Stromal score as a prognostic factor in primary gastric cancer and close association with tumor immune microenvironment

Mao, Min; Yu, Qingliang; Huang, Rongzhi; Lu, Yong; Wang, Zhen; Liao, Liang

doi:10.1002/cam4.2801

Cited by 31 publications

(29 citation statements)

References 32 publications

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“…In this study, we assessed the CC tumor stroma by assigning scores based on stromal signatures generated using the ESTIMATE algorithm ( 13 ), and found that patients with high stromal scores had worse survival prognosis than patients with low stromal scores. Our findings in CC are consistent with results for several other cancers, such as gastric cancer, prostate cancer, and early-stage non-small cell lung cancer ( 14 , 24 – 26 ). This indicates that the scores generated by this method may be a good tool to assess the CC tumor stroma condition and could be used as a prognosis factor for CC.…”

Section: Discussionsupporting

confidence: 91%

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification

et al. 2020

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The tumor stroma plays an important role in tumor progression and chemotherapeutic resistance; however, its role in colon cancer (CC) survival prognosis remains to be investigated. Here, we identified tumor stroma biomarkers and evaluated their role in CC prognosis stratification. Four independent datasets containing a total of 1,313 patients were included in this study and were divided into training and testing sets. Stromal scores calculated using the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithm were used to assess the tumor stroma level. Kaplan-Meier curves and the log-rank test were used to identify relationships between stromal score and prognosis. Tumor stroma biomarkers were identified by cross-validation of multiple datasets and bioinformatics methods. Cox proportional hazards regression models were constructed using four prognosis factors (age, tumor stage, the ESTIMATE stromal score, and the biomarker stromal score) in different combinations for prognosis prediction and compared. Patients with high stromal scores had a lower overall survival rate (p = 0.00016), higher risk of recurrence (p < 0.0001), and higher probability of chemotherapeutic resistance (p < 0.0001) than those with low scores. We identified 16 tumor stroma biomarkers and generated a new prognosis indicator termed the biomarker stromal score (ranging from 0 to 16) based on their expression levels. Its addition to an age/tumor stage-based model significantly improved prognosis prediction accuracy. In conclusion, the tumor stromal score is significantly negatively associated with CC survival prognosis, and the new tumor stroma indicator can improve CC prognosis stratification.

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Section: Discussionsupporting

confidence: 91%

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification

et al. 2020

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“…More and more evidence revealed the tumor immune microenvironment is a crucial factor in tumor biology ( Mao et al, 2020 ). To interpret the role of PLAU expression in HNSCC based on immunity conception, the scores or proportions of tumor infiltrating cells were compared in TCGA-HNSCC cohort.…”

Section: Resultsmentioning

confidence: 99%

Overexpressed PLAU and its potential prognostic value in head and neck squamous cell carcinoma

Chen

Wang

et al. 2021

PeerJ

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Background Metastasis is a major event for survival and prognosis in patients with head and neck squamous cell carcinomas (HNSCC). A primary cause of metastasis is the proteolytic degradation of the extracellular matrix (ECM). The plasminogen activator urokinase (PLAU) is involved in the transformation of plasminogen to plasmin leading to hydrolyzation of ECM-related proteins. However, the role of PLAU expression in HNSCC is unclear and the worth being investigated. Methods PLAU expression profiles and clinical parameters from multiple HNSCC datasets were used to investigate the relationship of PLAU expression and HNSCC survival. GO and PPI network were established on PLAU-related downstream molecular. The stroma score was deconvoluted for analysis of PLAU’s association with the immune environment. ROC analysis was applied to show the performance of PLAU in predicting HNSCC prognosis. Results PLAU mRNA was significantly elevated, as opposed to its methylation, in HNSCC tumor samples over normal specimens (all p < 0.01). Univariate and multivariate cox analysis showed PLAU could be an independent indicator for HNSCC prognosis. Combining with neck lymph node status, the AUC of PLAU in predicting 5-years overall survival reached to 0.862. GO enrichment analysis showed the major biological process (extracellular matrix organization and the P13K-Akt signaling pathway) may involve to the possible mechanism of PLAU’s function on HNSCC prognosis. Furthermore, PLAU expression was positively correlated with stroma cell score, M1 type macrophages, and negatively associated with CD4 + T cell, Tregs cell, and follicular helper T cell. Conclusions PLAU might be an independent biomarker for predicting outcomes of HNSCC patients. The elevated expression of PLAU was associated with HPV positivity and neck node status. The PI3K-Akt pathway and aberrant proportions of immune cells might underly the mechanism of PLAU’s oncogene role in HNSCC.

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“…By analyzing specific gene expression, the algorithm infers the infiltration of stromal and immune cells and assesses tumor purity (18). Previous reports have applied the ESTIMATE to build prognostic models associated with TME in various malignancies, indicating the feasibility of the algorithm in the prediction of survival outcome (19)(20)(21). In the present study, we aimed to develop a Prognostic Microenvironment-related Immune Signature via ESTIMATE (PROMISE model) for glioma using expression profiles in The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database.…”

Section: Introductionmentioning

confidence: 99%

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

Qiu

Cheng

et al. 2020

Front. Oncol.

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ObjectiveIn the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.MethodsStromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.ResultsWe obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.ConclusionsThe PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

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Stromal score as a prognostic factor in primary gastric cancer and close association with tumor immune microenvironment

Cited by 31 publications

References 32 publications

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification

Overexpressed PLAU and its potential prognostic value in head and neck squamous cell carcinoma

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

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