Identification of Significant Genes with poor prognosis in Liver Metastasis of Colorectal Cancer via Bioinformatical Analysis

Mapping Intimacies ◽

10.21203/rs.3.rs-78881/v1 ◽

2020 ◽

Author(s):

Junsheng Chen ◽

Hongzhou Liu

Keyword(s):

Colorectal Cancer ◽

Cytochrome P450 ◽

Liver Metastases ◽

Poor Prognosis ◽

Prion Diseases ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Chemical Carcinogenesis ◽

Venn Diagram ◽

Underlying Mechanisms

Abstract Background: Colorectal cancer (CRC) is a common malignant tumor in the world wild, and more than 50% patients have liver metastases. Purpose: The purpose of this study is to identify significant genes with poor outcome and the underlying mechanisms of CRC liver metastases. Methods: Gene expression profiles of GSE50760, GSE41568 and GSE14297 are available on website of GEO database. Differentially expressed genes (DEGs) between CRC liver metastases and primary tissues were picked out by GEO2R tool and Venn diagram software. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO). Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Results: There were total of 147 consistently expressed genes in the three datasets, including 123 up-regulated genes and 24 down-regulated genes enriched in complement and coagulation cascades, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, prion diseases, chemical carcinogenesis, staphylococcus aureus infection and linoleic acid metabolism. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 39 genes were selected. Moreover, for the analysis of CRC survival among those genes, Kaplan–Meier analysis was implemented and 4 (SERPING1 ITIH2 CDH2 APOE) of 39 genes had a significantly worse prognosis. Conclusion: we have identified four significant DEGs with poor prognosis in CRC liver metastases on the basis of integrated bioinformatical methods, which could be potential therapeutic targets for CRC patients with liver metastases.

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Bioinformatics Analysis of Genes Upregulating Poor Prognosis In Colorectal Cancer And Gastric Cancer

10.21203/rs.3.rs-635223/v1 ◽

2021 ◽

Author(s):

Ruizhi Dong ◽

Shaodong Li ◽

Bin Liang ◽

Zhenhua Kang

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Gastric Cancer ◽

Survival Rate ◽

Poor Prognosis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Venn Diagram ◽

Expression Levels ◽

Significant Difference

Abstract Purpose : To analyze the up-regulated genes of poor prognosis in colorectal cancer and gastric cancer by bioinformatics. Methods: We searched the gene expression profiles GSE156355 and GSE64916 in colorectal cancer and gastric cancer tissues in NCBI-GEO. With P value < 0.05 and log2>1 as the standard, Venn diagram software was used to identify the common DEGs in the two data sets. Kaplan Meier plotter was used to analyze the survival rate data of common differentially expressed genes, draw and select survival curves, and analyze their expression levels. Results: A total of 97 genes were detected to be up-regulated in the two gene expression profiles. There were 19 genes in the prognosis of gastric cancer and 15 genes in the prognosis of colorectal cancer that had significant differences in the survival rate. Among them, KCNQ1, TRIM29, GART, MSX1, SNAI1, SUV39H2, LOXL2 and KCTD14 significantly decreased the survival rate of gastric cancer and colorectal cancer. The expression of MSX1 was the highest in gastric cancer. The expression level of KCTD14 was the highest in colorectal cancer, and there was no significant difference in the expression levels of other genes. Conclusion: There are 19 and 15 genes with significantly different prognostic viability in gastric cancer and colorectal cancer, respectively. The survival rates of KCNQ1, TRIM29, GART, Msx1, SNAI1, SUV39H2, LOXL2 and KCTD14 were significantly decreased in gastric cancer and colorectal cancer. The expression of MSX1 was the highest in gastric cancer. The expression of KCTD14 was the highest in colorectal cancer.

