scholarly journals Prognostic Risk Model of Immune-Related Genes in Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yucheng Qian ◽  
Jingsun Wei ◽  
Wei Lu ◽  
Fangfang Sun ◽  
Maxwell Hwang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Assessment Model ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Tumor Immune Microenvironment ◽  
Risk Scoring ◽  
Immune Related Genes

PurposeWe focused on immune-related genes (IRGs) derived from transcriptomic studies, which had the potential to stratify patients’ prognosis and to establish a risk assessment model in colorectal cancer.SummaryThis article examined our understanding of the molecular pathways associated with intratumoral immune response, which represented a critical step for the implementation of stratification strategies toward the development of personalized immunotherapy of colorectal cancer. More and more evidence shows that IRGs play an important role in tumors. We have used data analysis to screen and identify immune-related molecular biomarkers of colon cancer. We selected 18 immune-related prognostic genes and established models to assess prognostic risks of patients, which can provide recommendations for clinical treatment and follow-up. Colorectal cancer (CRC) is a leading cause of cancer-related death in human. Several studies have investigated whether IRGs and tumor immune microenvironment (TIME) could be indicators of CRC prognoses. This study aimed to develop an improved prognostic signature for CRC based on IRGs to predict overall survival (OS) and provide new therapeutic targets for CRC treatment. Based on the screened IRGs, the Cox regression model was used to build a prediction model based on 18-IRG signature. Cox regression analysis revealed that the 18-IRG signature was an independent prognostic factor for OS in CRC patients. Then, we used the TIMER online database to explore the relationship between the risk scoring model and the infiltration of immune cells, and the results showed that the risk model can reflect the state of TIME to a certain extent. In short, an 18-IRG prognostic signature for predicting CRC patients’ survival was firmly established.

Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

2021 ◽  
Author(s):  
Jianxing Ma ◽  
Chen Wang
Keyword(s):  
Colorectal Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Nonnegative Matrix ◽  
Test Group ◽  
Progression Free Survival ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

scholarly journals Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiang Zhou ◽  
Keying Zhang ◽  
Fa Yang ◽  
Chao Xu ◽  
Jianhua Jiao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

scholarly journals An immune-associated gene prognostic index risk model for stomach adenocarcinoma

2021 ◽  
Author(s):  
Weijie Xue ◽  
Bingzi Dong ◽  
Yixiu Wang ◽  
Yuwei Xie ◽  
Qingkai Xue ◽  
...  
Keyword(s):  
T Cells ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Prognostic Index ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma ◽  
Immune Related Genes

Abstract Background Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempt to construct a valid immune-associated gene prognostic index risk model which could predict the survival of HCC patients and the efficacy of immune check point inhibitors (ICIs) treatment. Methods The transcriptome, clinical and gene mutational data were obtained from the TCGA database. And immune-related genes were downloaded from the ImmPort and InnateDB databases. Functional and enrichment analysis was performed to identify the potential molecular function and mechanism of these differentially expressed immune-associated genes. And then candidates genes related to overall survival (OS) of STAD was obtained by weighted gene co-expression network analysis (WGCNA). Next, the immune prognostic risk model was constructed via multivariate Cox regression analysis and verified with GEO STAD cohort. Afterwards, the association between the risk model and the immune characteristics and was estimated. Finally, the correlation between the risk model and efficacy of ICIs therapy. Results A total of 493 immune-related genes were identified to enriched in function associated to immune response as well as in immune and tumor-related pathways. Based on the cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on 8 genes (RNASE2, CGB5, INHBE, PTGER3, CTLA4, DUSP1, APOA1 and CD36). Patients were divided into two subsets according to risk score. Patients in low risk set had a better OS than those in high. In the low risk set, there were more CD8 T cells, activated memory CD4 T cells, follicular helper T cells and M1 macrophages, while monocytes, M2 macrophages, eosinophils and neutrophils were more plentiful in the high. And patients in the low risk set were more sensitive to ICIs therapy. Conclusion The IAGPI risk model can precisely predict prognosis, reflect tumor immune microenvironment and predict the efficacy of ICIs therapy in STAD patients.

scholarly journals Identification of a Potential Prognostic Signature based on Immune-related Genes in Primary Glioblastoma

2021 ◽  
Author(s):  
Zhaoming Zhou ◽  
Mingyao Lai ◽  
Jiayin Yu ◽  
Jiangfen Zhou ◽  
Qingjun Hu ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Cox Regression ◽  
Sequencing Analysis ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Prognosis Prediction ◽  
Immune Related Genes ◽  
Genome Atlas

