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

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. 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. 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). 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.

Xu K ，He J ，Zhang J ，Liu T ，Yang F ，Ren T ... -  《-》

A five-immune-related genes-based prognostic signature for colorectal cancer.

Colorectal cancer (CRC) is a common malignancy with high morbidity and mortality. Prognosis of CRC is highly heterogeneous which hinders the making of appropriate clinical decision tremendously. We here propose to comprehensively define prognosis of CRC patients in the context of mRNA levels of immune-related genes (IRGs). CRC samples were acquired from both the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). IRGs were obtained from ImmProt database. Univariate Cox-regression analysis was adopted to screen prognostically significant IRGs for CRC patients. LASSO Cox-regression analysis was used to identify the optimal prognostic IRGs and construct the prognostic model. Multivariate Cox-regression analysis was performed to determine the independence of associations of specific factors with CRC's OS. Nomogram that combines the independent prognostic factors was constructed to predict CRC patients' 1-year, 3-years, and 5-years OS probability. There were a total of 76 differentially expressed IRGs (DEGs) in CRC tumor compared with paracancerous samples. Five out of those DEGs were significantly associated with the CRC patients' OS probability and used for the construction of prognostic model. CRC samples were divided into high-risk and low-risk group based on prognostic model. Significant differences in OS probability were obtained between high-risk and low-risk CRC patients. Additionally, a nomogram containing risk score, age and stage could effectively predict long-term OS probability of CRC patients. In conclusion, we developed a robust IRGs-based prognostic signature which should be helpful for the understanding of heterogeneity of OS probability and the future clinical research.

Zhu L ，Wang H ，Wang Z 《-》

Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer.

The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC). Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database. Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717-0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates. In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

Wang Y ，Li W ，Jin X ，Jiang X ，Guo S ，Xu F ，Su X ，Wang G ，Zhao Z ，Gu X ... -  《BMC CANCER》

Identification and validation of an immune prognostic signature in colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Although the significant efficacy of immunotherapy has been shown, only limited CRC patients benefit from it. Therefore, we aimed to establish a prognostic signature based on immune-related genes (IRGs) to predict overall survival (OS) and the potential response to immunotherapy in CRC patients. Gene expression profiles and clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic signature composed of IRGs was established using univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analysis. CIBERSORT was used to estimate the immune cell infiltration. A total of 24 survival-related IRGs were identified from 247 differentially expressed IRGs. Then, 16 IRGs were selected to establish the prognostic signature that stratified the patients into the high-risk and low-risk groups with statistically different survival outcomes. The AUCs of the time-dependent ROC curves indicated that the signature had a strong predictive accuracy in internal and external validation sets. Multivariate cox regression analysis suggested that the signature could also act as an independent prognostic factor for OS. The low-risk group had a higher proportion of immune cell infiltration than the high-risk group, such as CD4 memory resting T cells, activated dendritic cells, and resting dendritic cells. In addition, patients in the high-risk group exhibited higher tumor mutation burden and BRAF mutation. We developed an immune-related prognostic signature to predict the OS and immune status in CRC patients. We believed that our signature is conducive to better stratification and more precise immunotherapy for CRC patients.

Li M ，Wang H ，Li W ，Peng Y ，Xu F ，Shang J ，Dong S ，Bu L ，Wang H ，Wei W ，Hu Q ，Liu L ，Zhao Q ... -  《-》

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer.

An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300; GSE15459, n = 191; and GSE26901, n = 109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n = 600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432; GSE84437, n = 431; and TCGA, n = 336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC > 0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p < 0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p < 0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p = 0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p = 0.00085). The patients' risk score increased with the progression of the clinicopathological stage. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

Huo J ，Wu L ，Zang Y 《-》