Identification of prognosis-associated immune genes and exploration of immune cell infiltration in colorectal cancer

2020 ◽  
Vol 14 (14) ◽  
pp. 1353-1369
Author(s):  
Yan-Dong Miao ◽  
Jiang-Tao Wang ◽  
Yuan Yang ◽  
Xue-Ping Ma ◽  
Deng-Hai Mi
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Expression Profiles ◽  
Clinical Stage ◽  
The Cancer Genome Atlas ◽  
Immune Genes ◽  
Prognosis Model ◽  
Cancer Genome Atlas

Aim: To identify prognosis-related immune genes (PRIGs) and construct a prognosis model of colorectal cancer (CRC) patients for clinical use. Materials & methods: Expression profiles were obtained from The Cancer Genome Atlas database and identified differentially expressed PRIGs of CRC. Results: A prognostic model was conducted based on nine PRIGs. The risk score, based on prognosis model, was an independent prognostic predictor. Five PRIGs and risk score were significantly associated with the clinical stage of CRC and five immune cells related to the risk score. Conclusion: The risk score was an independent prognostic biomarker for CRC patients. The research excavated immune genes that were associated with survival and that could be potential biomarkers for prognosis and treatment for CRC patients.

scholarly journals Effects of Hypoxia in Intestinal Tumors on Immune Cell Behavior in the Tumor Microenvironment

2020 ◽  
Author(s):  
Luping Zhang ◽  
Shaokun Wang ◽  
Yachen Wang ◽  
Weidan Zhao ◽  
Yingli Zhang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Groups ◽  
Low Risk ◽  
Computational Method ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Behavior

Abstract Background: Imbalanced nutritional supply and demand in the tumor microenvironment often leads to hypoxia. The subtle interaction between hypoxia and immune cell behavior plays an important role in tumor occurrence and development. However, the functional relationship between hypoxia and the tumor microenvironment remains unclear. Therefore, we aimed to investigate the effect of hypoxia on the intestinal tumor microenvironment.Method: We extracted the names of hypoxia-related genes from the Gene Set Enrichment Analysis (GSEA) database and screened them for those associated with the prognosis of colorectal cancer, with the final list including ALDOB, GPC1, ALDOC, and SLC2A3. Using the sum of the expression levels of these four genes, provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the expression coefficients, we developed a hypoxia risk score model. Using the median risk score value, we divided the patients in the two databases into high- and low-risk groups.GSEA was used to compare the enrichment differences between the two groups.We used the CIBERSORT computational method to analyze immune cell infiltration.Finally,the correlation between these five genes and hypoxia was analyzed. Result: The prognosis of the two groups differed significantly, with a higher survival rate in the low-risk group than in the high-risk group.We found that the different risk groups were enriched by immune-related and inflammatory pathways. We identified activated CD4 memory T cells and M0 macrophages in TCGA and GEO databases and found that CCL2/4/5, CSF1, and CX3CL1 contributed toward the increased infiltration rate of these immune cell types. Finally, we observed a positive correlation between the five candidate genes’ expression and the risk of hypoxia, with significant differences in the level of expression of each of these genes between patient risk groups.Conclusion: Overall, our data suggest that hypoxia is associated with the prognosis and rate of immune system infiltration in patients with colorectal cancer. This finding may improve immunotherapy for colorectal cancer.

scholarly journals Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

2020 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Fangli Yan ◽  
Yuhua Zheng
Keyword(s):  
Endometrial Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Gene Signature ◽  
Cancer Genome ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Uterine Corpus ◽  
Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis especially when at an advanced stage. In the present study, we explored the potential of an immune-related gene signature to predict overall survival in UCEC patients.Methods: We analyzed expression data of 616 UCEC patients from The Cancer Genome Atlas database and the International Cancer Genome Consortium as well as immune genes from the ImmPort database and identified the signature. We constructed a transcription factor regulatory network based on Cistrome databases and performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using Cox regression analysis then constructed a prognostic model. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.Results: Results indicated that the immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic model revealed a ten-gene prognosis signature , comprising PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC . This can be used as an independent tool to predict the prognosis of UCEC owing to the observed risk-score. In addition, levels of B cells and neutrophils were significantly correlated with the patient's risk score, and the expression of ten genes is associated with immune cell infiltrates.Conclusions: In summary, we present a 10-gene signature with the potential to predict the prognosis of UCEC. This is expected to guide future development of individualized treatment approaches.

