Interferon regulatory factor family influences tumor immunity and prognosis of patients with colorectal cancer

Abstract Background Since interferon regulatory factor (IRF) family functions in immune response to viral infection, its role in colorectal cancer (CRC) has not been inspected before. This study tries to investigate members of IRF family using bioinformatics approaches in aspect of differential expressions, biological function, tumor immune infiltration and clinical prognostic value for patients with CRC. Methods Transcriptome profiles data, somatic mutations and clinical information of CRC were obtained from COAD/READ dataset of The Cancer Genome Atlas (TCGA) as a training set. Gene expression data (GSE17536 and GSE39582) were downloaded from the Gene Expression Omnibus as a validating set. A random forest algorithm was used to score the risk for every case. Analyzing gene and function enrichment, constructing protein–protein interaction and noncoding RNA network, identifying hub-gene, characterizing tumor immune infiltration, evaluating differences in tumor mutational burden (TMB) and sensitivity to chemotherapeutics or immunotherapy were performed by a series of online tools and R packages. Immunohistochemical (IHC) examinations were carried out validation in tissue samples. Results Principal-component analysis (PCA) suggested that the transcript expression levels of nine members of IRF family differed between normal colorectum and CRC. The risk score constructed by IRF family not only acted as an independent factor for predicting survival in CRC patients with different biological processes, signaling pathways and TMB, but also indicated different immunotherapy response with diverse immune and stromal cells infiltration. IRF3 and IRF7 were upregulated in CRC and suggested a shorter survival time in patients with CRC. Differentially expressed members of IRF family exhibited varying degrees of immune cell infiltration. IHC analysis showed a positive association between IRF3 and IRF7 expression and tumor-infiltrating immune cells, including CD4+ T cell and CD68+ macrophages. Conclusions On account of differential expression, IRF family members can help to predict both response to immunotherapy and clinical prognosis of patients with CRC. Our bioinformatic investigation not only gives a preliminary picture of the genetic features as well as tumor microenvironment, but it may provide a clue for further experimental exploration and verification on IRF family members in CRC.

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Interferon regulatory factor-3 activation, hepatic interferon-stimulated gene expression, and immune cell infiltration in hepatitis C virus patients

Hepatology ◽

10.1002/hep.22076 ◽

2008 ◽

Vol 47 (3) ◽

pp. 799-809 ◽

Cited By ~ 78

Author(s):

Daryl T.-Y. Lau ◽

Penny Mar Fish ◽

Mala Sinha ◽

David M. Owen ◽

Stanley M. Lemon ◽

...

Keyword(s):

Gene Expression ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Immune Cell ◽

Interferon Regulatory Factor ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Regulatory Factor ◽

Interferon Regulatory Factor 3

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Comprehensive analysis of ceRNA networks reveals prognostic lncRNAs related to immune infiltration in colorectal cancer

BMC Cancer ◽

10.1186/s12885-021-07995-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jingyi Chen ◽

Yuxuan Song ◽

Mei Li ◽

Yu Zhang ◽

Tingru Lin ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Target Genes ◽

Immune Cell ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cerna Network

Abstract Background Competing endogenous RNA (ceRNA) represents a class of RNAs (e.g., long noncoding RNAs [lncRNAs]) with microRNA (miRNA) binding sites, which can competitively bind miRNA and inhibit its regulation of target genes. Increasing evidence has underscored the involvement of dysregulated ceRNA networks in the occurrence and progression of colorectal cancer (CRC). The purpose of this study was to construct a ceRNA network related to the prognosis of CRC and further explore the potential mechanisms that affect this prognosis. Methods RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs), and a prognosis-related ceRNA network was constructed based on DElncRNA survival analysis. Subsequently, pathway enrichment, Pearson correlation, and Gene Set Enrichment Analysis (GSEA) were performed to determine the function of the genes in the ceRNA network. Gene Expression Profiling Interactive Analysis (GEPIA) and immunohistochemistry (IHC) were also used to validate differential gene expression. Finally, the correlation between lncRNA and immune cell infiltration in the tumor microenvironment was evaluated based on the CIBERSORT algorithm. Results A prognostic ceRNA network was constructed with eleven key survival-related DElncRNAs (MIR4435-2HG, NKILA, AFAP1-AS1, ELFN1-AS1, AC005520.2, AC245884.8, AL354836.1, AL355987.4, AL591845.1, LINC02038, and AC104823.1), 54 DEmiRNAs, and 308 DEmRNAs. The MIR4435-2HG- and ELFN1-AS1-associated ceRNA subnetworks affected and regulated the expression of the COL5A2, LOX, OSBPL3, PLAU, VCAN, SRM, and E2F1 target genes and were found to be related to prognosis and tumor-infiltrating immune cell types. Conclusions MIR4435-2HG and ELFN1-AS1 are associated with prognosis and tumor-infiltrating immune cell types and could represent potential prognostic biomarkers or therapeutic targets in colorectal carcinoma.

