The IRF family can influence tumor immunity and the prognosis of patients with colorectal cancer

Abstract Background: The roles of interferon-regulatory factors (IRFs) in colorectal cancer (CRC) have not been studied through bioinformatics analysis. Methods: We used gene- and microRNA-expression data for patients with somatic mutations and colon adenocarcinoma/rectum adenocarcinoma from The Cancer Genome Atlas Genomic Data Commons as a training dataset. Gene-expression data (accessions GSE17536 and GSE39582) were downloaded from the Gene Expression Omnibus database as the validation dataset. Expressional differences, clinical correlations, disease prognosis, functional enrichment, and immune correlations of IRF genes were analyzed. The results were validated via immunohistochemistry. Results: The mRNA-expression levels of IRF3 and IRF7 differed between tumor and normal tissues and were correlated with patient prognosis. The IRF score was an independent risk factor for overall survival. IRFs recruited inflammatory cells; however, the immune and stromal scores showed inflammatory-cell recruitment only in the tumor stroma; therefore, they did not help eliminate tumor cells. Functional-enrichment analysis and pan-cancer expression analysis revealed that IRFs were differentially expressed in tumor tissues and associated with patient prognosis. Conclusions: IRFs were differentially expressed in tumor tissues and were associated with prognosis in CRC patients. Although IRFs can promote the infiltration of immune cells, the immune and stromal score showed that the infiltrated immune cells mostly stayed in the tumor stroma and cannot directly eliminate the tumor. Our findings can help to improve CRC prognosis and treatment strategies.

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Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

BMC Medical Genomics ◽

10.1186/s12920-021-00966-3 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Xiao-Liang Xing ◽

Zhi-Yong Yao ◽

Chaoqun Xing ◽

Zhi Huang ◽

Jing Peng ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Risk Assessment ◽

Dna Methylation ◽

Differentially Expressed Genes ◽

Risk Score ◽

Immune Cells ◽

Assessment Model ◽

Differentially Expressed ◽

Risk Assessment Model

Abstract Background Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC. Methods In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model. Results Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers. Conclusion This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

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Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021443 ◽

2021 ◽

Vol 18 (6) ◽

pp. 8997-9015

Author(s):

Ahmed Hammad ◽

◽

Mohamed Elshaer ◽

Xiuwen Tang ◽

◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Machine Learning ◽

Roc Curve ◽

Functional Enrichment ◽

Differentially Expressed ◽

Ppi Network ◽

Hub Genes ◽

Survival Analyses ◽

Potential Biomarkers

<abstract> <p>Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.</p> </abstract>

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Construction and Prognostic Analysis of miRNA-mRNA Regulatory Network in Liver Metastasis from Colorectal Cancer

10.21203/rs.3.rs-88881/v1 ◽

2020 ◽

Author(s):

Ruyun Cai ◽

Qian Lu ◽

Da Wang

Keyword(s):

Colorectal Cancer ◽

Liver Metastasis ◽

Liver Metastases ◽

Immune Cells ◽

Interaction Analysis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Mesenchymal Transition ◽

Prognostic Analysis

Abstract Background: Colorectal cancer (CRC) is one of the most common cancers in the world, and liver metastasis is the leading cause of colorectal cancer-related deaths. However, the mechanism of liver metastasis in CRC hasn’t been clearly elucidated.Methods: Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEGs), which were subjected to functional enrichment analysis and protein-protein interaction analysis. Subsequently, mRNA-miRNA network was constructed and the associated DEGs and DEMs were performed for prognostic analysis. Finally, we did infiltration analysis of GAS1-associated immune cells. Results: We obtained 325 DEGs and 9 differentially expressed miRNAs (DEMs) between primary CRC and liver metastases. Enrichment analysis and protein-protein interactions (PPI) further revealed the involvement of DEGs in the formation of the inflammatory microenvironment and epithelial-mesenchymal transition (EMT) during the liver metastases process in CRC. Survival analysis demonstrated that low-expressed GAS1 as well as low-expressed hsa-miR-33b-5p was a favorable prognostic indicator of overall survival. Further exploration of GAS1 revealed that its expression was interrelated with the infiltration of immune cells in tumor tissues. Conclusions: In summary, DEGs, DEMs and their interactions found in liver metastasis of CRC may provide a basis for further understanding of the mechanism of CRC metastasis.

