Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15565-e15565
Author(s):  
Qiqi Zhu ◽  
Du Cai ◽  
Wei Wang ◽  
Min-Er Zhong ◽  
Dejun Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Training Cohort ◽  
Gene Pairs ◽  
Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

scholarly journals Establishment of an immune-related gene pairs model to predict colon adenocarcinoma prognosis

2020 ◽  
Author(s):  
Jihang Luo ◽  
Puyu Liu ◽  
Leibo Wang ◽  
Yi Huang ◽  
Yuanyan Wang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
Risk Groups ◽  
High Risk Group ◽  
Related Gene ◽  
Immune Genes ◽  
Gene Pairs ◽  
Immune Related Gene

Abstract Background Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma(COAD) is the main pathological type of colon cancer. There is a lot of evidence describing the correlation between the prognosis of COAD and the immune system. The objective of the current study was the development of a robust prognostic immune-related gene pairs (IRGPs) model for estimating overall survival of COAD. Methods The gene expression profiles and clinical information of patients with colon adenocarcinoma come from TCGA and GEO databases and are divided into training and validation cohorts. Immune genes were selected which show significantly association with prognosis. Results Among 1647 immune genes, a 17 IRGPs model was built which was significantly associated with OS in the training cohort. In the training and validation data set, the IRGPs model divided patients into high-risk groups and low-risk groups, and the prognosis of the high-risk group was significantly worse( P <0.001). Univariate and multivariate Cox proportional hazard analysis confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways in high-risk groups were up-regulated. T cells regulatory and Macrophage M0 were significantly highly expressed in the high-risk group. Conclusion We successfully constructed an IRGPs model that can predict the prognosis of COAD, which provides new insights into the treatment strategy of COAD.

scholarly journals Development and validation of an individualized immune-related gene pairs prognostic signature in papillary renal cell carcinoma

2020 ◽  
Author(s):  
Xianghong Zhou ◽  
Shi Qiu ◽  
Di Jin ◽  
Kun Jin ◽  
Xiaonan Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Prognostic Signature ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Immune Related Genes

Abstract Background: Papillary renal carcinoma (PRCC) is one of the important subtypes of kidney cancer, with a high degree of heterogeneity. At present, there is still a lack of robust and accurate biomarkers for the diagnosis, prognosis and treatment selection of PRCC. Considering the important role of tumor immunity in PRCC, we aim to construct a signature based on immune-related gene pairs (IRGPs) to estimate the prognostic of patients with PRCC.Methods: We obtained gene expression profiling and clinical information of patients with PRCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), which were divided into discovery and validation cohorts, respectively. The immune-related genes in the samples were used to construct gene pairs, and the immune-related genes pairs (IRGPs) with robust impact for overall survival (OS) were screened out to construct the signature by univariate analysis, multivariate Cox analysis, and least absolute shrinkage and selection operator (Lasso) analysis. Then we verified the prognostic role of the signature, and assessed the relationship between this signature with tumor immune infiltration and functional pathways.Results: A total of 315 patients were included in our study, and divided to discovery (n=287) and validation (n=28) cohorts. Finally, we selected 14 IRGPs with a panel of 22 unique genes to construct the prognostic signature. According to the signature, we stratified patients into high-risk group and low-risk group. In both discovery and validation cohorts, the results of Kaplan-Meier analysis showed that there were significant differences in OS between the two groups (p<0.001). Combined with multiple clinical pathological factors, the results of multivariate analyses confirmed that this signature was an independent predictor of OS (HR, 3.548; 95%CI, 2.096−6.006; p<0.001). The results of immune infiltration analysis demonstrated that the abundance of multiple tumor-infiltration lymphocytes such as CD8+ T cells, Tregs, and T follicular cell helper were significantly higher in the high-risk group. Functional analysis showed that multiple immune-related signaling pathways were enriched in the high-risk group.Conclusions: We successfully established an individualized prognostic immune-related gene pairs signature, which can accurately and independently predict the OS of patients with PRCC.

scholarly journals Validation of an Immune-related Gene Pair Index as a Prognostic Marker of Early-Stage Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Zihao Wang ◽  
Xuan Xiang ◽  
Xiaoshan Wei ◽  
Linlin Ye ◽  
Yiran Niu ◽  
...  
Keyword(s):  
High Risk ◽  
Validation Cohort ◽  
Risk Group ◽  
Early Stage ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk ◽  
Data Sets ◽  
Related Gene ◽  
Immune Related Gene