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CRYAB predicts clinical prognosis and is associated with immunocyte infiltration in colorectal cancer

PeerJ ◽

10.7717/peerj.12578 ◽

2021 ◽

Vol 9 ◽

pp. e12578

Author(s):

Junsheng Deng ◽

Xiaoli Chen ◽

Ting Zhan ◽

Mengge Chen ◽

Xisheng Yan ◽

...

Keyword(s):

Colorectal Cancer ◽

T Cells ◽

Immune Cell ◽

Malignant Tumors ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Gene Marker ◽

Immune Cell Infiltration ◽

Clinical Prognosis ◽

Advanced Tumor

Background αB-Crystallin (CRYAB) is differentially expressed in various tumors. However, the correlation between CRYAB and immune cell infiltration in colorectal cancer (CRC) remains unclear. Materials & Methods Kaplan–Meier survival curves in The Cancer Genome Atlas (TCGA) were used to evaluate the relationship between CRYAB expression and both overall survival and progression-free survival. The relationships between CRYAB expression and infiltrating immune cells and their corresponding gene marker sets were examined using the TIMER database. Results The expression of CRYAB was lower in CRC tumor tissues than in normal tissues (P < 0.05). High CRYAB gene expression and high levels of CRYAB gene methylation were correlated with high-grade malignant tumors and more advanced tumor, nodes and metastasis (TNM) cancer stages. In addition, in colorectal cancer, there was a positive correlation between CRYAB expression and immune infiltrating cells including neutrophils, macrophages, CD8 + T cells, and CD4 + T cells, as well as immune-related genes including CD2, CD3D, and CD3E. Methylation sites such as cg13084335, cg15545878, cg13210534, and cg15318568 were positively correlated with low expression of CRYAB. Conclusion Because CRYAB likely plays an important role in immune cell infiltration, it may be a potential tumor-suppressor gene in CRC and a potential novel therapeutic target and predictive biomarker for colorectal cancer (CRC).

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Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.552331 ◽

2021 ◽

Vol 11 ◽

Author(s):

Henrik Failmezger ◽

Natalie Zwing ◽

Achim Tresch ◽

Konstanty Korski ◽

Fabian Schmich

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Computational Method ◽

The Cancer Genome Atlas ◽

Point Pattern Analysis ◽

Point Pattern ◽

Base Line ◽

Tumor Infiltration ◽

Durable Response ◽

Immune Infiltration

Cancer immunotherapy has led to significant therapeutic progress in the treatment of metastatic and formerly untreatable tumors. However, drug response rates are variable and often only a subgroup of patients will show durable response to a treatment. Biomarkers that help to select those patients that will benefit the most from immunotherapy are thus of crucial importance. Here, we aim to identify such biomarkers by investigating the tumor microenvironment, i.e., the interplay between different cell types like immune cells, stromal cells and malignant cells within the tumor and developed a computational method that determines spatial tumor infiltration phenotypes. Our method is based on spatial point pattern analysis of immunohistochemically stained colorectal cancer tumor tissue and accounts for the intra-tumor heterogeneity of immune infiltration. We show that, compared to base-line models, tumor infiltration phenotypes provide significant additional support for the prediction of established biomarkers in a colorectal cancer patient cohort (n = 80). Integration of tumor infiltration phenotypes with genetic and genomic data from the same patients furthermore revealed significant associations between spatial infiltration patterns and common mutations in colorectal cancer and gene expression signatures. Based on these associations, we computed novel gene signatures that allow one to predict spatial tumor infiltration patterns from gene expression data only and validated this approach in a separate dataset from the Cancer Genome Atlas.

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Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

BMC Medical Genomics ◽

10.1186/s12920-021-00923-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Guoliang Jia ◽

Zheyu Song ◽

Zhonghang Xu ◽

Youmao Tao ◽

Yuanyu Wu ◽

...

Keyword(s):

Gene Expression ◽

Cutaneous Melanoma ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Validation Dataset ◽

Survival Prognosis ◽

Gene Markers ◽

Clinical Prognosis ◽

Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

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Interaction between Host MicroRNAs and the Gut Microbiota in Colorectal Cancer

mSystems ◽

10.1128/msystems.00205-17 ◽

2018 ◽

Vol 3 (3) ◽

Cited By ~ 35

Author(s):

