19]. There is a sharp decrease in the 5-year survival rate of patients with the stage-wise progression of breast cancer [17]. Treatment of cancer remains a challenge because of the lack of knowledge about factors for cancer progression and metastasis [19].…”

Section: Introductionmentioning

confidence: 99%

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Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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Phone: +91 26743007Fax: +91 26742316 Email -dinesh@icgeb.res.in (DG)Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients.However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods.We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP. Key PointsDifferent supervised machine-learning algorithms such as Random Forest, SVM and Naive Bayes were trained with enriched features of the TCGA RNA-seq datasets selected for the study.We have developed two-class classification models, trained with relevant gene expression profiles to efficiently discriminate between the early and late IDC stages.Finally, we also developed a web server using python scikit-learn to provide freely available GUI based access to the machine learning models developed by us. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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“…Abnormal and ectopic CPS1 expression in tumour cells may lead to urea cycle deficiency and a nucleic acid pool imbalance . CPS1 overexpression is associated with poor prognosis in several types of cancer, such as colon cancer, cholangiocarcinoma and glioblastoma . CPS1 has also been reported to be regulated by hepatocyte nuclear factor 3β (HNF3β) and sirtuin 5 (SIRT5) by their transcriptional activation or deacetylation‐ and deglutarylation‐mediated activation .…”

Section: Discussionmentioning

confidence: 99%

“…15,16,29 CPS1 overexpression is associated with poor prognosis in several types of cancer, such as colon cancer, cholangiocarcinoma and glioblastoma. [30][31][32] CPS1 has also been reported to be regulated by hepatocyte nuclear factor 3β (HNF3β) and sirtuin 5 (SIRT5) by their transcriptional activation or deacetylation-and deglutarylation-mediated activation. [33][34][35] Thus, the results of our study might supplement the previous reported associations.…”

Section: Abnormal and Ectopic Cps1 Expression In Tumour Cells May Leadmentioning

confidence: 99%

Caspase recruitment domain family member 10 regulates carbamoyl phosphate synthase 1 and promotes cancer growth in bladder cancer cells

Liu

Zhang

et al. 2019

J Cellular Molecular Medi

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Bladder cancer, which can be divided into non‐muscle‐invasive and muscle‐invasive bladder cancer, is the most common urinary cancer in the United States. Caspase recruitment domain family member 10 (CARD10), also named CARD‐containing MAGUK protein 3 (CARMA3), is a member of the CARMA family and may activate the nuclear factor kappa B (NF‐κB) pathway. We utilized RNA sequencing and metabolic mass spectrometry to identify the molecular and metabolic feature of CARD10. The signalling pathway of CARD10 was verified by Western blotting analysis and functional assays. RNA sequencing and metabolic mass spectrometry of CARD10 knockdown identified the metabolic enzyme carbamoyl phosphate synthase 1 (CPS1) in the urea cycle as the downstream gene regulated by CARD10. We confirmed that CARD10 affected cell proliferation and nucleotide metabolism through regulating CPS1. We indicated that CARD10 promote bladder cancer growth via CPS1 and maybe a potential therapeutic target in bladder cancer.

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“…It is reasonable to hypothesize that the stage-specific gross changes are associated with signature molecular events, and try to probe such molecular bases of stage-wise progression of cancer. We had earlier published on stage-specific "hub driver" genes in colorectal cancer (Palaniappan et al, 2016). A stage-focussed analysis of colorectal cancer transcriptome data yielded negative results vis-a-vis the AJCC staging system (Huo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Novel Significant Stage-Specific Differentially Expressed Genes in Liver Hepatocellular Carcinoma

Sarathi

Palaniappan

2018

Preprint

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Liver cancer is among the top deadly cancers worldwide with a very poor prognosis, and the liver is a particularly vulnerable site for metastasis of other cancers. In this study, we developed a novel computational framework for the stage-specific analysis of hepatocellular carcinoma initiation and progression. Using publicly available clinical and RNA-Seq data of cancer samples and controls, we annotated the gene expression matrix with sample stages. We performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in gene expression in cancer samples relative to control. Using a contrast against the control, we were able to identify differentially expressed genes (log fold change >2) that were significant at an adjusted pvalue < 10E-3. In order to identify genes that were specific to each stage without confounding differential expression in other stages, we developed a full set of pairwise stage contrasts and enforced a p-value threshold (<0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. Our analysis yielded two stage

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Computational Identification of Novel Stage-Specific Biomarkers in Colorectal Cancer Progression

Cited by 63 publications

References 92 publications

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Caspase recruitment domain family member 10 regulates carbamoyl phosphate synthase 1 and promotes cancer growth in bladder cancer cells

Novel Significant Stage-Specific Differentially Expressed Genes in Liver Hepatocellular Carcinoma

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