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Differential expression of micro-RNA in tumor and non-tumor tissues of patients with colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e13516 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e13516-e13516

Author(s):

Inna A. Novikova ◽

Natalya N. Timoshkina ◽

Oleg Ivanovich Kit ◽

Sergey I. Poluektov ◽

Andrey V. Dashkov ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Differential Expression ◽

Tumor Markers ◽

Target Genes ◽

Tumor Tissue ◽

Micro Rna ◽

Differentially Expressed ◽

Tumor Tissues ◽

Micro Rnas

e13516 Background: The colorectal cancer (CRC) incidence is steadily increasing. Moreover, the problem of its early diagnosis remains unresolved due to the low specificity of known tumor markers, and the problem of creating new therapeutic approaches is due to the lack of a complete understanding of the mechanisms of regulation of gene expression in this oncopathology. The study of micro-RNAs (short non-coding RNAs that regulate gene expression) can be the solution to both problems. The aim of the study was to analyze micro-RNA differential expression in the tumor and non-tumor tissues of CRC patients. Methods: 5 patients with CRC (colon adenocarcinoma, G2) were selected for the multiple parallel micro-RNA sequencing. The mirVana miRNA Isolation Kit protocol was used to isolate small RNA fractions. The miRNA library was prepared using the TruSeq Small RNASample Preparation Kit. Sequencing of the nucleotide sequences of cDNA libraries was performed using a MiSeq (Illumina, USA). The copy numbers of micro-RNA were determined by comparing the nucleotide sequence of the sequenced molecules in each sample with the known nucleotide sequences of micro-RNA presented in the databases. When analyzing the differential expression of micro-RNA, the DESeq2 method implemented in R medium was used. Results: Six differentially expressed micro-RNAs were detected (p < 0.05): 2 that decrease expression (hsa-miR-143-3p,hsa-miR-26a-5p) and 4 increase expression in the tumor relative to non-tumor (hsa-miR-25-3p, hsa-miR-92a-3p, hsa-miR-21-5p, hsa-let-7i-5p). The highest level of expression in both tumor and non-tumor tissue was observed for hsa-miR-143-3p, the lowest one for hsa-let-7i-5p. Moreover, the largest difference in micro-RNA expression in tumor tissue relative to non-tumor was shown for hsa-miR-92a-3p (4.5 times, p = 0.02), the smallest for hsa-miR-143-3p (2.4 times, p = 0.04). For miRNAs that differentially changed their expression, a search was made for target genes using the miRWalk 3.0 database. 14573 target genes were found, of which 3346 were for hypo-expressed micro-RNAs and 11228 for hyper-expressed micro-RNAs. Conclusions: Sequencing revealed 6 differentially expressed micro-RNAs (hsa-miR-143-3p, hsa-miR-26a-5p, hsa-miR-25-3p, hsa-miR-92a-3p, hsa-miR-21-5p, hsa-let-7i-5p) in the tumor tissue is relatively non-tumor tissues of the colon. The data obtained expand the understanding of the mechanisms of gene regulation in the context of this oncopathology and may possibly become the basis for highly specific tumor markers panel.

