Construction of immune-related risk signature for uveal melanoma

Zhang, Zhaotian; Ni, Yicheng; Chen, Genghang; Wei, Yantao; Peng, Manjuan

doi:10.1080/21691401.2020.1773480

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…Further analysis revealed that naïve B cell (a subtype of the B cell) and CD4 memory resting T cell (a subtype of CD4+ T cell) were enrichment in low-risk group. Previous study revealed that naïve B cell and CD4 memory resting T cell infiltration was positively correlated with prognosis in several cancers [30][31][32]. Thus, infiltrating of naïve B cell and CD4 memory resting T cell may play important roles in the prognosis of CCA patients, which will be worth further investigating.…”

Section: Discussionmentioning

confidence: 95%

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Chen¹,

Chen²,

Lu³

et al. 2021

Preprint

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Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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Section: Discussionmentioning

confidence: 95%

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Chen¹,

Chen²,

Lu³

et al. 2021

Preprint

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“…As is known, the occurrence and progression of many human cancers are related to cancer hallmarks such as immune response, and immunotherapy has curative effects on certain cancers (27)(28)(29). It has been reported that immune cells such as CD8 + T cells, monocytes, memory CD4 + T cells, and mast cells are significantly related to the OS of UM patients (30). Moreover, some immunerelated gene signatures related to the survival of UM patients have been identified (15).…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Prognostic and Immunological Analysis of a Six-Gene Signature Associated With Glycolysis and Immune Response in Uveal Melanoma

2021

Front. Immunol.

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Uveal melanoma (UM) is a subtype of melanoma with poor prognosis. This study aimed to construct a new prognostic gene signature that can be used for survival prediction and risk stratification of UM patients. In this work, transcriptome data from the Molecular Signatures Database were used to identify the cancer hallmarks most relevant to the prognosis of UM patients. Weighted gene co-expression network, univariate least absolute contraction and selection operator (LASSO), and multivariate Cox regression analyses were used to construct the prognostic gene characteristics. Kaplan–Meier and receiver operating characteristic (ROC) curves were used to evaluate the survival predictive ability of the gene signature. The results showed that glycolysis and immune response were the main risk factors for overall survival (OS) in UM patients. Using univariate Cox regression analysis, 238 candidates related to the prognosis of UM patients were identified (p < 0.05). Using LASSO and multivariate Cox regression analyses, a six-gene signature including ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3, and SLC39A4 was constructed. Kaplan–Meier analysis of the UM cohort in the training set showed that patients with higher risk scores had worse OS (HR = 2.61, p < 0.001). The time-dependent ROC (t-ROC) curve showed that the risk score had good predictive efficiency for UM patients in the training set (AUC > 0.9). Besides, t-ROC analysis showed that the predictive ability of risk scores was significantly higher than that of other clinicopathological characteristics. Univariate and multivariate Cox regression analyses showed that risk score was an independent risk factor for OS in UM patients. The prognostic value of risk scores was further verified in two external UM cohorts (GSE22138 and GSE84976). Two-factor survival analysis showed that UM patients with high hypoxia or immune response scores and high risk scores had the worst prognosis. Moreover, a nomogram based on the six-gene signature was established for clinical practice. In addition, risk scores were related to the immune infiltration profiles. Taken together, this study identified a new prognostic six-gene signature related to glycolysis and immune response. This six-gene signature can not only be used for survival prediction and risk stratification but also may be a potential therapeutic target for UM patients.

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“…Combining this immunologic information with clinical (e.g., patient age) and pathological (e.g., tumor stage) data achieved an AUC of approximately 0.8 in predicting survival. 49 Other investigators have shown the promise of assessing gene expression from CNN-based image processing of cytopathologic slides. 50 …”

Section: Studies In Ummentioning

confidence: 99%

Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review

Chandrabhatla

Horgan

Cotton

et al. 2023

Invest. Ophthalmol. Vis. Sci.

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Purpose There is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment of various medical conditions in ophthalmology and beyond. Applications of ML for ocular neoplasms are in early development and this review synthesizes the current state of ML in ocular oncology. Methods We queried PubMed and Web of Science and evaluated 804 publications, excluding nonhuman studies. Metrics on ML algorithm performance were collected and the Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. We report the results of 63 unique studies. Results Research regarding ML applications to intraocular cancers has leveraged multiple algorithms and data sources. Convolutional neural networks (CNNs) were one of the most commonly used ML algorithms and most work has focused on uveal melanoma and retinoblastoma. The majority of ML models discussed here were developed for diagnosis and prognosis. Algorithms for diagnosis primarily leveraged imaging (e.g., optical coherence tomography) as inputs, whereas those for prognosis leveraged combinations of gene expression, tumor characteristics, and patient demographics. Conclusions ML has the potential to improve the management of intraocular cancers. Published ML models perform well, but were occasionally limited by small sample sizes owing to the low prevalence of intraocular cancers. This could be overcome with synthetic data enhancement and low-shot ML techniques. CNNs can be integrated into existing diagnostic workflows, while non-neural networks perform well in determining prognosis.

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Construction of immune-related risk signature for uveal melanoma

Cited by 5 publications

References 25 publications

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

A Comprehensive Prognostic and Immunological Analysis of a Six-Gene Signature Associated With Glycolysis and Immune Response in Uveal Melanoma

Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review

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