Abstract Background. Lung squamous cell carcinoma (LUSC) is one of the subtypes of non-small-cell lung cancer (NSCLC) and accounts for approximately 20 to 30% of all lung cancers.Methods. In this study, we developed an immune-related gene pair index (IRGPI) for early-stage LUSC from 3 public LUSC data sets, including The Cancer Genome Atlas LUSC cohort and Gene Expression Omnibus data sets, and explored whether IRGPI could act as a prognostic marker to identify patients with early-stage LUSC at high risk.Results. IRGPI was constructed by 68 gene pairs consisting of 123 unique immune-related genes from TCGA LUSC cohort. In the derivation cohort, the hazard of death among high-risk group was 10.51 times that of the low-risk group (HR, 10.51; 95%CI, 6.96-15.86; p<0.001). The hazard of death among the high-risk group was 2.26 times that of the low-risk group (HR, 2.26; 95%CI, 1.2-4.25; p=0.009) in the GSE37745 validation cohort and was 3.2 times that of low-risk group (HR, 3.2; 95%CI, 0.98-10.4; p=0.042) in the GSE41271 validation cohort. The infiltrations of CD8+ T cells and T follicular helper cells were lower in the high-risk group, as compared with the low-risk group in the TCGA cohort (6.94% vs 9.63%, p=0.004; 2.15% vs 3%, p=0.002, respectively). The infiltrations of neutrophils, activated mast cells and monocytes were higher in the high-risk group, as compared with the low-risk group in the TCGA cohort (1.63% vs 0.72%, p=0.001; 1.64% vs 1.02%, p=0.007; 0.57% vs 0.35%, p=0.041, respectively).Conclusions. IRGPI is a significant prognostic biomarker for predicting overall survival in early-stage LUSC patients.

scholarly journals Establishment of an immune-related gene pairs model to predict colon adenocarcinoma prognosis

2020 ◽  
Author(s):  
Jihang Luo ◽  
Puyu Liu ◽  
Leibo Wang ◽  
Yi Huang ◽  
Yuanyan Wang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
Risk Groups ◽  
High Risk Group ◽  
Related Gene ◽  
Immune Genes ◽  
Gene Pairs ◽  
Immune Related Gene

Abstract Background. Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma(COAD) is the main pathological type of colon cancer. There is a lot of evidence describing the correlation between the prognosis of COAD and the immune system. The objective of the current study was the development of a robust prognostic immune-related gene pairs (IRGPs) model for estimating overall survival of COAD. Methods. The gene expression profiles and clinical information of patients with colon adenocarcinoma come from TCGA and GEO databases and are divided into training and validation cohorts. Immune genes were selected which show significantly association with prognosis. Results. Among 1647 immune genes, a 17 IRGPs model was built which was significantly associated with OS in the training cohort. In the training and validation data set, the IRGPs model divided patients into high-risk groups and low-risk groups, and the prognosis of the high-risk group was significantly worse(P＜0.001). Univariate and multivariate Cox proportional hazard analysis confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways in high-risk groups were up-regulated. T cells regulatory and Macrophage M0 were significantly highly expressed in the high-risk group. Conclusion. We successfully constructed an IRGPs model that can predict the prognosis of COAD, which provides new insights into the treatment strategy of COAD.

An Immune-Related Gene Pairs Signature Predicts Overall Survival in Melanoma

2021 ◽  
Author(s):  
Ding Pan ◽  
Qi-Feng Ou ◽  
Pan-Feng Wu ◽  
Fang Yu ◽  
Ju-Yu Tang
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Immune Genes ◽  
Gene Pairs ◽  
Immune Related Gene ◽  
Public Data

