Dataset Information

Construction and Validation of a Novel Immunosignature for Overall Survival in Uveal Melanoma.

ABSTRACT:

Objectives

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, and immune infiltration plays a crucial role in the prognosis of UM. This study aimed to generate an immunological marker-based predictive signature for the overall survival (OS) of UM patients.

Methods

Single-sample gene-set enrichment analysis (ssGSEA) was used to profile immune cell infiltration in 79 patients with UM from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate least absolute shrinkage and selection operator (LASSO) Cox regressions were used to determine the prognostic factors for UM and construct the predictive immunosignature. Receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the clinical ability and accuracy of the model. In addition, the predictive accuracy was compared between the immunosignature and the Tumor, Node, Metastasis (TNM) staging system of American Joint Committee on Cancer (AJCC). We further analyzed the differences in clinical characteristics, immune infiltrates, immune checkpoints, and therapy sensitivity between high- and low-risk groups characterized by the prognostic model.

Results

Higher levels of immune cell infiltration in UM were related to a lower survival rate. Matrix metallopeptidase 12 (MMP12), TCDD inducible poly (ADP-ribose) polymerase (TIPARP), and leucine rich repeat neuronal 3 (LRRN3) were identified as prognostic signatures, and an immunological marker-based prognostic signature was constructed with good clinical ability and accuracy. The immunosignature was developed with a concordance index (C-index) of 0.881, which is significantly better than that of the TNM staging system (p < 0.001). We further identified 1,762 genes with upregulated expression and 798 genes with downregulated expression in the high-risk group, and the differences between the high- and low-risk groups were mainly in immune-related processes. In addition, the expression of most of the immune checkpoint-relevant and immune activity-relevant genes was significantly higher in the high-risk group, which was more sensitive to therapy.

Conclusion

We developed a novel immunosignature constructed by MMP12, TIPARP, and LRRN3 that could effectively predict the OS of UM.

SUBMITTER: Gu C

PROVIDER: S-EPMC8450517 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundUveal melanoma (UM) represents the most common primary intra-ocular malignancy in adults. Up to 50% of the patients develop distant metastases within 10 years from diagnosis, with the liver as the most common site. Upon metastatization, life expectancy strongly reduces and immune checkpoint inhibitors that prove effective in cutaneous melanoma do not modify clinical outcome. To date, few studies have focused on deciphering the immunomodulatory features of metastatic UM microenvironment, and there are no prognostic models for clinical use. This highlights the urgent need to understand the delicate interplay between tumor and immune cells acting at the site of metastasis.MethodsWe collected a patient cohort comprising 21 metastatic UM patients. Hepatic and extra-hepatic UM metastasis samples were studied by multiplex immunofluorescence to assess the tumor immune cell composition. Quantitative analyses were performed to correlate immune cell densities with treatment response, metastasis site and patient survival.ResultsCompared to patients with progressive disease, those with controlled disease had a higher intra-tumoral/peritumoral ratio of CD8 + Granzyme B+ cells, higher density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL) and an increased percentage of UM cells in close proximity to T lymphocytes, reflecting a role of tumor-killing T cells in the disease. In liver metastases (LM), the intra-tumoral densities of CD163+ tumor-associated macrophages (TAM) and of total CD8+ T cells were higher than in extra-hepatic UM metastases, but the percentage of Granzyme B+ CTL was lower. Moreover, LM displayed more UM cells adjacent to both CTL and TAM, and also more T cells in proximity to TAM, all signs of an impaired immune response. The percentage of activated CTL within the tumor represented a prognostic indicator, as patients with a higher intra-tumoral percentage of CD8 + Granzyme B+ cells had the better outcome. A temptative Immunoscore was generated and proved capable to stratify patients with improved survival. Finally, CD4 + FoxP3+ T cells appeared a crucial population for response to immunotherapy.ConclusionThe results of this study underly the clinical relevance and functional importance of composition and localization of antitumor effector cells for the progression of UM metastasis.

Project description:BackgroundMany factors have an aberrant effect on the overall survival of lung cancer (LC) patients. In recent years, remarkable progress has been made in immunotherapy, targeted treatment, and promising biomarkers. However, the available treatments and diagnostic methods are not specific for all patients.AimTo establish a system for predicting poor survival in patients with LC.MethodsThe expression matrix and clinical information for this study were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the differential analysis of all screened genes, weighted gene coexpression network analysis was performed to analyze hub genes related to patient survival. A logistic regression model was used to construct the scoring system. The expression of the hub genes was verified by performing quantitative reverse transcription-polymerase chain reaction.ResultsA total of 5007 differentially expressed genes were selected for the Weighted Gene Co-expression Network Analysis algorithm. We found that the turquoise module showed the highest correlation with patient prognosis. The gene module with the greatest positive correlation with patient survival was located in the turquoise area. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses performed for the genes contained in the turquoise module indicated the potential roles of the selected genes in the regulation of LC development. In addition, protein-protein interaction analysis was performed to screen hub genes, which identified 100 hub genes located in the core area of the network. We then intersected the 100 hub genes with 75 key genes sorted by module members to identify real hub genes associated with prognosis. Forty-one genes were finally selected. We then used a logistic regression model to determine 11 independent risk genes, namely CCNB2, CDC20, CENPO, FOXM1, HJURP, NEK2, OIP5, PLK1, PRC1, SKA1, UBE2C and SPARC.ConclusionWe constructed a predictive model based on 11 independent risk genes to establish a system predicting the survival status of patients with non-small-cell lung carcinoma.

Project description:BackgroundUveal melanoma (UM) is a rare but aggressive cancer, which is the most common primary intraocular malignancy in adults. We aimed to develop and validate a competing risk nomogram to predict cancer-specific survival (CSS) of patients with UM, as well as compare its prognostic value with that of the American Joint Committee on Cancer (AJCC) staging system.MethodsData of patients diagnosed with UM from 2010 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We extracted and integrated significant prognostic factors based on competing risk regression to build a nomogram. The nomogram with an online prediction version was also created. The performance of the nomogram was evaluated using Harrell's concordance index (C-index) and calibration plots. Receiver operating characteristic (ROC) curve was carried out to estimate clinical applicability of the model. Improvements in the predictive accuracy of our new model compared with AJCC staging system were estimated by calculating the relative integrated discrimination improvement (IDI) and the net reclassification improvement (NRI).ResultsA total of 839 eligible patients with primary UM were randomly assigned to a training cohort (588, 70%) and a validation cohort (251, 30%). Age, histological type, T stage and M stage were independent prognostic factors to predict CSS of UM and were incorporated in the nomogram. The calibration plots indicated that the 3- and 5-year CSS probabilities were consistent between the nomogram prediction and the actual observation. The C-index for this model was 0.778 (95% CI:0.756-0.800) and 0.786 (95% CI: 0.749-0.816) in the training cohort and validation cohort. Areas under the curve (AUCs) were 0.814, 0.771, and 0.792 in the training cohort, 0.788, 0.781 and 0.804 in the validation cohort, respectively. The NRI value in AJCC staging system was - 0.153 (95% CI -0.29 - - 0.041) for 3 years of follow-up and - 0.276 (95% CI -0.415 - - 0.132) for 5 years of follow-up. The IDI values for 3 and 5 years of follow-up in the AJCC staging system were - 0.021 (P = 0.076) and - 0.045 (P = 0.004), respectively.ConclusionsWe have developed and validated a competing risk nomogram to reliably predict cancer-specific survival of patients with UM. This convenient tool may be useful for evaluating cancer-specific prognosis.