scholarly journals Regulation of Long Non-coding RNA KCNQ1OT1 Network in Colorectal Cancer Immunity

2021 ◽  
Vol 12 ◽  
Author(s):  
Junjie Liu ◽  
Wei Lv ◽  
Shuling Li ◽  
Jingwen Deng
Keyword(s):  
Colorectal Cancer ◽  
Immune Cell ◽  
Cytotoxic T Cells ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Cerna Network ◽  
Non Coding Rna ◽  
Immune Cell Subsets ◽  
Cancer Genome Atlas ◽  
Cancer Tissues

Over the past few decades, researchers have become aware of the importance of non-coding RNA, which makes up the vast majority of the transcriptome. Long non-coding RNAs (lncRNAs) in turn constitute the largest fraction of non-coding transcripts. Increasing evidence has been found for the crucial roles of lncRNAs in both tissue homeostasis and development, and for their functional contributions to and regulation of the development and progression of various human diseases such as cancers. However, so far, only few findings with regards to functional lncRNAs in cancers have been translated into clinical applications. Based on multiple factors such as binding affinity of miRNAs to their lncRNA sponges, we analyzed the competitive endogenous RNA (ceRNA) network for the colorectal cancer RNA-seq datasets from The Cancer Genome Atlas (TCGA). After performing the ceRNA network construction and survival analysis, the lncRNA KCNQ1OT1 was found to be significantly upregulated in colorectal cancer tissues and associated with the survival of patients. A KCNQ1OT1-related lncRNA-miRNA-mRNA ceRNA network was constructed. A gene set variation analysis (GSVA) indicated that the expression of the KCNQ1OT1 ceRNA network in colorectal cancer tissues and normal tissues were significantly different, not only in the TCGA-COAD dataset but also in three other GEO datasets used as validation. By predicting comprehensive immune cell subsets from gene expression data, in samples grouped by differential expression levels of the KCNQ1OT1 ceRNA network in a cohort of patients, we found that CD4+, CD8+, and cytotoxic T cells and 14 other immune cell subsets were at different levels in the high- and low-KCNQ1OT1 ceRNA network score groups. These results indicated that the KCNQ1OT1 ceRNA network could be involved in the regulation of the tumor microenvironment, which would provide the rationale to further exploit KCNQ1OT1 as a possible functional contributor to and therapeutic target for colorectal cancer.

scholarly journals Construction of a Risk Score Prognosis Model for Hepatocellular Carcinoma Based on Pyroptosis Related Genes

2021 ◽  
Author(s):  
Xin-yu Li ◽  
Li-xin Su ◽  
Ming-zhe Wen ◽  
Jian-xiong You ◽  
xi-tao Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Model Based ◽  
Prognosis Model ◽  
Cancer Genome Atlas

Abstract Background: In this study, a prognostic model based on pyroptosis-related genes was established to predict overall survival (OS) in patients with hepatocellular carcinoma(HCC).Methods: The gene expression data and clinical information of HCC patients were acquired from The Cancer Genome Atlas (TCGA). Using bioinformatics analysis, this predictive signature was constructed and validated. The performance of predictive signature was assessed by the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). Results: A total of 3 pyroptosis-related genes (BAK1, GSDME, and NOD2) were used to construct a survival prognostic model, and experimental validation performed using an experimental cohort. The prognosis model exhibited good performance based on the AUC (AUC: 0.826 at 1 years, 0.796 at 3 years, 0.867 at 5 years). The calibration plots showed excellent calibration.Conclusion: In this study, a novel prognostic model based on three pyroptosis-related genes is constructed and used to predict the prognosis of HCC patients. The model can accurately and conveniently predict the 1- 3-and 5-year OS of HCC patients.

