A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer

Abstract Purpose: The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. Methods: High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. Results: A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). Conclusion: The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

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Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

BioMed Research International ◽

10.1155/2020/2719739 ◽

2020 ◽

Vol 2020 ◽

pp. 1-43

Author(s):

Beilei Wu ◽

Lijun Tao ◽

Daqing Yang ◽

Wei Li ◽

Hongbo Xu ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Immune Cells ◽

Clinical Characteristics ◽

Cox Regression ◽

Roc Curves ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model

Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

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Identification of an independent immune-genes prognostic index for renal cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08367-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Guangyao Li ◽

Xiyi Wei ◽

Shifeng Su ◽

Shangqian Wang ◽

Wei Wang ◽

...

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Roc Curve ◽

Cox Regression ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model ◽

Set Up ◽

Immune Related Genes ◽

Survival Risk

Abstract Background Considerable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC. Methods Files containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis. Results Via differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients’ survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways. Conclusion In general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

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A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

Cancer Cell International ◽

10.1186/s12935-021-01823-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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Clinical risk score is a key decisional tool in the surgical management of stage IV colorectal cancer with synchronous liver metastases: a monocentric retrospective study

HPB ◽

10.1016/j.hpb.2021.08.215 ◽

2021 ◽

Vol 23 ◽

pp. S777-S778

Author(s):

T. Stecca ◽

G. Lamon ◽

L. Bonariol ◽

B. Pauletti ◽

E. Caratozzolo ◽

...

Keyword(s):

Colorectal Cancer ◽

Retrospective Study ◽

Liver Metastases ◽

Surgical Management ◽

Risk Score ◽

Stage Iv ◽

Clinical Risk Score ◽

Synchronous Liver Metastases ◽

Clinical Risk ◽

Stage Iv Colorectal Cancer

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A Novel Six-Gene-Based Prognostic Model Predicts Survival and Clinical Risk Score for Gastric Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.615834 ◽

2021 ◽

Vol 12 ◽

Author(s):

Juan Li ◽

Ke Pu ◽

Chunmei Li ◽

Yuping Wang ◽

Yongning Zhou

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Signaling Pathway ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Patient Specific ◽

Regression Analyses ◽

Prognostic Performance ◽

Score Model

Background: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment. However, autophagy-related genes (ARGs) have rarely been analyzed in gastric cancer (GC). The purpose of this study was to analyze ARGs in GC using bioinformatic analysis and to identify new biomarkers for predicting the overall survival (OS) of patients with GC.Methods: The gene expression profiles and clinical data of patients with GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and ARGs were obtained from two other datasets (the Human Autophagy Database and Molecular Signatures Database). Lasso, univariate, and multivariate Cox regression analyses were performed to identify the OS-related ARGs. Finally, a six-ARG model was identified as a prognostic indicator using the risk-score model, and survival and prognostic performance were analyzed based on the Kaplan-Meier test and ROC curve. Estimate calculations were used to assess the immune status of this model, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for investigating the functions and terms associated with the model-related genes in GC.Results: The six ARGs, DYNLL1, PGK2, HPR, PLOD2, PHYHIP, and CXCR4, were identified using Lasso and Cox regression analyses. Survival analysis revealed that the OS of GC patients in the high-risk group was significantly lower than that of the low-risk group (p < 0.05). The ROC curves revealed that the risk score model exhibited better prognostic performance with respect to OS. Multivariate Cox regression analysis indicated that the model was an independent predictor of OS and was not affected by most of the clinical traits (p < 0.05). The model-related genes were associated with immune suppression and several biological process terms, such as extracellular structure organization and matrix organization. Moreover, the genes were associated with the P13K-Akt signaling pathway, focal adhesion, and MAPK signaling pathway.Conclusions: This study presents potential prognostic biomarkers for GC patients that would aid in determining the best patient-specific course of treatment.

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A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

10.21203/rs.3.rs-52209/v2 ◽

2021 ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background: Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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Prognostic and Predictive Values of the Autophagy Signature in Colon Cancer

10.21203/rs.3.rs-63586/v1 ◽

2020 ◽

Author(s):

Qian Xu ◽

Yugang Guo ◽

Jintao Fang ◽

Jiawei Zhou ◽

Guohui Ma ◽

...

Keyword(s):

Colon Cancer ◽

Risk Score ◽

Clinical Decision Making ◽

Cox Regression ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

Training Data ◽

Regression Analyses ◽

Data Set ◽

Predictive Values

Abstract Background: In the clinical decision-making among patients with colon cancer (COAD), making an accurate prognosis of the patients plays a central role. The effects of autophagy on the clinical outcomes of cancer, including COAD, have been widely reported in numerous studies. Here, weaim to build a novel autophagy-associated, risk-stratification scoring system to predict the overall survival(OS)of patients with COAD. Methods: In this study, the candidate autophagy-related prognostic genes correlated with the survival of COAD patients from The Cancer Genome Atlas (TCGA) public RNA microarray and clinical data sets were selected as training data set. A cohort of 67 patients from TCGA and a cohort of 124 patients from GEO were used for the external validation. The autophagy-related mRNAs(ARGs) were analyzed by multivariate Cox regression analyses. Spearman correlation analysis were used to construct autophagy-related mRNAs and lncRNAs coexpression network. Results: 6 autophagy-related mRNAs and 14 lncRNAs with prognostic value were extracted for constructing two novel autophagy-related RNAs signatures, respectively. Univariate and multivariate Cox regression analyses were then demonstrated that the two signature could act as independent prognostic predictor for OS. Additionally, a prognostic nomogram incorporating the clinicopathological characteristics(patient’s age, tumor stage) and autophagy-related lncRNA risk score was constructed to predict the OS, which was used in the training and validation sets (5-year C-index: 0.826 and 0.895, respectively), demonstrating better discrimination ability and clinical net benefit than the risk score model. Further gene set enrichment analysis revealed that autophagy-associated lncRNAs were significantly enriched in cancer-related pathways.Conclusions: The identified autophagy-related mRNAs and lncRNAs signature had important clinical implications in prognosis prediction and the user-friendly nomogram may offer an extra insight for individualized therapy of COAD.

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A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

10.21203/rs.3.rs-52209/v1 ◽

2020 ◽

Author(s):

Xin Xu ◽

Youliang Wu ◽

Mingliang Wang ◽

Yida Lu ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

Differentially Expressed ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background: Gastric cancer (GC) is one of the most fatal malignant tumors with a high mortality rate. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) for the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analysis were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified and showed a significant association with the overall survival (OS) of male GC patients. Survival analysis indicated that patients with high-risk group exhibited a poor clinical outcome. The result of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had an excellent predictive performance in both TCGA and validated cohorts. Besides, the result of tumor-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumor immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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