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Epigenetically regulated gene expression profiles reveal four molecular subtypes with prognostic and therapeutic implications in colorectal cancer

Briefings in Bioinformatics ◽

10.1093/bib/bbaa309 ◽

2020 ◽

Author(s):

Xiaokang Wang ◽

Jinfeng Liu ◽

Danwen Wang ◽

Maohui Feng ◽

Xiongzhi Wu

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Metabolic Activity ◽

Poor Prognosis ◽

Molecular Subtypes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Epigenetic Mechanisms ◽

Regulated Gene Expression ◽

Marked Proliferation

Abstract Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of colorectal cancer (CRC). Herein, we first demonstrated that the frequencies of the aberrancies of DNA methylation-correlated (METcor) and microRNA (miRNA)-correlated (MIRcor) genes were significantly co-regulated. Next, through integrative clustering of the expression profiles of METcor and MIRcor genes, four molecular subtypes were identified in CRC patients from The Cancer Genome Atlas and then validated in four independent datasets. More importantly, the four subtypes were well characterized and showed distinct clinical and molecular features: (i) S-I: high metabolic activity, sensitive to 5-fluorouracil-based chemotherapy and good prognosis; (ii) S-II: moderate metabolic activity, marked proliferation, frequent KRAS mutation and intermediate prognosis; (iii) S-III: moderate metabolic activity, marked proliferation, promoter DNA hypermethylation, high mutation burden, frequent BRAF and EGFR mutations, moderate levels of epithelial-mesenchymal transition (EMT) and transforming growth factor β (TGFβ) signals, immune-inflamed phenotype, sensitive to cetuximab and death protein-1 inhibitor treatment and relatively poor prognosis and (iv) S-IV: miRNA overexpression, stem/serrated/mesenchymal-like properties, hypoxia, high levels of EMT and TGFβ signals, immune-excluded phenotype and poor prognosis. Overall, this study established a molecular classification based on epigenetically regulated gene expression profiles, thereby providing a better understanding of the epigenetic mechanisms underlying CRC heterogeneity.

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Identification of Significant Genes With Poor Colorectal Cancer Prognosis In Via Bioinformatical Analysis

10.21203/rs.3.rs-645864/v1 ◽

2021 ◽

Author(s):

Shaodong Li ◽

Ruizhi Dong ◽

Bin Liang ◽

Zhenhua Kang

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Reduction Process ◽

Zinc Ion ◽

Cancer Prognosis ◽

Venn Diagram ◽

Normal Tissues ◽

Bioinformatical Analysis

Abstract Purpose:Identification of significant genes with poor colorectal cancer prognosis in via bioinformatical analysis.Method:Gene expression profiles of GSE74602、 GSE110223、GSE113513 and GSE 141174 were available from GEO database. There are 65 CRC tissues and 65 normal tissues in the four profile datasets. Differentially expressed genes (DEGs) between CRC tissues and normal tissues were picked out by GEO2R tool and Venn diagram software. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO). Then protein-protein interaction (PPI) of these DEGs with Search Tool for the Retrieval of Interacting Genes (STRING).Results:There were total of 171 consistently expressed genes in the four datasets, including 148 up-regulated and 23 down-regulated genes. up-regulated DEGs were particularly enriched in oxidation-reduction process, in extracellular exosome, in zinc ion binding, in Metabolic pathways, Mineral absorption; and down-regulated DEGs in positive regulation of cell proliferation, in cytosol, in One carbon pool by folate. Furthermore, for the analysis of overall survival among those genes, Kaplan–Meier analysis was implemented and 30 of 88 genes had a significantly worse prognosis. For validation in Gene Expression Profiling Interactive Analysis (GEPIA), 13 of 30 genes were discovered highly expressed in CRC tissues compared to normal tissues. Furthermore, MYC 和 FGFR3 markedly enriched in the Bladder cancer pathway.Conclusion: We have identified two significant up-regulated DEGs with poor prognosis in CRC , which could be potential therapeutic targets for CRC patients.