Abstract Background:The incidence rate and mortality rate of melanoma have been increasing in recent decades. Increasing evidence has depicted the correlation between melanoma prognosis and immune signature. Therefore, the aim of this study is to develop a robust prognostic immune-related gene pairs (IRGPs) signature for estimating overall survival (OS) of melanoma.Methods:Gene expression profiling and clinical information of melanoma patients were derived from two public data sets, divided into training and validation cohorts. Immune genes significantly associated with prognosis were selected. Results:Among 1,646 immune genes, a 25 IRGPs signature was built which was significantly associated with OS in the training cohort (P=1.80×10−22; hazard ratio [HR] =9.50 [6.04, 14.93]). In the validation datasets, the IRGPs signature significantly divided patients into high- vs low- risk groups considering their prognosis (P=2.47×10−4; HR =2.99 [1.66, 5.38]) and was prognostic in multivariate analysis. Functional analysis showed that several biological processes, including keratinization and pigment phenotype-related pathways, enriched in the high-risk group. Macrophages M0, NK cells resting and T cells gamma delta were significantly higher in the high-risk group compared with the low-risk group. Conclusions:We successfully constructed a robust IRGPs signature with prognostic values for melanoma, providing new insights into post-operational treatment strategies.

scholarly journals Validation of an Immune-Related Gene Pair Index as a Prognostic Marker of Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Zihao Wang ◽  
Xuan Xiang ◽  
Xiaoshan Wei ◽  
Linlin Ye ◽  
Yiran Niu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
T Cells ◽  
High Risk ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Data Sets ◽  
Related Gene ◽  
Training Cohort ◽  
Immune Related Gene

Abstract Background: Lung squamous cell carcinoma (LUSC) is one of the subtypes of non-small-cell lung cancer (NSCLC) and accounts for approximately 20 to 30% of all lung cancers. Methods: In this study, we developed an immune-related gene pair index (IRGPI) for LUSC from 8 public LUSC data sets, including The Cancer Genome Atlas LUSC cohort and Gene Expression Omnibus data sets, and explored the prognostic value of IRGPI for patients with LUSC. Results: IRGPI was constructed by 13 gene pairs consisting of 25 unique immune-related genes from the training cohort. Multivariate cox regression analysis showed that high risk based on IRGPI was an independent risk factor for poor prognosis of patients with LUSC in the training cohort (230 patients; HR= 3.40; 95%CI [2.34-4.94]; p<0.001), the testing cohort (228 patients; HR=2.11; 95%CI [1.48-3.01]; p<0.001) and the validation cohort (472 patients; HR=1.99; 95%CI [1.5-2.63]; p<0.001). The infiltrations of naïve B cells, plasma cells, CD8+ T cells, activated memory CD4+ T cells, gamma delta (γδ) T cells, M1 macrophages, and activated dendritic cells were lower in the high-risk group, as compared with the low-risk group in the TCGA cohort. The infiltrations of neutrophils, activated mast cells, and monocytes were higher in the high-risk group. Conclusions: IRGPI is a significant prognostic biomarker for predicting overall survival in LUSC patients. Combining clinical features with IRGPI will improve prognostic accuracy.

scholarly journals Comprehensive Analysis of a 14 Immune-related Gene Pairs Signature to Predict the Prognosis of Gastric Cancer and Immune Features

2020 ◽  
Author(s):  
Chuan Liu ◽  
Bo Chen ◽  
Zhangheng Huang ◽  
Chuan Hu ◽  
Liqing Jiang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Comprehensive Analysis ◽  
Immune Checkpoints ◽  
Related Gene ◽  
Gene Pairs ◽  
Immune Related Gene

Abstract Background: As a new method for predicting tumor prognosis, the predictive effect of immune-related gene pairs (IRGPs) has been confirmed in several cancers, but there is no comprehensive analysis of the clinical significance of IRGPs in gastric cancer (GC).Methods: The clinical and gene expression profile data GC patients were obtained from the GEO database. Based on the ImmPort database, differently expressed immune-related genes (DEIRGs) events were determined by a comparison of GC samples and adjacent normal samples. Cox proportional regression was used to construct an IRGPs signature, and its availability was validated with three external validation sets. In addition, we explored the association between clinical data and immune features and established a nomogram to predict outcomes of GC patients. Result: A total of 88 DEIRGs were found in GC from the training set, which formed 3828 IRGPs. 14 overall survival (OS)-related IRGPs were used to construct the prognostic signature. As a result, the patients in the high- risk group have a poorer OS compared with the low-risk group. In addition, the fraction of CD8+ T cells, plasma cells, T cells CD4 memory activated, and macrophages M1 were higher in the high-risk group. The expression of two immune checkpoints, CD276 and VTCN1, was significantly higher in the high-risk group. Based on the independent prognostic factors, a nomogram was established and showed excellent performance.Conclusion: The 14 OS-related IRGPs signature was associated with the OS, immune cells, and immune checkpoints of GC patients, which can provide the basis of related immunotherapy.

