An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma

Zheng, Zhuotao; Zhang, Lingyue; Tu, Zewei; Deng, Yan; Yin, Xingtian

doi:10.1042/bsr20203812

Cited by 12 publications

(9 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This tumor is more common in adults, with a high degree of malignancy. The posterior pole of the eye is a predilection site, easy to transfer through blood flow, and the prognosis is poor [ 2 , 3 ]. The occurrence and development of UVM is a complex process involving multifactor, multistage, and multigene variation accumulation and interaction [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

Tian

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Background. The aim of this study was to identify prognostic fatty acid metabolism lncRNAs and potential molecular targeting drugs in uveal melanoma through integrated bioinformatics analysis. Methods. In the present study, we obtained the expression matrix of 309 FAM-mRNAs and identified 225 FAM-lncRNAs by coexpression network analysis. We then performed univariate Cox analysis, LASSO regression analysis, and cross-validation and finally obtained an optimized UVM prognosis prediction model composed of four PFAM-lncRNAs (AC104129.1, SOS1-IT1, IDI2-AS1, and DLGAP1-AS2). Results. The survival curves showed that the survival time of UVM patients in the high-risk group was significantly lower than that in the low-risk group in the train cohort, test cohort, and all patients in the prognostic prediction model ( P < 0.05 ). We further performed risk prognostic assessment, and the results showed that the risk scores of the high-risk group in the train cohort, test cohort, and all patients were significantly higher than those of the low-risk group ( P < 0.05 ), patient survival decreased and the number of deaths increased with increasing risk scores, and AC104129.1, SOS1-IT1, and DLGAP1-AS2 were high-risk PFAM-lncRNAs, while IDI2-AS1 were low-risk PFAM-lncRNAs. Afterwards, we further verified the accuracy and the prognostic value of our model in predicting prognosis by PCA analysis and ROC curves. Conclusion. We identified 24 potential molecularly targeted drugs with significant sensitivity differences between high- and low-risk UVM patients, of which 13 may be potential targeted drugs for high-risk patients. Our findings have important implications for early prediction and early clinical intervention in high-risk UVM patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

Tian

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Other studies have reported variable gene signatures for predicting the OS of UM patients. The AUC of the ROC of the gene signature constructed by Xue et al was 0.8 [ 29 ], Zheng et al was 0.9 [ 30 ], Gu et al was 0.869 [ 31 ], Luo et al were 0.766 and 0.732 [ 32 ], and Li et al was 0.82 [ 33 ]. Although the AUC value of the ROC curves identified in our study was not the highest among them, was not even the lowest, which indicated that our gene signature was potentially significant.…”

Section: Discussionmentioning

confidence: 99%

Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma

Lei

Zhang

2022

BMC Med Genomics

View full text Add to dashboard Cite

Purpose Uveal melanoma (UM) is an aggressive intraocular malignancy, leading to systemic metastasis in half of the patients. However, the mechanism of the high metastatic rate remains unclear. This study aimed to identify key genes related to metastasis and construct a gene-based signature for better prognosis prediction of UM patients. Methods Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate, Lasso-penalized and multivariate Cox regression analyses were performed to establish a prognostic signature for UM patients. Results The tan and greenyellow modules were significantly associated with the metastasis of UM patients. Significant genes related to the overall survival (OS) in these two modules were then identified. Additionally, an OS-predicting signature was established. The UM patients were divided into a low- or high-risk group. The Kaplan–Meier curve indicated that high-risk patients had poorer OS than low-risk patients. The receiver operating curve (ROC) was used to validate the stability and accuracy of the final five-gene signature. Based on the signature and clinical traits of UM patients, a nomogram was established to serve in clinical practice. Conclusions We identified key genes involved in the metastasis of UM. A robust five-gene‐based prognostic signature was constructed and validated. In addition, the gene signature-based nomogram was created that can optimize the prognosis prediction and identify possible factors causing the poor prognosis of high-risk UM patients.

show abstract

“…Receiver operating characteristic (ROC) curves were produced with the R package 'timeROC' to assess the sensitivity and speci city of the PRG-signature model [13]. The area under the curve (AUC) was then calculated as the main quantitative parameter [14]. Univariate Cox regression was a critical analytical approach for determining the PRG-independent signature's predictive function based on clinical and survival data.…”

Section: Methodsmentioning

confidence: 99%

Development And Validation of A Pyroptosis -Related Signature And Immune Microenvironment Infiltration In Cutaneous Melanoma

Wei

Lan

Tang

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Cutaneous melanoma is cancer that is both malignant and aggressive, with a poor prognosis. Pyroptosis can affect the prognosis of cancer patients by controlling tumor cell growth, migration, and metastasis, as well as is closely related to the tumor immune microenvironment. The significance of pyroptosis-related genes (PRGs) in cutaneous melanoma, however, is unknown. Methods: The training set and external validation sets were cutaneous melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO), respectively. By using univariate Cox regression analysis and selection operator (Lasso) regression model, prognostic genes for overall survival (OS) were found. Candidate genes that were screened were used to calculate risk scores and construct a PRG risk model. The Kaplan Meier curve, time-dependent receiver operating characteristic (ROC) curve, and area under the curve (AUC)were used to assess the functional and prognostic usefulness of gene signatures in the risk model. Furthermore, to speculate on the activity of immune cell infiltration and immune-related pathways in the tumor immune microenvironment and calculate corresponding scores, a single sample gene set enrichment analysis (ssGSEA) was used.Results: An eight PRGs risk signature (AIM2, CASP4, CASP5, CASP8, IL18, NLRC4, NLRP6, PRKACA) were conducted and divided all cutaneous melanoma patients in the TCGA cohort into two groups: Low-risk and High-risk. Both the training and external validation sets showed that patients in the low-risk group showed a significantly higher likelihood of survival than those in the high-risk group (p < 0.001). Except for PRKACA, all the other eight PRGs in our study appeared to be longer survival times for patients. The results of ssGSEA in terms of 16 types of immune cells and the activity of 13 immune-related pathways showed that the High-risk group had lower immune pathway activity and lower levels of immune cell infiltration. In conclusion, the PRG-signature may be a significant predictor of prognosis and may play an essential role in UM patients' tumor immunity.

show abstract

An autophagy-related prognostic signature associated with immune microenvironment features of uveal melanoma

Cited by 12 publications

References 35 publications

Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma

Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma

Development And Validation of A Pyroptosis -Related Signature And Immune Microenvironment Infiltration In Cutaneous Melanoma

Contact Info

Product

Resources

About