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Construction and prognostic analysis of miRNA-mRNA regulatory network in liver metastasis from colorectal cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02107-z ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Ruyun Cai ◽

Qian Lu ◽

Da Wang

Keyword(s):

Colorectal Cancer ◽

Liver Metastasis ◽

Liver Metastases ◽

Immune Cells ◽

Interaction Analysis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Mesenchymal Transition ◽

Prognostic Analysis

Abstract Background Colorectal cancer (CRC) is one of the most common cancers in the world, and liver metastasis is the leading cause of colorectal cancer-related deaths. However, the mechanism of liver metastasis in CRC has not been clearly elucidated. Methods Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEGs), which were subjected to functional enrichment analysis and protein-protein interaction analysis. Subsequently, mRNA-miRNA network was constructed, and the associated DEGs and DEMs were performed for prognostic analysis. Finally, we did infiltration analysis of growth arrest specific 1 (GAS1)-associated immune cells. Results We obtained 325 DEGs and 9 differentially expressed miRNAs (DEMs) between primary CRC and liver metastases. Enrichment analysis and protein-protein interactions (PPI) further revealed the involvement of DEGs in the formation of the inflammatory microenvironment and epithelial-mesenchymal transition (EMT) during the liver metastases process in CRC. Survival analysis demonstrated that low-expressed GAS1 as well as low-expressed hsa-miR-33b-5p was a favorable prognostic indicator of overall survival. Further exploration of GAS1 revealed that its expression was interrelated with the infiltration of immune cells in tumor tissues. Conclusions In summary, DEGs, DEMs, and their interactions found in liver metastasis of CRC may provide a basis for further understanding of the mechanism of CRC metastasis.

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Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893615999200629124444 ◽

2020 ◽

Vol 15 ◽

Author(s):

Chen-An Tsai ◽

James J. Chen

Keyword(s):

Gene Expression ◽

Correlation Analysis ◽

Gene Expression Data ◽

Differentially Expressed Gene ◽

Differentially Expressed ◽

Superior Performance ◽

Expression Data ◽

Gene Set ◽

Gene Sets ◽

Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

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Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

Egyptian Journal of Medical Human Genetics ◽

10.1186/s43042-021-00161-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rowan AlEjielat ◽

Anas Khaleel ◽

Amneh H. Tarkhan

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Differentially Expressed Genes ◽

Gene Network ◽

Disease Diagnosis ◽

Receptor Activation ◽

Functional Enrichment ◽

Differentially Expressed ◽

Inflammatory Disorder ◽

Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

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Behavioral sensitization induced by methamphetamine causes differential alterations in gene expression and histone acetylation of the prefrontal cortex in rats

BMC Neuroscience ◽

10.1186/s12868-021-00616-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hui Li ◽

Jing-An Chen ◽

Qian-Zhi Ding ◽

Guan-Yi Lu ◽

Ning Wu ◽

...

Keyword(s):

Gene Expression ◽

Prefrontal Cortex ◽

Histone Acetylation ◽

Differentially Expressed Genes ◽

Behavioral Sensitization ◽

Functional Enrichment ◽

Differentially Expressed ◽

Adult Rats ◽

Mrna Microarray ◽

H3 Acetylation

Abstract Background Methamphetamine (METH) is one of the most widely abused illicit substances worldwide; unfortunately, its addiction mechanism remains unclear. Based on accumulating evidence, changes in gene expression and chromatin modifications might be related to the persistent effects of METH on the brain. In the present study, we took advantage of METH-induced behavioral sensitization as an animal model that reflects some aspects of drug addiction and examined the changes in gene expression and histone acetylation in the prefrontal cortex (PFC) of adult rats. Methods We conducted mRNA microarray and chromatin immunoprecipitation (ChIP) coupled to DNA microarray (ChIP-chip) analyses to screen and identify changes in transcript levels and histone acetylation patterns. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were performed to analyze the differentially expressed genes. We then further identified alterations in ANP32A (acidic leucine-rich nuclear phosphoprotein-32A) and POU3F2 (POU domain, class 3, transcription factor 2) using qPCR and ChIP-PCR assays. Results In the rat model of METH-induced behavioral sensitization, METH challenge caused 275 differentially expressed genes and a number of hyperacetylated genes (821 genes with H3 acetylation and 10 genes with H4 acetylation). Based on mRNA microarray and GO and KEGG enrichment analyses, 24 genes may be involved in METH-induced behavioral sensitization, and 7 genes were confirmed using qPCR. We further examined the alterations in the levels of the ANP32A and POU3F2 transcripts and histone acetylation at different periods of METH-induced behavioral sensitization. H4 hyperacetylation contributed to the increased levels of ANP32A mRNA and H3/H4 hyperacetylation contributed to the increased levels of POU3F2 mRNA induced by METH challenge-induced behavioral sensitization, but not by acute METH exposure. Conclusions The present results revealed alterations in transcription and histone acetylation in the rat PFC by METH exposure and provided evidence that modifications of histone acetylation contributed to the alterations in gene expression caused by METH-induced behavioral sensitization.