scholarly journals Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

2020 ◽  
Vol 29 ◽  
pp. 096368972097713
Author(s):  
Xueping Jiang ◽  
Yanping Gao ◽  
Nannan Zhang ◽  
Cheng Yuan ◽  
Yuan Luo ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Gene Pair ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
M2 Macrophage ◽  
Related Gene ◽  
Immune Related Gene ◽  
Potential Biomarker

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

scholarly journals A radiomics nomogram for prediction of overall survival  in hepatocellular carcinoma after hepatectomy

2020 ◽  
Author(s):  
Qinqin Liu ◽  
Jing Li ◽  
Fei Liu ◽  
Weilin Yang ◽  
Jingjing Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Factors ◽  
Training Cohort ◽  
Texture Parameters ◽  
Radiomics Signature

Abstract Background Hepatocellular carcinoma (HCC) is associated with dismal prognosis, and prediction of the prognosis of HCC can assist the therapeutic decisions. More and more studies showed that the texture parameters of images can reflect the heterogeneity of the tumor, and may have the potential to predict the prognosis of patients with HCC after surgical resection. The aim of the study was to investigate the prognostic value of computed tomography (CT) texture parameters for patients with HCC after hepatectomy, and try to develop a radiomics nomograms by combining clinicopathological factors with radiomics signature.Methods 544 eligible patients were enrolled in the retrospective study and randomly divided into training cohort (n=381) and validation cohort (n=163). The regions of interest (ROIs) of tumor is delineated, then the corresponding texture parameters are extracted. The texture parameters were selected by using the least absolute shrinkage and selection operator (LASSO) Cox model in training cohort, and the radiomics score (Rad-score) was generated. According to the cut-off value of the Rad-score calculated by the receiver operating characteristic (ROC) curve, the patients were divided into high-risk group and low-risk group. The prognosis of the two groups was compared and validated in the validation cohort. Univariate and multivariable analyses by COX proportional hazard regression model were used to select the prognostic factors of overall survival (OS). The radiomics nomogram for OS were established based on the radiomics signature and clinicopathological factors. The Concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomogram.Result 7 texture parameters associated with OS were selected in the training, and the radiomics signature was formulated based on the texture parameters. The patients were divided into high-risk group and low-risk group by the cut-off values of the Rad-score of OS. The 1-, 3- and 5-year OS rate was 71.0%, 45.5% and 35.5% in the high-risk group, respectively, and 91.7%, 82.1% and 78.7%, in the low-risk group, respectively, with significant difference (P <0.001). COX regression model found that Rad-score was an independent prognostic factor of OS. In addition, the radiomics nomogram was developed based on five variables: α‐fetoprotein (AFP), platelet lymphocyte ratio (PLR), largest tumor size, microvascular invasion (MVI) and Rad-score. The nomograms displayed good accuracy in predicting OS (C-index=0.747) in the training cohort and was confirmed in the validation cohort (C-index=0.777). The calibration plots also showed an excellent agreement between the actual and predicted survival probabilities. The DAC indicated that the radiomics nomogram showed better clinical usefulness than the clinicopathologic nomogram.Conclusion The radiomics signature is potential biomarkers of the prognosis of HCC after hepatectomy. Radiomics nomogram that integrated radiomics signature can provide more accurate estimate of OS for patients with HCC after hepatectomy.

scholarly journals Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Qianshi Zhang ◽  
Zhen Feng ◽  
Yongnian Zhang ◽  
Shasha Shi ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Poor Prognosis ◽  
Validation Cohort ◽  
Risk Group ◽  
High Risk Group ◽  
Related Gene ◽  
Prognostic Signature ◽  
Discovery Cohort ◽  
Immune Related Gene

Background. Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC. Methods. Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis. Results. A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included “TCA cycle” and “RNA degradation,” while the high-risk group was enriched in the “CAMs” and “focal adhesion” pathways. Conclusion. We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

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