Downregulation of ACE2 is associated with advanced pathological features and poor prognosis in clear cell renal cell carcinoma

2021 ◽  
Author(s):  
Tianjiao Wang ◽  
Fang Xie ◽  
Yun-Hui Li ◽  
Bin Liang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Immune Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Histological Grade ◽  
Clinical Stage ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma

Aims: The aim of this study was to explore the alteration in ACE2 expression and correlation between ACE2 expression and immune infiltration in clear cell renal cell carcinoma (ccRCC). Methods: The authors first analyzed the expression profiles and prognostic value of ACE2 in ccRCC patients using The Cancer Genome Atlas public database. The authors used ESTIMATE and CIBERSORT algorithms to analyze the correlation between ACE2 expression and tumor microenvironment in ccRCC samples. Results: ACE2 was correlated with sex, distant metastasis, clinical stage, tumor T stage and histological grade. Moreover, downregulation of ACE2 was correlated with unfavorable prognosis. In addition, ACE2 expression was associated with different immune cell subtypes. Conclusion: The authors' analyses suggest that ACE2 plays an important role in the development and progression of ccRCC and may serve as a potential prognostic biomarker in ccRCC patients.

scholarly journals CpG-methylation-based risk score predicts progression in colorectal cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 605-615 ◽  
Author(s):  
Yao Deng ◽  
Hao Wan ◽  
Jianbo Tian ◽  
Xiang Cheng ◽  
Meilin Rao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Free Interval ◽  
Future Clinical Practice ◽  
Risk Of Progression ◽  
Precision Therapy ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: To identify patients with colorectal cancer (CRC) who are at a truly higher risk of progression, which is key for individualized approaches to precision therapy. Materials & methods: We developed a predictor associated with progression-free interval (PFI) using The Cancer Genome Atlas CRC methylation data. Results: The risk score was associated with PFI in the whole cohort (p < 0.001). A nomogram consisting of the risk score and other significant clinical features was generated to predict the 3- and 5-year PFI in the whole set (area under the curve: 0.79 and 0.71, respectively). Conclusion: The risk score based on 23 DNA-methylation sites may serve as the basis for improved prediction of progression in patients with CRC in future clinical practice.

scholarly journals Prognostic implications of metabolism-associated gene signatures in colorectal cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9847
Author(s):  
Yandong Miao ◽  
Qiutian Li ◽  
Jiangtao Wang ◽  
Wuxia Quan ◽  
Chen Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinicopathological Feature ◽  
Metabolic Genes ◽  
Cox Analysis

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

scholarly journals Epithelial-Mesenchymal Transformation Related Genomic Analysis Reveals Prognostic And Immunogenic Significance In Colorectal Cancer

2021 ◽  
Author(s):  
Yangyang Yin ◽  
Ying Liu ◽  
Yun Pan ◽  
Tianyu Yu ◽  
Bin Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Treatment Response ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Mesenchymal Transition ◽  
Prognosis Model ◽  
Mesenchymal Transformation

