Mining the potential prognostic value of synaptosomal-associated protein 25 (SNAP25) in colon cancer based on stromal-immune score

Zou, Jinyan; Duan, Darong; Yu, Changhong; Pan, Jie; Xia, Jinwei; Yang, Zaixing; Shasha, Cai

doi:10.7717/peerj.10142

Cited by 14 publications

(15 citation statements)

References 26 publications

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“…Quite recently, Zou et al (2020) reported SNAP25 as a prognostic gene based on stromal-immune score. However, no concrete model or algorithm was built to predict survival outcomes in that study, which might largely reduce the practicability and reliability of the results.…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, the latest multi-central clinical research showed deficiency in predictive accuracy of the system ( Pages et al, 2018 ), probably due to the neglection of stromal components in TME. Quite recently, Zou et al (2020) reported SNAP25 as a prognostic gene based on stromal-immune score. However, no concrete model or algorithm was built to predict survival outcomes in that study, which might largely reduce the practicability and reliability of the results.…”

Section: Discussionmentioning

confidence: 99%

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Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

et al. 2021

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BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

et al. 2021

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“…2015; Zou et al, 2020;Lan et al, 2020). Nevertheless, precise mechanisms have yet to be fully elucidated.…”

Section: Discussionmentioning

confidence: 99%

A Bioinformatics Analysis of the Potential Roles of Aquaporin 4 in Human Brain Tumors: An Immune-Related Process

et al. 2021

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Aquaporin 4 (AQP4) is an ubiquitously expressed membrane protein channel found in the central nervous system and mainly on astrocytes. Recent studies on AQP4 has implicated it in tumorigenesis. It is of interest to determine the potential value of AQP4 in identifying, guiding treatment and prognosticating various types of CNS cancers. This investigation systematically investigated the oncogenic role of AQP4 across 33 CNS tumors found in GEO and TCGA datasets. We found that CNS tumors strongly expressed AQP4. There appeared to be a strong link between the prognosis of patients with a CNS malignancy and degree of AQP4 expression. AQP4 expression influences the degree of CD8+ T-cell infiltration level as well as cancer-associated fibroblast infiltration in CNS tumors. Moreover, synaptic vesicle cycle and phosphatidylinositol signaling system-associated functions were also found to be related to AQP4 functional mechanisms. Furthermore, potential AQP4 inhibitors have also been explored by using Specs data base and virtual screening technique. This study contributes toward current knowledge regarding the role of AQP4 in CNS tumors.

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“…Nonetheless, the latest multi-central clinical research showed de ciency in predictive accuracy of the system [24], probably due to the neglection of stromal components in TME. Quite recently, Cai et al reported SNAP25 as a prognostic gene based on stromal-immune score [25]. However, no concrete model or algorithm was built to predict survival outcomes in that study, which might largely reduce the practicability and reliability of the results.…”

Section: Discussionmentioning

confidence: 97%

Stromal Score-based Gene Signature: A Prognostic Prediction Model for Colon Cancer

Jia

Dai

Zhang

et al. 2020

Preprint

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BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate and multivariate CoxPH regression analyses were used successively to select prognostic gene signature. An independent dataset from GEO was used as a validation cohort.ResultsLow stromal score was demonstrated to be a favorable factor to overall survival of colon cancer patients in TCGA cohort (log-rank test p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate and multivariate CoxPH regression analyses, a gene signature consisting of LEP, SYT3, NOG and IGSF11 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (log-rank test p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for 5-year overall survival of the model (AUC value = 0.736). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was verified to be of significant prognostic value for different subgroups in an independent colon cancer cohort from GEO database, which was demonstrated to be especially accurate for young patients (AUC value = 0.752). ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

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Mining the potential prognostic value of synaptosomal-associated protein 25 (SNAP25) in colon cancer based on stromal-immune score

Cited by 14 publications

References 26 publications

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer

A Bioinformatics Analysis of the Potential Roles of Aquaporin 4 in Human Brain Tumors: An Immune-Related Process

Stromal Score-based Gene Signature: A Prognostic Prediction Model for Colon Cancer

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