A novel immune-related gene signature predicting survival in sarcoma patients

Abstract

Sarcomas are a heterogeneous group of rare mesenchymal tumors. The migration of immune cells into these tumors and the prognostic impact of tumor-specific factors determining their interaction with these tumors remain poorly understood. The current risk stratification system is insufficient to provide a precise survival prediction and treatment response. Thus, valid prognostic models are needed to guide treatment. This study analyzed the gene expression and outcome of 980 sarcoma patients from seven public datasets. The abundance of immune cells and the response to immunotherapy was calculated. Immune-related genes (IRGs) were screened through a weighted gene co-expression network analysis (WGCNA). A least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a powerful IRG signature predicting prognosis. The identified IRG signature incorporated 14 genes and identified highrisk patients in sarcoma cohorts. The 14-IRG signature was identified as an independent risk factor for overall and disease-free survival. Moreover, the IRG signature acted as a potential indicator for immunotherapy. The nomogram based on the risk score was built to provide a more accurate survival prediction. The decision tree with IRG risk score discriminated risk subgroups powerfully. This proposed IRG signature is a robust biomarker to predict outcomes and treatment responses in sarcoma patients.

Transcriptomics-based subphenotyping of the human placenta enabled by weighted correlation network analysis in early-onset preeclampsia with and without fetal growth restriction

“…Since modules thus constructed are impartial to clinical outcome, WGCNA can be applied when analyzing datasets with unknown sample stratifications. Translational examples include use in identifying tumor subgroups with differing outcomes but common presentations 10 . By distinguishing molecular subtypes and their links to candidate prognostic markers and druggable targets, this approach allows investigators to crystalize transcriptome expression data in a potentially actionable way 7 .…”

A nomogram for predicting survival in patients with advanced (stage III/IV) pancreatic body tail cancer: a SEER-based study

“…The nomogram is a new multivariate model capable of integrating the relative contribution of each prognostic variable to the prognostic prediction outcome [ 16 , 17 ]. Although the role of the nomogram has been validated in various cancers [ 18 – 20 ], its use in patients with advanced PBTC applications has not been adequately studied. Therefore, we retrospectively analysed 1256 advanced PBTC patients from the SEER database to establish a valuable nomogram based on a Cox proportional hazards regression model to predict 1-, 3-, and 5-year survival in advanced PBTC patients and to validate it.…”

Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database

“…However, it is still unclear how immune-related genes participate in allograft rejection and whether they can be applied as prognostic markers for graft loss. Since immune-related genes play essential roles in allograft rejection, the use of immune-related genes for prognosis estimation was highlighted in many diseases (6)(7)(8). We hypothesized that immune-related genes may predict kidney graft loss.…”