An immune-related gene signature predicts prognosis of gastric cancer

Jiang, Bitao; Sun, Qingsen; Tong, Yao; Wang, Yuzhuo; Ma, Haifen; Xia, Xuefei; Zhou, Yu; Zhang, Xingguo; Gao, Feng; Shu, Ping

doi:10.1097/md.0000000000016273

Cited by 32 publications

(32 citation statements)

References 30 publications

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“…Choosing the right patients for immunotherapy and predicting the prognostic outcomes has been essential. In previous studies, several indicators have been developed to evaluate the applicability of therapies on certain patients, as well as predicting the prognostic outcomes [12,16]. The IRGs have played critical roles in the tumorgenesis and progression, which were also believed to function in immunotherapies [11].…”

Section: Discussionmentioning

confidence: 99%

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Exploration of prognostic index based on immune-related genes in patients with liver hepatocellular carcinoma

et al. 2020

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The present study aimed to screen the immune-related genes (IRGs) in patients with liver hepatocellular carcinoma (LIHC) and construct a synthetic index for indicating the prognostic outcomes. The bioinformatic analysis was performed on the data of 374 cancer tissues and 50 normal tissues, which were downloaded from TCGA database. We observed that 17 differentially expressed IRGs were significantly associated with survival in LIHC patients. These LIHC-specific IRGs were validated with function analysis and molecular characteristics. Cox analysis was applied for constructing a RiskScore for predicting the survival. The RiskScore involved six IRGs and corresponding coefficients, which was calculated with the following formula: RiskScore = [Expression level of FABP5 *(0.064)] + [Expression level of TRAF3 * (0.198)] + [Expression level of CSPG5 * (0.416)] + [Expression level of IL17D * (0.197)] + [Expression level of STC2 * (0.036)] + [Expression level of BRD8 * (0.140)]. The RiskScore was positively associated with the poor survival, which was verified with the dataset from ICGC database. Further analysis revealed that the RiskScore was independent of any other clinical feature, while it was linked with the infiltration levels of six types of immune cells. Our study reported the survival-associated IRGs in LIHC and then constructed IRGs-based RiskScore as prognostic indicator for screening patients with high risk of short survival. Both the screened IRGs and IRGs-based RiskScore were clinically significant, which may be informative for promoting the individualized immunotherapy against LIHC.

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Section: Discussionmentioning

confidence: 99%

“…Learning more about the IRGs has been essential to demonstrate the mechanism of immunotherapy against cancer. Some critical IRGs could be promising biomarkers for predicting the outcome of cancer after the treatment [11,12].…”

Section: Introductionmentioning

confidence: 99%

Exploration of prognostic index based on immune-related genes in patients with liver hepatocellular carcinoma

et al. 2020

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“…Previous studies have reported several prognostic models for predicting the prognosis of gastric cancer patients [16,17]. However, these prognostic models can't predict the mortality risk for an individual patient.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, more and more studies have focused on the important role of immune microenvironment in tumorigenesis and progression [14,15]. Jiang et al developed a prognostic signature in predicting the prognosis of gastric cancer patients [16]. Yang et al developed a prognostic signature based on immune genes to predict overall survival of GC patients [17].…”

Section: Introductionmentioning

confidence: 99%

Immune gene prognostic signature for disease free survival of gastric cancer Translational research of an artificial intelligence survival predictive system

Zhang

Huang

et al. 2020

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Background The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer. Methods Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system. Results Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score. Conclusions The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

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“…Recent studies have integrated IRGs expression pro le with clinical information to gain insight into the potential clinical utility of IRGs on risk strati cation and survival prediction in several tumors [8][9][10][11]. In terms of the prognostic value of IRGs in GC, Jiang et al proposed a 16-IRGs signature that could serve as a reliable prognostic tool for overall survival (OS) [12]. Nevertheless, IRGs prognostic models for male GC patients have not been reported.…”

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confidence: 99%

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

Wang

et al. 2020

Preprint

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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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An immune-related gene signature predicts prognosis of gastric cancer

Abstract: Supplemental Digital Content is available in the text

Cited by 32 publications

References 30 publications

Exploration of prognostic index based on immune-related genes in patients with liver hepatocellular carcinoma

Exploration of prognostic index based on immune-related genes in patients with liver hepatocellular carcinoma

Immune gene prognostic signature for disease free survival of gastric cancer Translational research of an artificial intelligence survival predictive system

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

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