scholarly journals Association Between Obesity and Incident Colorectal Cancer: An Analysis Based on Colorectal Cancer Database in the Cancer Genome Atlas

2021 ◽  
Author(s):  
Su Yongxian ◽  
Chen Tonghua
Keyword(s):  
Colorectal Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Obese Group ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Significant Difference ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Abstract Background To investigate gene factors of colorectal cancer (CRC) in obesity and potential molecular markers. Methods Clinical data and mRNA expression data from The Cancer Genome Atlas (TCGA) was collected and divided into obese group and non-obese group according to BMI. The differential expressed genes (DEGs) were screened out by “Limma” package of R software based on (|log2(fold change)|>2 and p < 0.05). The functions of DEGs were revealed with Gene Ontology and Kyoto Encyclopedia Genes and Genomes pathway enrichment analysis using the DAVID database. Then STRING database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. Kaplan-Meier analysis was used to assess the potential prognostic genes for CRC patients. Results It has revealed 2055 DEGs in obese group with CRC, 7615 DEGs in non-obese group and 9046 DEGs in total group. MS4A12, TMIGD1, CA2, GBA3 and SLC51B were the top five downregulated genes in obese group. A PPI network consisted of 1042 nodes and 4073 edges, and top ten hub genes SST, PYY, GNG12, CCL13, MCHR2, CCL28, ADCY9, SSTR1, CXCL12 and ADRA2A were identified in obese group. PDCD11 may well predict overall survivals of CRC patients in non-obese group. The survival time of obese group was shorter than that of non-obese group, but there was no significant difference. Conclusions PDCD11 may be a potential molecular marker for non-obese patients with CRC.

scholarly journals Exceptional Chemotherapy Response in Metastatic Colorectal Cancer Associated With Hyper-Indel–Hypermutated Cancer Genome and Comutation of POLD1 and MLH1

2017 ◽  
pp. 1-12
Author(s):  
Manish R. Sharma ◽  
James T. Auman ◽  
Nirali M. Patel ◽  
Juneko E. Grilley-Olson ◽  
Xiaobei Zhao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Somatic Mutation ◽  
Mutation Rate ◽  
Germ Line ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Response To Chemotherapy ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Purpose A 73-year-old woman with metastatic colon cancer experienced a complete response to chemotherapy with dose-intensified irinotecan that has been durable for 5 years. We sequenced her tumor and germ line DNA and looked for similar patterns in publicly available genomic data from patients with colorectal cancer. Patients and Methods Tumor DNA was obtained from a biopsy before therapy, and germ line DNA was obtained from blood. Tumor and germline DNA were sequenced using a commercial panel with approximately 250 genes. Whole-genome amplification and exome sequencing were performed for POLE and POLD1. A POLD1 mutation was confirmed by Sanger sequencing. The somatic mutation and clinical annotation data files from the colon (n = 461) and rectal (n = 171) adenocarcinoma data sets were downloaded from The Cancer Genome Atlas data portal and analyzed for patterns of mutations and clinical outcomes in patients with POLE- and/or POLD1-mutated tumors. Results The pattern of alterations included APC biallelic inactivation and microsatellite instability high (MSI-H) phenotype, with somatic inactivation of MLH1 and hypermutation (estimated mutation rate > 200 per megabase). The extremely high mutation rate led us to investigate additional mechanisms for hypermutation, including loss of function of POLE. POLE was unaltered, but a related gene not typically associated with somatic mutation in colon cancer, POLD1, had a somatic mutation c.2171G>A [p.Gly724Glu]. Additionally, we noted that the high mutation rate was largely composed of dinucleotide deletions. A similar pattern of hypermutation (dinucleotide deletions, POLD1 mutations, MSI-H) was found in tumors from The Cancer Genome Atlas. Conclusion POLD1 mutation with associated MSI-H and hyper-indel–hypermutated cancer genome characterizes a previously unrecognized variant of colon cancer that was found in this patient with an exceptional response to chemotherapy.

