Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Zhang, Lianghao; Li, Long‐Qing; Zhan, Yonghao; Zhu, Zhaowei; Zhang, Xuepei

doi:10.3389/fmolb.2021.607090

Cited by 14 publications

(14 citation statements)

References 39 publications

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“…It is possible that the dysregulated immune contexture may result in the survival differences observed between risk groups as defined by the IRGPI. Noteworthily, the infiltration level of M1 macrophages in the high-risk and low-risk group are inconsistent in researches [49,50,[58][59][60][61]. Of these studies, two of them are consistent with our result [50,58], three of them report that there is no significant difference [59][60][61], and only one research is contrary to ours [49].…”

Section: Discussioncontrasting

confidence: 58%

“…Noteworthily, the infiltration level of M1 macrophages in the high-risk and low-risk group are inconsistent in researches [49,50,[58][59][60][61]. Of these studies, two of them are consistent with our result [50,58], three of them report that there is no significant difference [59][60][61], and only one research is contrary to ours [49]. The inconsistencies in the infiltration level of M1 macrophages may be related to study population differences, that is, the samples that generate risk scores are different.…”

Section: Discussioncontrasting

confidence: 49%

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Identification and validation of an immune-related gene pairs signature for three urologic cancers

Xie

Zhang

et al. 2022

Aging

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Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immunerelated gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high-and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the metavalidation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.* Age, stage and immune risk were adjusted in multivariate analysis. 4. The significant biological processes enriched by genes consisted in the IRGPI. Supplementary Table

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Section: Discussioncontrasting

confidence: 58%

Section: Discussioncontrasting

confidence: 49%

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Xie

Zhang

et al. 2022

Aging

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“…The formation of large-scale gene expression datasets along with the emergence of high-throughput gene detection technologies has made it possible to classify patients in a more precise manner and identify the key molecular characteristics combined with clinical characteristics for a better design of personalized treatment plans for patients [ 9 ]. As invasion is one of the important clinical parameters affecting the prognosis of patients with cervical cancer, this study developed a subtype classification of patients with cervical cancer according to the cervical cancer invasion-related genes.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer

Wang

2022

Computational and Mathematical Methods in Medicine

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Background. As one of the main causes leading to female cancer deaths, cervical cancer shows malignant features of local infiltration and invasion into adjacent organs and tissues. This study was designed to categorize novel molecular subtypes according to cervical cancer invasion and screen reliable prognostic markers. Methods. Invasion-related gene sets and expression profiles of invasion-related genes were collected from the CancerSEA database and The Cancer Genome Atlas (TCGA), respectively. Samples were clustered by nonnegative matrix factorization (NMF) to obtain different molecular subgroups, immune microenvironment characteristics of which were further systematically compared. Limma was employed to screen differentially expressed gene sets in different subtypes, followed by Lasso analysis for dimension reduction. Multivariate and univariate Cox regression analysis was performed to determine prognostic characteristics. The Kaplan-Meier test showed the prognostic differences of patients with different risks. Additionally, receiver operating characteristic (ROC) curves were applied to validate the prognostic model performance. A nomogram model was developed using clinical and prognostic characteristics of cervical cancer, and its prediction accuracy was reflected by calibration curve. Results. This study filtered 19 invasion-related genes with prognosis significance in cervical cancer and 2 molecular subtypes (C1, C2). Specifically, the C1 subtype had an unfavorable prognosis, which was associated with the activation of the TGF-beta signaling pathway, focal adhesion, and PI3K-Akt signaling pathway. 875 differentially expressed genes were screened, and 8 key genes were finally retained by the dimension reduction analysis. An 8-gene signature was established as an independent factor predictive of the prognosis of cervical cancer. The signature performance was even stronger when combined with N stage. Conclusion. Based on invasion-related genes, the present study categorized two cervical cancer subtypes with distinct TME characteristics and established an 8-gene marker that can accurately and independently predict the prognosis of cervical cancer.

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“…Currently, immunotherapy for TME has been shown to improve BLCA treatment ( 2 ). Therefore, more novel candidates are needed for BLCA immunotherapy ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Exploration of Tumor Microenvironment Modulation Based on the ESTIMATE Algorithm in Bladder Urothelial Carcinoma Microenvironment

Zhan

et al. 2022

Front. Oncol.

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Recently, the tumor microenvironment (TME) has been reported to be closely related to the tumor initiation, progression, and prognosis. Bladder urothelial carcinoma (BLCA), one of the most common subtypes of bladder cancer worldwide, has been associated with increased morbidity and mortality in the past decade. However, whether the TME status of BLCA contributes to the prediction of BLCA prognosis still remains uncertain. In this study, the ESTIMATE algorithms were used to estimate the division of immune and stromal components in 406 BLCA samples downloaded from The Cancer Genome Atlas database (TCGA). Based on the comparison between ESTIMATE scores, the differentially expressed genes (DEGs) were selected. Using the univariate Cox regression analysis, prognosis-related DEGs were further identified (p < 0.05). The LASSO regression analysis was then used to screen 11 genes that were highly related to the TME of BLCA to generate a novel prognostic gene signature. The following survival analyses showed that this signature could effectively predict the prognosis of BLCA. The clinical value of this signature was further verified in an external cohort obtained from the First Affiliated Hospital of Wenzhou Medical University (n = 120). Based on the stage-correlation analysis and differential expression analysis, IGF1 and MMP9 were identified as the hub genes in the signature. Additionally, using CIBERSORT algorithms, we found that both IGF1 and MMP9 were significantly associated with immune infiltration. Collectively, a novel TME-related prognostic signature contributes to accurately predict the prognosis of BLCA.

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Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Cited by 14 publications

References 39 publications

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer

Comprehensive Exploration of Tumor Microenvironment Modulation Based on the ESTIMATE Algorithm in Bladder Urothelial Carcinoma Microenvironment

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