Abstract Background: Glioblastoma (GBM) is a common primary brain tumor with a high incidence in adults with malignant and fast-growing biological characteristics. In this study, we explore the immune-related prognosis markers of GBM at the mRNA level.Methods: The sequencing data and clinical information of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). The differentially expressed genes (DEGs) were calculated between normal tissues from the Genotype-Tissue Expression (GTEx) database and tumor tissues from TCGA and CGGA. We obtained immune genes from the ImmProt database. The intersection of DEGs and immune genes were defined as the immune-related differential genes (IR-DEGs), based on which, survival associated IR-DEGs were determined by multivariate Cox regression analysis. The survival risk score (SRS) was determined for each sample with the top 6 prognostic associated IR-DEGs. One-year, two-year, and three-year potential survival were predicted by the prognosis prediction model established by multivariate and univariate Cox regression. In addition, we performed CIBERSORT in GBM patients with samples from TCGA cohort; association analysis was performed with prognostic IR-DEGs and immune cells. Furthermore, the influence of prognostic IR-DEGs on the brain tumor microenvironment (TME) was validated in single-cell sequencing analysis.Results: We found 301 IR-DRGs in GBM primary tumor compared with normal tissue, and 19 of them could predict the overall survival (OS) more accurately in GBM patients. Six IR-DEGs (PLAUR, TNFSF14, CTSB, SOCS3, PTX3, and FCGR2B) were selected to construct the SRS, with which, GBM patients were divided into two different groups which combined with high and low risk. The SRS was found to be an independent prognostic factor for GBM and could predict GBM patients’ possible survival with an acceptable efficiency. Moreover, the expression of 6 IR-DEGs and their co-expressed IR-DEGs could influence TME and were associated with GBM prognosis.Conclusions: This study identified a potential immune prognostic signature of glioblastoma, which could enhance the prognosis prediction ability for GBM patients. The immune-related-genes in the TME could potentially benefit the immunotherapy development for GBM patients.

scholarly journals A Novel Assessment Model Based on Molecular Subtypes of Hypoxia-Related LncRNAs for Prognosis of Bladder Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xianwu Chen ◽  
Yan Zhang ◽  
Feifan Wang ◽  
Xuejian Zhou ◽  
Qinghe Fu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Therapy ◽  
Assessment Model ◽  
Immune Checkpoints ◽  
Cox Regression Analysis ◽  
Model Based

Hypoxia is a common feature in various tumors that regulates aggressiveness. Previous studies have demonstrated that some dysregulated long non-coding RNAs (lncRNAs) are correlated with tumor progression, including bladder cancer (BCa). However, the prognostic effect of hypoxia-related lncRNAs (HRLs) and their clinical relevance, as well as their regulatory effect on the tumor immune microenvironment, are largely unknown in BCa. A co-expression analysis between hypoxia genes and lncRNA expression, which was downloaded from the TCGA database, was performed to identify HRLs. Univariate Cox regression analysis was performed to select the most desirable lncRNAs for molecular subtype, and further LASSO analysis was performed to develop a prognostic model. This molecular subtype based on four HRLs (AC104653, AL136084, AL139393, and LINC00892) showed good performance in the tumor microenvironment and tumor mutation burden. The prognostic risk model suggested better performance in predicting BCa patients’ prognosis and obtained a close correlation with clinicopathologic features. Furthermore, four of five first-line clinical chemotherapies showed different sensitivities to this model, and nine immune checkpoints showed different expression in the molecular subtypes or the risk model. In conclusion, this study indicates that this molecular subtype and risk model based on HRLs may be useful in improving the prognostic prediction of BCa patients with different clinical situations and may help to find a useful target for tumor therapy.

scholarly journals A New Prognostic and Therapeutic Immune-related Risk Signature of Colorectal Cancer

2021 ◽  
Author(s):  
Yuan Li ◽  
Hao Huang ◽  
Jun Feng ◽  
Yulan Zhu ◽  
Tianwei Jiang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
R Package ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Related Risk ◽  
Set Up ◽  
Microsatellite Stability

Abstract BackgroundAlthough some advanced colorectal cancer (CRC) patients could select immunotherapy, but still most microsatellite stability (MSS) CRC patients did not respond. Our present study aims to set up a novel system for prognostic prediction and immunotherapeutic responsiveness for MSS CRC patients.MethodsUnivariable Cox regression survival analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were performed to identify prognostic genes and establish immune risk signatures. Multivariate Cox regression analysis was performed to verify whether these clinical features could predict prognosis. R package was used to analyze the relationship between the immune-related risk model and these immune cells, effector molecules, and immune checkpoints.ResultsWe constructed an immune-related signature and verified its predictive capability. Immune-related signature included 12 differentially expressed IRGs (12 DE IR MSSGs), including CXCL1, CD36, FABP4, MS4A2, NRG1, VGF, GRP, HDC, XCL1, NGF, MAGEA1, and IL13. The signature consisting of 12 DE IR MSSGs was an independent and effective prognostic factor for the overall survival of CRC patients. In addition, the signature consisting of 12 DE IR MSSGs reflected the infiltration characteristics of different immunocytes in tumor immune microenvironment. The signature consisting of 12 DE IR MSSGs also had a significant correlation with immune checkpoint molecules.