scholarly journals Identification of Potential Signatures and Their Functions for Acute Lymphoblastic Leukemia: A Study Based on the Cancer Genome Atlas

2021 ◽  
Vol 12 ◽  
Author(s):  
Weimin Wang ◽  
Chunhui Lyu ◽  
Fei Wang ◽  
Congcong Wang ◽  
Feifei Wu ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Real Time ◽  
Lymphoblastic Leukemia ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Cerna Network ◽  
Cancer Genome Atlas ◽  
Genome Atlas ◽  
Real Time Qpcr

ObjectiveAcute lymphoblastic leukemia (ALL) is a malignant disease most commonly diagnosed in adolescents and young adults. This study aimed to explore potential signatures and their functions for ALL.MethodsDifferentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) were identified for ALL from The Cancer Genome Atlas (TCGA) and normal control from Genotype-Tissue Expression (GTEx). DElncRNA–microRNA (miRNA) and miRNA–DEmRNA pairs were predicted using online databases. Then, a competing endogenous RNA (ceRNA) network was constructed. Functional enrichment analysis of DEmRNAs in the ceRNA network was performed. Protein–protein interaction (PPI) network was then constructed. Hub genes were identified. DElncRNAs in the ceRNA network were validated using Real-time qPCR.ResultsA total of 2,903 up- and 3,228 downregulated mRNAs and 469 up- and 286 downregulated lncRNAs were identified for ALL. A ceRNA network was constructed for ALL, consisting of 845 lncRNA-miRNA and 395 miRNA–mRNA pairs. These DEmRNAs in the ceRNA network were mainly enriched in ALL-related biological processes and pathways. Ten hub genes were identified, including SMAD3, SMAD7, SMAD5, ZFYVE9, FKBP1A, FZD6, FZD7, LRP6, WNT1, and SFRP1. According to Real-time qPCR, eight lncRNAs including ATP11A-AS1, ITPK1-AS1, ANO1-AS2, CRNDE, MALAT1, CACNA1C-IT3, PWRN1, and WT1-AS were significantly upregulated in ALL bone marrow samples compared to normal samples.ConclusionOur results showed the lncRNA expression profiles and constructed ceRNA network in ALL. Furthermore, eight lncRNAs including ATP11A-AS1, ITPK1-AS1, ANO1-AS2, CRNDE, MALAT1, CACNA1C-IT3, PWRN1, and WT1-AS were identified. These results could provide a novel insight into the study of ALL.

Expression of DUSP4 transcript variants as a potential biomarker for colorectal cancer

2020 ◽  
Vol 14 (8) ◽  
pp. 639-650
Author(s):  
Tatiana Varela ◽  
Vincent Laizé ◽  
Natércia Conceição ◽  
Paulo Caldeira ◽  
Ana Marreiros ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Kras Mutations ◽  
Normal Tissues ◽  
Potential Biomarker ◽  
Tumor Stages ◽  
Cancer Genome Atlas ◽  
Poorly Differentiated ◽  
Genome Atlas

Aim: To provide novel data on the expression of DUSP4 transcripts in colorectal cancer (CRC) tissues and to explore their potential as biomarkers. Materials & methods: DUSP4 transcripts expression was determined by quantitative real-time PCR in tissues from 28 CRC patients. Their association with clinicopathological factors and survival analysis was performed. Data from 380 CRC patients available at The Cancer Genome Atlas project were also analyzed. Results: All transcripts were overexpressed in CRC tissues. Variant X1 was the most upregulated and associated with KRAS mutations and poorly differentiated tumor. Overexpression of DUSP4 transcripts could distinguish all tumor stages from normal tissues. Similar results were found in The Cancer Genome Atlas cohort. Conclusion: DUSP4 transcripts have the potential to serve as diagnostic biomarkers for CRC, particularly variant X1.