Ce Yuan ◽

Michael B. Burns ◽

Subbaya Subramanian ◽

Ran Blekhman

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Gut Microbiota ◽

Mirna Expression ◽

Gut Microbiome ◽

Noncoding Rna ◽

Microbial Community Composition ◽

Host Cells ◽

Microbiome Composition ◽

Small Noncoding Rna

ABSTRACT Although variation in gut microbiome composition has been linked with colorectal cancer (CRC), the factors that mediate the interactions between CRC tumors and the microbiome are poorly understood. MicroRNAs (miRNAs) are known to regulate CRC progression and are associated with patient survival outcomes. In addition, recent studies suggested that host miRNAs can also regulate bacterial growth and influence the composition of the gut microbiome. Here, we investigated the association between miRNA expression and microbiome composition in human CRC tumor and normal tissues. We identified 76 miRNAs as differentially expressed (DE) in tissue from CRC tumors and normal tissue, including the known oncogenic miRNAs miR-182, miR-503, and mir-17~92 cluster. These DE miRNAs were correlated with the relative abundances of several bacterial taxa, including Firmicutes , Bacteroidetes , and Proteobacteria . Bacteria correlated with DE miRNAs were enriched with distinct predicted metabolic categories. Additionally, we found that miRNAs that correlated with CRC-associated bacteria are predicted to regulate targets that are relevant for host-microbiome interactions and highlight a possible role for miRNA-driven glycan production in the recruitment of pathogenic microbial taxa. Our work characterized a global relationship between microbial community composition and miRNA expression in human CRC tissues. IMPORTANCE Recent studies have found an association between colorectal cancer (CRC) and the gut microbiota. One potential mechanism by which the microbiota can influence host physiology is through affecting gene expression in host cells. MicroRNAs (miRNAs) are small noncoding RNA molecules that can regulate gene expression and have important roles in cancer development. Here, we investigated the link between the gut microbiota and the expression of miRNA in CRC. We found that dozens of miRNAs are differentially regulated in CRC tumors and adjacent normal colon and that these miRNAs are correlated with the abundance of microbes in the tumor microenvironment. Moreover, we found that microbes that have been previously associated with CRC are correlated with miRNAs that regulate genes related to interactions with microbes. Notably, these miRNAs likely regulate glycan production, which is important for the recruitment of pathogenic microbial taxa to the tumor. This work provides a first systems-level map of the association between microbes and host miRNAs in the context of CRC and provides targets for further experimental validation and potential interventions.

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Identification of a gene set correlated with immune status in ovarian cancer by transcriptome-wide data mining

10.21203/rs.3.rs-17604/v1 ◽

2020 ◽

Author(s):

Lili Fan ◽

Han Lei ◽

Ying Lin ◽

Zhengwei Zhou ◽

Guang Shu ◽

...

Keyword(s):

Ovarian Cancer ◽

Immune Cell ◽

Good Prognosis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Survival Prognosis ◽

Immune Activity ◽

Gene Set ◽

Immune Infiltration ◽

Keywords Ovarian Cancer

Abstract Background : Ovarian cancer (OC) is a serious tumor disease in gynecology. Many papers have reported that high tumor mutational burden (TMB) can generate many neoantigens to result in a higher degree of tumor immune infiltration, so our study aims to predict the key molecules in OC immunotherapy by combined TMB with immunoactivity-related gene. Method: We divided OC cases into two groups: the low & high TMB group hinged on the somatic mutation data from the Cancer Genome Atlas (TCGA). We also used single-sample gene set enrichment analysis (ssGSEA) scores of immune cell types to conduct unsupervised clustering of OC patients in the TCGA cohort and some of them were defined as the low & high immunity group. Besides, to further understand the function of these genes, we conducted Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interaction network, survival prognosis analysis and immune infiltration analysis. Finally, the effects on prognosis and immunotherapy in OC patients were explored by the Group on Earth Observations verification the patients' responses to immunotherapy. Results: We found that the higher the TMB was associated with the higher OC grades. Moreover, both high TMB and high immunity were significantly correlated with a good prognosis of OC. Then, 14 up-regulated differential expression genes (Up-DEGs) that were closely related to the prognosis of OC patients were screened according to the high TMB group and the high immunity group. Next, pathway analysis revealed that Up-DGEs were mainly involved in immune response and T cell proliferation. Finally, four genes had a good prognosis and were validated in the GEO dataset which included CXCL13, FCRLA, PLA2G2D, and MS4A1. We also identified that four genes had a good prognosis in melanoma patients treated with anti-PD-L1 and anti-CTLA-4 in the TIDE database. Conclusion: High TMB can promote immune cell infiltration and increases immune activity. And our analysis also demonstrated that the higher the TMB, the higher the immune activity, the better the prognosis of OC. Altogether, we found that CXCL13, FCRLA, PLA2G2D, and MS4A1 may be biomarkers for OC immunotherapy. Keywords: ovarian cancer, TMB, immune cells infiltration, survival prognosis.

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Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Bioscience Reports ◽

10.1042/bsr20201087 ◽

2020 ◽

Vol 40 (12) ◽

Author(s):

Dafeng Xu ◽

Yu Wang ◽

Kailun Zhou ◽

Jincai Wu ◽

Zhensheng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Immune Cells ◽

Prognostic Value ◽

Tumor Tissue ◽

Clinical Stage ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

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