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Distinct high resolution genome profiles of early onset and late onset colorectal cancer integrated with gene expression data identify candidate susceptibility loci

Molecular Cancer ◽

10.1186/1476-4598-9-100 ◽

2010 ◽

Vol 9 (1) ◽

pp. 100 ◽

Cited By ~ 54

Author(s):

Marianne Berg ◽

Trude H Agesen ◽

Espen Thiis-Evensen ◽

INFAC-study group [infac] ◽

Marianne A Merok ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

High Resolution ◽

Gene Expression Data ◽

Early Onset ◽

Late Onset ◽

Expression Data ◽

Susceptibility Loci ◽

Genome Profiles

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Uncovering Potential Therapeutic Targets in Colorectal Cancer by Deciphering Mutational Status and Expression of Druggable Oncogenes

Cancers ◽

10.3390/cancers11070983 ◽

2019 ◽

Vol 11 (7) ◽

pp. 983 ◽

Cited By ~ 3

Author(s):

Otília Menyhart ◽

Tatsuhiko Kakisaka ◽

Lőrinc Sándor Pongor ◽

Hiroyuki Uetake ◽

Ajay Goel ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Gene Expression Data ◽

Drug Targets ◽

Therapeutic Targets ◽

Independent Set ◽

Driver Mutations ◽

Expression Data ◽

Expression Levels ◽

Mutational Status

Background: Numerous driver mutations have been identified in colorectal cancer (CRC), but their relevance to the development of targeted therapies remains elusive. The secondary effects of pathogenic driver mutations on downstream signaling pathways offer a potential approach for the identification of therapeutic targets. We aimed to identify differentially expressed genes as potential drug targets linked to driver mutations. Methods: Somatic mutations and the gene expression data of 582 CRC patients were utilized, incorporating the mutational status of 39,916 and the expression levels of 20,500 genes. To uncover candidate targets, the expression levels of various genes in wild-type and mutant cases for the most frequent disruptive mutations were compared with a Mann–Whitney test. A survival analysis was performed in 2100 patients with transcriptomic gene expression data. Up-regulated genes associated with worse survival were filtered for potentially actionable targets. The most significant hits were validated in an independent set of 171 CRC patients. Results: Altogether, 426 disruptive mutation-associated upregulated genes were identified. Among these, 95 were linked to worse recurrence-free survival (RFS). Based on the druggability filter, 37 potentially actionable targets were revealed. We selected seven genes and validated their expression in 171 patient specimens. The best independently validated combinations were DUSP4 (p = 2.6 × 10−12) in ACVR2A mutated (7.7%) patients; BMP4 (p = 1.6 × 10−04) in SOX9 mutated (8.1%) patients; TRIB2 (p = 1.35 × 10−14) in ACVR2A mutated patients; VSIG4 (p = 2.6 × 10−05) in ANK3 mutated (7.6%) patients, and DUSP4 (p = 7.1 × 10−04) in AMER1 mutated (8.2%) patients. Conclusions: The results uncovered potentially druggable genes in colorectal cancer. The identified mutations could enable future patient stratification for targeted therapy.

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