Development of a prognostic model based on an immunogenomic landscape analysis of colorectal cancer

2020 ◽  
Author(s):  
HJ Li ◽  
YL Wang ◽  
L Ming ◽  
XQ Guo ◽  
YL Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Patient Needs ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Screening and therapeutic programs for colorectal cancer (CRC) are invasive or not effective and unable to meet patient needs. Major advances in immunogenomics may change this status but need more exploration. Differentially expressed genes and immune-related genes (IRGs) were identified by computational methods. A prognostic model was established and validated based on survival-related IRGs via stepwise multivariate Cox regression analysis. Nine IRGs were selected and identified as survival-related genes. A 7-gene prognostic model could offer a preliminary and valid determination of risk in CRC patients. The area under the curve of the receiver operating characteristic was 0.672. The 7-gene prognostic model might be used as a novel prognostic tool in CRC patients.

scholarly journals Identification of Prognostic Immune-Related Genes in Pancreatic Adenocarcinoma and Establishment of a Prognostic Nomogram: A Bioinformatic Study

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Guolin Wu ◽  
Zhenfeng Deng ◽  
Zongrui Jin ◽  
Jilong Wang ◽  
Banghao Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Evaluation ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Background. The prognosis of pancreatic adenocarcinoma (PAAD) is extremely poor and has not been improved. Thus, an effective method to assess the prognosis of patients must be established to improve their survival rate. Method. This study investigated immune-related genes that could be used as potential therapeutic targets for PAAD. Level 3 gene expression data from the PAAD cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. For validation, other PAAD datasets (DSE62452) were downloaded from the Gene Expression Omnibus (GEO) database. The PAAD datasets from TCGA and GEO were used to screen immune-related genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping immune-related genes of the two datasets were identified. Coexpression networks of the immune-related genes were constructed. Results. A signature of three immune-related genes (CKLF, ERAP2, and EREG) was identified in patients with PAAD. The signature could be used to divide the patients with PAAD into high- and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of PAAD. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model based on the clinical characteristics and risk score of the TCGA PAAD dataset. The accuracy of the prognostic signature was further evaluated through functional evaluation and principal component analysis. Conclusions. The results indicated that the signature of three immune-related genes had excellent predictive value for PAAD. These findings might help improve personalized treatment and medical decisions.

scholarly journals Development of a novel immune-related genes prognostic signature for osteosarcoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zuo-long Wu ◽  
Ya-jun Deng ◽  
Guang-zhi Zhang ◽  
En-hui Ren ◽  
Wen-hua Yuan ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Information ◽  
Prognostic Indicator ◽  
Tissue Expression ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature’s prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

scholarly journals Identification and validation of lncRNAs involved in m6A regulation for patients with ovarian cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianfeng Zheng ◽  
Jialu Guo ◽  
Benben Cao ◽  
Ying Zhou ◽  
Jinyi Tong
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Clinicopathological Characteristics ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Both N6-methyladenosine (m6A) modification and lncRNAs play an important role in the carcinogenesis and cancer inhibition of ovarian cancer (OC). However, lncRNAs involved in m6A regulation (LI-m6As) have never been reported in OC. Herein, we aimed to identify and validate a signature based on LI-m6A for OC. Methods RNA sequencing profiles with corresponding clinical information associated with OC and 23 m6A regulators were extracted from TCGA. The Pearson correlation coefficient (PCC) between lncRNAs and 23 m6A regulators (|PCC|> 0.4 and p < 0.01) was calculated to identify LI-m6As. The LI-m6As with significant prognostic value were screened based on univariate Cox regression analysis to construct a risk model by LASSO Cox regression. Gene Set Enrichment Analysis (GSEA) was implemented to survey the biological functions of the risk groups. Several clinicopathological characteristics were utilized to evaluate their ability to predict prognosis, and a nomogram was constructed to evaluate the accuracy of survival prediction. Besides, immune microenvironment, checkpoint, and drug sensitivity in the two risk groups were compared using comprehensive algorithms. Finally, real-time qPCR analysis and cell counting kit-8 assays were performed on an alternative lncRNA, CACNA1G-AS1. Results The training cohort involving 258 OC patients and the validation cohort involving 111 OC patients were downloaded from TCGA. According to the PCC between the m6A regulators and lncRNAs, 129 LI-m6As were obtained to perform univariate Cox regression analysis and then 10 significant prognostic LI-m6As were identified. A prognostic signature containing four LI-m6As (AC010894.3, ACAP2-IT1, CACNA1G-AS1, and UBA6-AS1) was constructed according to the LASSO Cox regression analysis of the 10 LI-m6As. The prognostic signature was validated to show completely opposite prognostic value in the two risk groups and adverse overall survival (OS) in several clinicopathological characteristics. GSEA indicated that differentially expressed genes in disparate risk groups were enriched in several tumor-related pathways. At the same time, we found significant differences in some immune cells and chemotherapeutic agents between the two groups. An alternative lncRNA, CACNA1G-AS1, was proven to be upregulated in 30 OC specimens and 3 OC cell lines relative to control. Furthermore, knockdown of CACNA1G‐AS1 was proven to restrain the multiplication capacity of OC cells. Conclusions Based on the four LI-m6As (AC010894.3, ACAP2-IT1, CACNA1G-AS1, and UBA6-AS1), the risk model we identified can independently predict the OS and therapeutic value of OC. CACNA1G‐AS1 was preliminarily proved to be a malignant lncRNA.

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