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Cytochrome P450 content in primary tumors and liver metastases of patients with metastatic colorectal cancer

Experimental Oncology ◽

10.32471/exp-oncology.2312-8852.vol-42-no-4.15310 ◽

2020 ◽

Vol 42 (4) ◽

Keyword(s):

Colorectal Cancer ◽

Cytochrome P450 ◽

Liver Metastases ◽

Metastatic Colorectal Cancer ◽

Primary Tumors ◽

Cytochrome P450 Content

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High TKTL1 expression as a sign of poor prognosis in colorectal cancer with synchronous rather than metachronous liver metastases

Cancer Biology & Therapy ◽

10.1080/15384047.2020.1803008 ◽

2020 ◽

Vol 21 (9) ◽

pp. 826-831

Author(s):

Reetta Peltonen ◽

Kaisa Ahopelto ◽

Jaana Hagström ◽

Camilla Böckelman ◽

Caj Haglund ◽

...

Keyword(s):

Colorectal Cancer ◽

Liver Metastases ◽

Poor Prognosis

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Gene Expression Profiles as Predictors of Poor Outcomes in Stage II Colorectal Cancer: A Systematic Review and Meta-analysis

Clinical Colorectal Cancer ◽

10.3816/ccc.2009.n.035 ◽

2009 ◽

Vol 8 (4) ◽

pp. 207-214 ◽

Cited By ~ 22

Author(s):

An-Ting T. Lu ◽

Shelley R. Salpeter ◽

Anthony E. Reeve ◽

Steven Eschrich ◽

Patrick G. Johnston ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Systematic Review ◽

Expression Profiles ◽

Meta Analysis ◽

Gene Expression Profiles ◽

Stage Ii ◽

Stage Ii Colorectal Cancer ◽

Poor Outcomes

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Chamber-enriched gene expression profiles in failing human hearts with reduced ejection fraction

Scientific Reports ◽

10.1038/s41598-021-91214-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Luo ◽

Jun Yin ◽

Denise Dwyer ◽

Tracy Yamawaki ◽

Hong Zhou ◽

...

Keyword(s):

Heart Failure ◽

Ejection Fraction ◽

Human Genetics ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Left Heart ◽

Reduced Ejection Fraction ◽

Cell Type Composition ◽

Type Composition ◽

Underlying Mechanisms

AbstractHeart failure with reduced ejection fraction (HFrEF) constitutes 50% of HF hospitalizations and is characterized by high rates of mortality. To explore the underlying mechanisms of HFrEF etiology and progression, we studied the molecular and cellular differences in four chambers of non-failing (NF, n = 10) and HFrEF (n = 12) human hearts. We identified 333 genes enriched within NF heart subregions and often associated with cardiovascular disease GWAS variants. Expression analysis of HFrEF tissues revealed extensive disease-associated transcriptional and signaling alterations in left atrium (LA) and left ventricle (LV). Common left heart HFrEF pathologies included mitochondrial dysfunction, cardiac hypertrophy and fibrosis. Oxidative stress and cardiac necrosis pathways were prominent within LV, whereas TGF-beta signaling was evident within LA. Cell type composition was estimated by deconvolution and revealed that HFrEF samples had smaller percentage of cardiomyocytes within the left heart, higher representation of fibroblasts within LA and perivascular cells within the left heart relative to NF samples. We identified essential modules associated with HFrEF pathology and linked transcriptome discoveries with human genetics findings. This study contributes to a growing body of knowledge describing chamber-specific transcriptomics and revealed genes and pathways that are associated with heart failure pathophysiology, which may aid in therapeutic target discovery.

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High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning

Briefings in Bioinformatics ◽

10.1093/bib/bbab368 ◽

2021 ◽

Author(s):

Xiangtao Li ◽

Shaochuan Li ◽

Lei Huang ◽

Shixiong Zhang ◽

Ka-chun Wong

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Single Cell ◽

Deep Neural Networks ◽

Expression Profiles ◽

Marker Gene ◽

Gene Expression Profiles ◽

Underlying Mechanisms ◽

Cell Data ◽

Gene Expression Levels

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

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