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

Xu, Xin; Lu, Yida; Wu, Youliang; Wang, Mingliang; Wang, Xiaodong; Wang, Huizhen; Chen, Bo; Li, Yongxiang

doi:10.1186/s12935-021-01823-0

Cited by 10 publications

(7 citation statements)

References 48 publications

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“…Intriguingly, many risk models include NPR3 as a key indicator. Xu et al also established an NPR3-containing 7-IRG risk model, and the risk score presented excellent predictive ability in gastric cancer and revealed the status of the tumour immune microenvironment 32 . Similarly, Wang et al proposed a model consisting of 4 stemness-related genes (PSMB9, CXCL13, NPR3, and CDKN2C) to identify targets of BC stem cells and improved the therapeutic effect 33 .…”

Section: Discussionmentioning

confidence: 99%

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

Wang

et al. 2023

Sci Rep

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Breast cancer (BC) is one of the most common malignancies. However, the existing pathological grading system cannot accurately and effectively predict the survival rate and immune checkpoint treatment response of BC patients. In this study, based on The Cancer Genome Atlas (TCGA) database, a total of 7 immune-related genes (IRGs) were screened out to construct a prognostic model. Subsequently, the clinical prognosis, pathological characteristics, cancer-immunity cycle, tumour immune dysfunction and exclusion (TIDE) score, and immune checkpoint inhibitor (ICI) response were compared between the high- and low-risk groups. In addition, we determined the potential regulatory effect of NPR3 on BC cell proliferation, migration, and apoptosis. The model consisting of 7 IRGs was an independent prognostic factor. Patients with lower risk scores exhibited longer survival times. Moreover, the expression of NPR3 was increased but the expression of PD-1, PD-L1, and CTLA-4 was decreased in the high-risk group compared to the low-risk group. In addition, compared with si-NC, si-NPR3 suppressed proliferation and migration but promoted apoptosis in both MDA-MB-231 and MCF-7 cells. This study presents a model for predicting survival outcomes and provides a strategy to guide effective personalized immunotherapy in BC patients.

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Section: Discussionmentioning

confidence: 99%

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

Wang

et al. 2023

Sci Rep

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“…Prior researchers have established effective and predictive GC signatures related to immunity, ferroptosis and hypoxia genes. However, these prognostic models were limited by insufficient validation ( Liu et al, 2020 ), limited to one sex of patients and a small number of samples for validation ( Xu et al, 2021 ), and lack of drug response ( Shao et al, 2021 ). The Hippo pathway has been shown to be involved in the progression of GC, but there is no study on the association between HSPRGs and GC prognosis.…”

Section: Discussionmentioning

confidence: 99%

HIPPO signaling-related signature for predicting prognosis and therapeutic response in gastric cancer

et al. 2023

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Background: Gastric cancer (GC) is a multifactorial progressive disease with high mortality and heterogeneous prognosis. Effective prognostic biomarkers for GC were critically needed. Hippo signaling pathway is one of the critical mechanisms regulating the occurrence and development of GC, and has potential clinical application value for the prognosis and treatment of GC patients. However, there is no effective signature based on Hippo signaling pathway-related genes (HSPRGs) to predict the prognosis and treatment response of GC patients. Our study aimed to build a HSPRGs signature and explore its performance in improving prognostic assessment and drug therapeutic response in GC.Methods: Based on gene expression profiles obtained from The Cancer Genome Atlas (TCGA) database, we identified differentially expressed HSPRGs and conducted univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a multigene risk signature. Subsequently, the Kaplan-Meier curve and receiver operating characteristic (ROC) were performed to evaluate the predictive value of the risk signature in both training and validation cohort. Furthermore, we carried out univariate and multivariate Cox regression analysis to investigate the independent prognostic factors and establish a predictive nomogram. The enriched signaling pathways in risk signature were analyzed by gene set enrichment analysis (GSEA). Tumor immune dysfunction and exclusion (TIDE) and drug sensitivity analysis were performed to depict therapeutic response in GC.Results: In total, 38 differentially expressed HSPRGs were identified, and final four genes (DLG3, TGFB3, TGFBR1, FZD6) were incorporated to build the signature. The ROC curve with average 1-, 3-, and 5-year areas under the curve (AUC) equal to .609, .634, and .639. Clinical ROC curve revealed that risk signature was superior to other clinicopathological factors in predicting prognosis. Calibration curves and C-index (.655) of nomogram showed excellent consistency. Besides, in the immunotherapy analysis, exclusion (p < 2.22 × 10–16) and microsatellite instability (p = .0058) performed significantly differences. Finally, our results suggested that patients in the high-risk group were more sensitive to specific chemotherapeutic agents.Conclusion: Results support the hypothesis that Hippo-related signature is a novel prognostic biomarker and predictor, which could help optimize GC prognostic stratification and inform clinical medication decisions.

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“…Based on machine learning, prognostic models have been shown to have predictive value in various diseases, including renal cancer 21 , 22 colon adenocarcinoma 23 , and endometriosis 24 . Although previous studies have screened immune-related genes of GC to predict the prognosis characteristics, their prediction accuracy is not high 25 , 26 . In our study, we used a combination of 98 machine learning algorithms to determine the best immune-related prognostic signature for GC and performed external prognostic prediction validation using multiple datasets.…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Ning

Sun

Fan

et al. 2023

Sci Rep

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Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

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

Cited by 10 publications

References 48 publications

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

A novel immune score model predicting the prognosis and immunotherapy response of breast cancer

HIPPO signaling-related signature for predicting prognosis and therapeutic response in gastric cancer

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

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