An immune cell infiltration-related gene signature predicts prognosis for bladder cancer

Abstract

To explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.

An Immune-Related Clinical-Gene-Risk-Score (CGRS) prognosis Model for Bladder Cancer

“…A series of immune-related prognostic gene signatures for bladder cancers have been constructed based on the public datasets obtained from TCGA and GEO databases [36][37][38][39][40][41][42][43]. They usually employed the TCGA-BLCA dataset to build the model and then validated it with a single external GEO dataset.…”

Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients

“…Considering the inter-and intratumoral heterogeneity in BCa, a multigene signature holding homogenous expression profiles, may be an attractive method to address this challenge. Based on the advance in bioinformatics, numerous prognostic multigene signatures have been developed (8,9). But these signatures are seldom used in clinical practices because of moderate accuracy, low reliability, and unsuitable machine learning methods.…”

POLR3G promotes EMT via PI3K/AKT signaling pathway in bladder cancer

“…As a result, physicians are now able to select high‐risk patients and potential responders with greater accuracy. This allows for the preferential use of chemotherapy or immune checkpoint blockade‐based immunotherapy, providing more personalized treatment approaches and improving patient outcomes 8–11 . Despite the availability of molecular biomarkers, their usefulness in clinical settings is often limited due to underutilized data information, lack of clinical validation, and insufficient independent verification.…”

“…This allows for the preferential use of chemotherapy or immune checkpoint blockade-based immunotherapy, providing more personalized treatment approaches and improving patient outcomes. [8][9][10][11] Despite the availability of molecular biomarkers, their usefulness in clinical settings is often limited due to underutilized data information, lack of clinical validation, and insufficient independent verification. To improve the prognosis of BLCA patients, it is critical to identify effective strategies that can overcome these limitations.…”