scholarly journals ECM–Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Stepan Nersisyan ◽  
Victor Novosad ◽  
Narek Engibaryan ◽  
Yuri Ushkaryov ◽  
Sergey Nikulin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regulatory Network ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Survival Prediction ◽  
Concordance Index ◽  
Logrank Test ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Interactions of the extracellular matrix (ECM) and cellular receptors constitute one of the crucial pathways involved in colorectal cancer progression and metastasis. With the use of bioinformatics analysis, we comprehensively evaluated the prognostic information concentrated in the genes from this pathway. First, we constructed a ECM–receptor regulatory network by integrating the transcription factor (TF) and 5’-isomiR interaction databases with mRNA/miRNA-seq data from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD). Notably, one-third of interactions mediated by 5’-isomiRs was represented by noncanonical isomiRs (isomiRs, whose 5’-end sequence did not match with the canonical miRBase version). Then, exhaustive search-based feature selection was used to fit prognostic signatures composed of nodes from the network for overall survival prediction. Two reliable prognostic signatures were identified and validated on the independent The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) cohort. The first signature was made up by six genes, directly involved in ECM–receptor interaction: AGRN, DAG1, FN1, ITGA5, THBS3, and TNC (concordance index 0.61, logrank test p = 0.0164, 3-years ROC AUC = 0.68). The second hybrid signature was composed of three regulators: hsa-miR-32-5p, NR1H2, and SNAI1 (concordance index 0.64, logrank test p = 0.0229, 3-years ROC AUC = 0.71). While hsa-miR-32-5p exclusively regulated ECM-related genes (COL1A2 and ITGA5), NR1H2 and SNAI1 also targeted other pathways (adhesion, cell cycle, and cell division). Concordant distributions of the respective risk scores across four stages of colorectal cancer and adjacent normal mucosa additionally confirmed reliability of the models.

scholarly journals Characterization of bidirectional gene pairs in The Cancer Genome Atlas (TCGA) dataset

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7107 ◽  
Author(s):  
Juchuanli Tu ◽  
Xiaolu Li ◽  
Jianjun Wang
Keyword(s):  
Gene Pair ◽  
Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Gene Pairs ◽  
Significant Difference ◽  
Cancer Genome Atlas ◽  
Tcga Dataset ◽  
Bidirectional Gene Pair ◽  
Genome Atlas

The “bidirectional gene pair” indicates a particular head-to-head gene organization in which transcription start sites of two genes are located on opposite strands of genomic DNA within a region of one kb. Despite bidirectional gene pairs are well characterized, little is known about their expression profiles and regulation features in tumorigenesis. We used RNA-seq data from The Cancer Genome Atlas (TCGA) dataset for a systematic analysis of the expression profiles of bidirectional gene pairs in 13 cancer datasets. Gene pairs on the opposite strand with transcription end site distance within one kb or on the same strand with the distance of two genes between 1–10 kb and gene pairs comprising two randomly chosen genes were used as control gene pairs (CG1, CG2, and random). We identified and characterized up-/down-regulated genes by comparing the expression level between tumors and adjacent normal tissues in 13 TCGA datasets. There were no consistently significant difference in the percentage of up-/down-regulated genes between bidirectional and control/random genes in most of TCGA datasets. However, the percentage of bidirectional gene pairs comprising two up- or two down-regulated genes was significantly higher than gene pairs from CG1/2 in 12/11 analyzed TCGA datasets and the random gene pairs in all 13 TCGA datasets. Then we identified the methylation correlated bidirectional genes to explore the regulatory mechanism of bidirectional genes. Like the differentially expressed gene pairs, the bidirectional genes in a pair were significantly prone to be both hypo- or hyper-methylation correlated genes in 12/13 TCGA datasets when comparing to the CG2/random gene pairs despite no significant difference between the percentages of hypo-/hyper-methylation correlated genes in bidirectional and CG2/random genes in most of TCGA datasets. Finally, we explored the correlation between bidirectional genes and patient’s survival, identifying prognostic bidirectional genes and prognostic bidirectional gene pairs in each TCGA dataset. Remarkably, we found a group of prognostic bidirectional gene pairs in which the combination of two protein coding genes with different expression level correlated with different survival prognosis in survival analysis for OS. The percentage of these gene pairs in bidirectional gene pair were significantly higher than the gene pairs in controls in COAD datasets and lower in none of 13 TCGA datasets.

