Identification and validation of novel biomarkers affecting bladder cancer immunotherapy via machine learning and its association with M2 macrophages

Abstract

BackgroundImmunotherapy has shown promising results in bladder cancer therapy options.MethodsAnalysis of open-access data was conducted using the R software. Open-access data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and IMvigor210 databases. Immunofluorescence and co-culture systems were utilized to validate the effect of PTHLH on M2 macrophage polarization.ResultsHere, through the combined (TCGA, GSE128959, GSE13507, and GSE83586) and IMvigor210 cohorts, we comprehensively investigated the biological and immune microenvironment differences in patients with diverse immunotherapy responses. Meanwhile, we found that M2 macrophage could affect bladder cancer immunotherapy sensibility. Moreover, based on the machine learning algorithm (LASSO logistics regression), PTHLH, BHMT2, and NGFR were identified, which all have good prediction abilities for patient immunotherapy. Then, a logistics regression model was established based on PTHLH, BHMT2, and NGFR, and each patient was assigned a logistics score. Subsequently, we investigated the difference in patients with high low logistics scores, including biological enrichment, immune microenvironment, and genomic characteristics. Meanwhile, data from the Human Protein Atlas database indicated a higher protein level of PTHLH in bladder cancer tissue. Immunofluorescence indicated that the knockdown of PTHLH in bladder cancer cells can significantly inhibit the M2 polarization of co-culture M0 macrophages.ConclusionsOur study investigated the difference between bladder cancer immunotherapy responders and non-responders. Meanwhile, the PTHLH was identified as a novel biomarker for bladder cancer immunotherapy.

Independent prognostic value of CLDN6 in bladder cancer based on M2 macrophages related signature

“…Interestingly, the risk score was also associated with the infiltration of multiple immune cells in bladder cancer tissues, among which there was a strong positive correlation with the infiltration of M2 macrophages ( Figure 2 G). M2 macrophages are strongly associated with immunotherapy effects, 19 so we used the TIDE algorithm to predict potential immunotherapy responses. We found a positive association between risk score and TIDE score, indicating that risk score implied poor immunotherapy effects ( Figure 2 H).…”

“… 30 Meanwhile, there is a study using machine learning to construct a model based on M2 macrophage infiltration, which can predict the immunotherapy response in bladder cancer. 19 In addition to the stimulation of a series of factors such as IL-4, the process of macrophages polarization toward M2 is also regulated by a variety of factors produced by tumor cells themselves. 30 , 31 There is a lack of reliable model to predict prognosis in bladder cancer patients.…”

Integrative analysis of TROAP with molecular features, carcinogenesis, and related immune and pharmacogenomic characteristics in soft tissue sarcoma

“…It is well known that immune checkpoints are closely related to the response to immunotherapy 15 . Therefore, we analyzed the relationship between TROAP expression and immune checkpoints.…”

Identification of bladder cancer subtypes and predictive model for prognosis, immune features, and immunotherapy based on neutrophil extracellular trap-related genes

“…Despite the advancements in smoking cessation, surgery, and systemic therapy, encompassing chemotherapy and immunotherapy, the overall prognosis of individuals with BLCA has not witnessed any notable improvement in survival rates during recent years 27 , 28 . The association between the development of BLCA and the immunological characteristics of its tumor microenvironment is significant 29 , 30 . Consequently, it is imperative to investigate immune-related biomarkers for prognostic prediction and treatment guidance in BLCA patients.…”