Therapeutic Benefits and Prognostic Value of a Model Based on 7 Immune-associated Genes in Bladder Cancer

The emergence of immunotherapy has heralded a profound transformation in the therapeutic landscape of bladder cancer (BLAC). Immunotherapy, with its unique potential for "combination therapy", has brought about greater possibilities for treating BLCA. However, there is significant heterogeneity among bladder cancer patients, and a portion of those in advanced stages may not experience substantial benefits from chemotherapy. Immunotherapy offers a potential ray of hope for specific patient subsets. Thus, predicting the effectiveness of tumor immunotherapy and providing them with more precise treatment strategies hold paramount importance and clinical value in delivering personalized therapeutic interventions for advanced bladder cancer patients. This study is designed to establish a risk score model derived from immune-related genes that can effectively assess prognosis and immunotherapy outcomes in patients with bladder cancer.

The IMvigor210 dataset served as our training set for developing the prognostic model based on immune-related genes. Robust 7-gene expression patterns were investigated from the training set. A time-dependent receiver operating characteristic (ROC) curve and Kaplan-Meier (KM)analysis were employed to determine the prognostic relevance of these gene patterns. Independent datasets collected from the Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases were additionally utilized for re-determination. The association between the 7-gene signature-based risk score and immunological subtypes, tumor mutational burden (TMB), immune checkpoint expressions, and the proportion of immune cell infiltration was assessed within training and test sets. Furthermore, the training set's predictive potential for immunotherapy response was assessed using the 7-gene signature, and its validity was externally verified on three datasets (GSE176307, GSE140901, and GSE91016). By validating the 7-gene signature externally, we eneralized the findings beyond the original training set, and assessed the model's performance in diverse contexts. Consistent performance across these datasets reinforces the robustness and clinical utility of our 7-gene signature.

Employing the transcriptional and clinical information from the IMvigor210 for training, 348 patients were classified into two clusters with notable distinctions in prognostic stratification and immunotherapy efficacy. Seven immune-related genes Indoleamine 2,3-dioxygenase 1 (IDO1), TNF receptor superfamily member 17 (TNFRSF17), Killer Cell Lectin Like Receptor K1 (KLRK1), TNF receptor superfamily member 14 (TNFSF14), Lymphocyte-activation gene 3 (LAG3), Killer Cell Lectin Like Receptor C1 (KLRC1), and Ecto-5'-nucleotidase (NT5E) were screened based on different expression genes (DEGs) between the two clusters. The expression levels of these seven genes and the accompanying univariate component Cox regression coefficients, were computed to create a 7-gene signature-based risk score. The median value of the risk score was utilized to categorize the BLCA individuals into high-risk and low-risk groups. Researchers identified that in the low-risk group, individuals exhibited a noticeably improved chance of surviving. The external validation cohorts verified the risk score model's prognostic capacity. Furthermore, it was demonstrated that while low-risk individuals possessed higher TMB scores, higher expression of immune checkpoint genes, and lower levels of immunological infiltration, they responded more favorably to immunotherapy. The clinical relevance of the risk score model was validated in three immunotherapy groups.

The risk score model might be utilized to forecast the prognosis and efficacy of immunotherapy in BLCA patients, offering a novel course of treatment for these individuals. For patients undergoing immunotherapy, this gene signature can help predict treatment response. Low-risk patients may benefit from more tailored monitoring and personalized immunotherapy regimens. However, more investigations are required to validate its accuracy and effectiveness in a prospective cohort with larger sample sizes.