Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer

Abstract

The morbidity of bladder cancer (BLCA) is high and has gradually elevated in recent years. BLCA is also characterized by high recurrence and high invasiveness. Due to the drug resistance and lack of effective prognostic indicators, the prognosis of patients with BLCA is greatly affected. Iron metabolism is considered to be a pivot of tumor occurrence, progression, and tumor microenvironment (TME) in tumors, but there is little research in BLCA. Herein, we used univariate COX regression analysis to screen 95 prognosis-related iron metabolism-related genes (IMRGs) according to transcription RNA sequencing and prognosis information of the Cancer Genome Atlas (TCGA) database. TCGA-BLCA cohort was clustered into four distinct iron metabolism patterns (C1, C2, C3, and C4) by the non-negative matrix factorization (NMF) algorithm. Survival analysis showed that C1 and C3 patterns had a better prognosis. Gene set variant analysis (GSVA) revealed that C2 and C4 patterns were mostly enriched in carcinogenic and immune activation pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) also confirmed the level of immune cell infiltration in C2 and C4 patterns was significantly elevated. Moreover, the immune checkpoint genes in C2 and C4 patterns were observably overexpressed. Studies on somatic mutations showed that the tumor mutation burden (TMB) of C1 and C4 patterns was the lowest. Chemotherapy response assessment revealed that C2 pattern was the most sensitive to chemotherapy, while C3 pattern was the most insensitive. Then we established the IMRG prognosis signature (IMRGscore) by the least absolute shrinkage and selection operator (LASSO), including 13 IMRGs (TCIRG1, CTSE, ATP6V0A1, CYP2C8, RNF19A, CYP4Z1, YPEL5, PLOD1, BMP6, CAST, SCD, IFNG, and ASIC3). We confirmed IMRGscore could be utilized as an independent prognostic indicator. Therefore, validation and quantification of iron metabolism landscapes will help us comprehend the formation of the BLCA immunosuppressive microenvironment, guide the selection of chemotherapeutic drugs and immunotherapy, and predict the prognosis of patients.

Identification of a New Prediction Model for Bladder Cancer Related to Immune Functions and Chemotherapy Using Gene Sets of Biological Processes

“…We further evaluated the differences in somatic mutation data between the two groups. The three genes with the highest mutation rates were TTN, KMT2D, and TP53 in BLCA, similar to Song et al [ 40 ] Our calculations showed that the high TMB group had a longer survival time, which is also in line with the conclusion of Samstein et al [ 41 ]. Moreover, high TMB cannot predict the immune checkpoint blockade responses in all cancer types [ 42 ].…”

Generalized machine learning based on multi‐omics data to profile the effect of ferroptosis pathway on prognosis and immunotherapy response in patients with bladder cancer

“…Song et al constructed a prediction model of bladder cancer associated with iron death including TCIRG1, but it lacked a validation group and had a single algorithm. Therefore, we rebuild the model on this basis, hoping to get a greater breakthrough 28 . The expression of SKAP1 is primarily found in T lymphocytes 29 .…”

“…Therefore, we rebuild the model on this basis, hoping to get a greater breakthrough. 28 The expression of SKAP1 is primarily found in T lymphocytes. 29 Within the SH3 domain of SKAP1, tryptophan 333 interacts with the PRR motif of adhesion and degranulationpromoting adapter protein (ADAP).…”

A model based on eight iron metabolism-related genes accurately predicts acute myeloid leukemia prognosis

“…Growing evidence shows that aberrant iron metabolism participates in AML initiation, progression, and infiltration of immune cells to the microenvironment (Kennedy et al, 2014;Benadiba et al, 2017;Hagag et al, 2018). Numerous studies have implicated dysregulated expression of IMRGs in cancer progression (Li et al, 2020;Zhu et al, 2021;Song et al, 2022). Therefore, iron metabolism is a potential target for clinical diagnosis and treatment of AML.…”