Abstract Background: Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Epithelial-mesenchymal transition (EMT) has been viewed to play a vital role in immune regulation and treatment response shaping. EMT is associated with an invasive or metastatic phenotype in CRC and is also related to patient prognosis and treatment responsiveness. We aimed to explore an EMT gene panel with potential application in patient classification and precise treatment and assist clinicians to generate individualized immunotherapeutic strategies for CRC patients.Methods: TCGA, GEO, STRING, TRRUST, TragetScan, miRTarBase, miRDB, cBioPortal, StarBase databases were utilized in this study.Results: In this study, EMT factors were screened in three different ways(EMT factors, EMT-related pathways, EMT genotyping genes).And the prognosis-related modules were screened by the WGCNA method. Then, Cox single factor regression analysis was performed on the module hub gene, combined with multivariate Cox regression, the prognosis model was established, and the risk score of each sample was calculated. Then the samples were divided into high-risk samples and low-risk samples according to the risk score, and the differences in immune cell infiltration, mutation, CNV, clinical characteristics between high-risk and low-risk samples were compared. The risk model can effectively predict the prognosis of samples, which is verified by two external data, and it can also effectively predict the prognosis of CRC samples in the other two digestive tract cancers (liver cancer and gastric cancer) and has a good indication for the effect of chemotherapy treatment response and immunotherapy. Conclusion: The prognosis model can effectively predict the prognosis of samples and may be an effective tool for treatment guidance in CRC patients.

scholarly journals Identification of Hub Genes for Controlling Cancer Stem Cell Characteristics in Colorectal Cancer by Bioinformatics Analysis

2021 ◽  
Author(s):  
Xiang Li ◽  
Shuoyang Huang ◽  
Chao Yang ◽  
Yongbin Zheng
Keyword(s):  
Colorectal Cancer ◽  
Stem Cell ◽  
Survival Data ◽  
Prognostic Model ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Immune Microenvironment ◽  
Hub Genes ◽  
Normal Tissues ◽  
Cancer Genome Atlas

Abstract Background Cancer stem cells (CSCs), which are capable of infinite proliferation and self-renewal, play a crucial role in the occurrence and development of colorectal cancer (CRC). The study of the expression characteristics of CRC stem cell-related genes and their interaction with the immune microenvironment may contribute to CRC treatment. Results In order to explore the hub genes that regulate the stemness characteristics of CRC, we obtained gene expression values of the Cancer Genome Atlas (TCGA), stemness indices (mRNAsi), and corresponding survival data from UCSC Xena Browser. Differentially expressed genes (DEGs) were identified in cancer and normal tissues. Then we screened 2 modules and 210 mRNAsi-related genes from 4,941 DEGs by weighted gene co-expression network analysis. A prognostic model including ten genes (VCAN, SPARC, COL12A1, THBS2, COL1A2, COL5A1, TAGLN, DCN, MYH11, CDH11) was constructed using protein interaction networks and LASSO regression. We also evaluated the relationship between cancer stemness and immune response and found there was a strong correlation between each other. Conclusions Our study establishes a prognostic model associated with CSCs and reveals the association between mRNAsi and the tumor immune microenvironment, which is useful for the targeted therapy of CRC.

Construction prognosis model based on autophagy-related gene signatures in hepatocellular carcinoma

2020 ◽  
Vol 14 (13) ◽  
pp. 1229-1242
Author(s):  
Jiangtao Wang ◽  
Yandong Miao ◽  
Juntao Ran ◽  
Yuan Yang ◽  
Quanlin Guan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Clinical Stage ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Prognosis Model ◽  
Clinicopathological Significance ◽  
Autophagy Pathway

Aim: To develop robust and accurate prognostic biomarkers to help clinicians optimize therapeutic strategies. Materials & methods: Differentially prognosis-related autophagy genes were identified by bioinformatics analysis method. Results: Seven prognosis-related autophagy genes were more significantly related to the prognosis of hepatocellular carcinoma (HCC). Functional enrichment analysis demonstrated that these genes were mainly enriched in the autophagy pathway. BIRC5, HSPB8 and TMEM74 exhibited significant prognostic value for HCC. Besides, the risk score and BIRC5 have significant significance with clinicopathological significance of HCC. Conclusion: The research has identified a number of prognosis-related autophagy genes that associated with the survival and clinical stage of HCC. In addition, the prognostic model can be used to calculate the patient’s risk score and these prognosis-related autophagy genes might serve as therapeutic targets.

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