scholarly journals Integrating Transcriptomics for the Identification of Potential Age-related Genes and Cells in Three Major Urogenital Cancers Across the Cancer Genome Atlas

2020 ◽  
Author(s):  
Jinlong Cao ◽  
Jianpeng Li ◽  
Xin Yang ◽  
Pan Li ◽  
Zhiqiang Yao ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
R Package ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Age Related ◽  
Cancer Genome Atlas ◽  
Urogenital Cancers ◽  
Genome Atlas

Abstract Background: Cancer is often defined as a disease of aging. The majority of patients with urogenital cancers are the elderly, whose clinical characteristics are greatly affected by age and aging. Here, we aimed to explore age-related biological changes in three major urogenital cancers by integrative bioinformatics analysis.Methods: First, mRNA (count format) and clinical data for bladder cancer, prostate cancer and renal cell carcinoma were downloaded from the Cancer Genome Atlas (TCGA) portal. The expressions of 64 cells were obtained by xCell deconvolution method. EdgeR package and limma package were used to analyze differentially expressed genes and cells in the young group and the old group, respectively. ClusterProfiler R package and clueGO plugin were used for enrichment analysis, and cytohubba plugin was used for hub genes analysis. Then co-expression analysis and chromosome distribution for hub genes were analyzed and demonstrated by RIdeogram R package. The clinical correlation of hub genes and key cells was analyzed by Graphpad Prism software. Finally, the correlation between hub genes and key cells was explored by corrplot R package.Results: We screened and identified 14 hub genes and 4 key cells related to age and urogenital cancers. The age-related differentially expressed genes and co-expressed genes were mainly enriched in muscle movement (Cl-, Ca2+), inflammatory response, antibacterial humoral immune response, substance metabolism and transport, redox reaction, etc. Most of the age-related genes are on chromosome 17. Moreover, the correlation between cells and genes was analyzed. Conclusion: Our study analyzed age-related genes and cells in the tumor microenvironment of urogenital cancers, and explored the pathways involved. This could contribute to personalized therapy for patients of different ages and a new understanding of the potential relationship between the aging microenvironment and urogenital cancers.

Aberrantly methylated-differentially expressed genes and pathways in Epstein–Barr virus-associated gastric cancer

2020 ◽  
Vol 16 (6) ◽  
pp. 187-197
Author(s):  
Jing-jing Jing ◽  
Hao Li ◽  
Ze-yang Wang ◽  
Heng Zhou ◽  
Li-ping Sun ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Epstein Barr Virus ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Barr Virus ◽  
Cancer Genome Atlas ◽  
Epstein Barr ◽  
Genome Atlas

Aim: To identify the methylated-differentially expressed genes (MDEGs) that may serve as diagnostic markers and therapeutic targets in Epstein–Barr virus-associated gastric cancer (EBVaGC) and to explore the methylation-based pathways for elucidating biological mechanisms of EBVaGC. Materials & methods: Gene expression and methylation profiles were downloaded from GEO database. MDEGs were identified by GEO2R. Pathway enrichment analyses were conducted based on DAVID database. Hub genes were identified by Cytoscape, which were further verified by The Cancer Genome Atlas database. Results: A total of 367 hypermethylated, lowly expressed genes were enriched in specific patterns of cell differentiation. 31 hypomethylated, highly expressed genes demonstrated enrichment in regulation of immune system process. After validation using The Cancer Genome Atlas database, seven genes were confirmed to be significantly different hub genes in EBVaGC. Conclusion: EBVaGC-specific MDEGs and pathways can be served as potential biomarkers for precise diagnosis and treatment of EBVaGC and provide novel insights into the mechanisms involved.

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