Oxidative Medicine and Cellular Longevity
Volume 2022 (2022), Article ID 4754935, 39 pages
https://doi.org/10.1155/2022/4754935
Prognostic Signature Development on the Basis of Macrophage Phagocytosis-Mediated Oxidative Phosphorylation in Bladder Cancer
Correspondence should be addressed to Jian Hou and Xiangyang Wen
Received 2 July 2022; Revised 3 September 2022; Accepted 13 September 2022; Published 29 September 2022
Academic Editor: Nemanja Jovicic
Copyright © 2022 Genyi Qu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background. Macrophages are correlated with the occurrence and progression of bladder cancer (BCa). However, few research has focused on the predictive relevance of macrophage phagocytosis-mediated oxidative phosphorylation (MPOP) with BCa overall survival. Herein, we aimed to propose the targeted macrophage control based on MPOP as a treatment method for BCa immunotherapy. Methods. The mRNA expression data sets and clinical data of bladder cancer originated from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data set. A systematic study of several GEO data sets found differentially expressed macrophage phagocytosis regulators (DE-MPR) between BCa and normal tissues. To discover overall survival-associated DE-MPR and develop prognostic gene signature with performance validated based on receiver operating curves and Kaplan-Meier curves, researchers used univariate and Lasso Cox regression analysis (ROC). External validation was done with GSE13057 and GSE69795. To clarify its molecular mechanism and immune relevance, GO/KEGG enrichment analysis and tumor immune analysis were used. To find independent bladder cancer prognostic variables, researchers employed multivariate Cox regression analysis. Finally, using TCGA data set, a predictive nomogram was built. Results. In BCa, a four-gene signature of oxidative phosphorylation composed of PTPN6, IKZF3, HDLBP, and EMC1 was found to predict overall survival. With the MPOP feature, the ROC curve showed that TCGA data set and the external validation data set performed better in predicting overall survival than the traditional AJCC stage. The four-gene signature can identify cancers from normal tissue and separate patients into the high-risk and low-risk groups with different overall survival rates. The four MPOP-gene signature was an independent predictive factor for BCa. In predicting overall survival, a nomogram integrating genetic and clinical prognostic variables outperformed AJCC staging. Multiple oncological features and invasion-associated pathways were identified in the high-risk group, which were also correlated with significantly lower levels of immune cell infiltration. Conclusion. This paper found the MPOP-feature gene and developed a predictive nomogram capable of accurately predicting bladder cancer overall survival. The above discoveries can contribute to the development of personalized treatments and medical decisions.
1. Introduction
Bladder cancer (BCa) has been recognized as one of the most prevalent cancers of the genitourinary system, as well as one of the top 10 cancers worldwide [1]. BCa is divided into muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) in accordance with whether it invades the bladder’s musculature [2]. Surgery and postoperative Bacille Calmette-Guérin (BCG) infusion and other immunotherapies have been adopted to treat BCA, whereas there is still ~20% of BCa cases with their bladder muscle invaded. MIBC exhibits a high rate of recurrence, progression, and mortality even based on the existing treatments [3, 4]. Patients with urothelial cancer of any stage had a five-year overall survival rate of 66-68 percent [5]. The optimal preclinical model and the absence of precise biomarkers for early cancer identification hinder effective clinical therapy of BCa. As a result, it is critical to look at various types of cell death to overcome tumor cell resistance and find novel and effective biomarkers for BCa early diagnosis. The molecular mechanism of the incidence and malignant progression of bladder cancer should be understood in depth to develop more effective treatment methods, so as to facilitate the clinical prognosis of patients. The cancer niche is heavily dependent on inflammatory cells [6]. Varying numbers of macrophages, the major component of leukocyte infiltration, exist in all tumors [7]. Macrophages have a vital role in tumor inflammation. TAM supports tumor growth on multiple levels (e.g., increasing genomic instability, cultivating cancer stem cells, paving the route for metastasis, and taming protective immunity). TAMs express checkpoint triggers, thus controlling T-cell activation and becoming the target of checkpoint-blocking immunotherapies. Macrophage-centered therapies consist of techniques that trigger extracellular death or phagocytosis of cancer cells (e.g., tumor recruiting and survival prevention, functional re-education anticancer, M1-like mode, and tumor-targeting monoclonal antibody) [8]. Phagocytosis is of great significance in neutralizing and terminating infections, whereas it is also beneficial to establish, balance, and manifest noninfectious illnesses. The process consists of the clearance of apoptotic cells, turnover of senescent erythrocytes, monitoring of tumors, elimination of cell fragments, and synaptic pruning after injury [9–15]. Imbalance between professional and nonprofessional phagocytes can result in the accumulation of autoimmune, developmental defects, and toxic proteins [16, 17]. We have previously found that the polarization of macrophage can weaken attenuating inflammatory scar stenosis in New Zealand rabbits [18]. Our recent study has also found the effect exerted by M2-TAMs on the promotion of bone metastasis, chemotherapy, and endocrine therapy for resistance in prostate cancer (PCA), and the immunotherapy in patients with PCA can be affected by the regulation of macrophage polarization [19].
In macrophages, NO is synthesized by iNOS, while superoxide is mainly produced by NADPH oxidase. The reaction of superoxide with NO leads to the formation of peroxynitrite in vivo. This iNOS-derived peroxynitrite results in nitrotyrosine formation, increased antibacterial activity, and cytotoxic actions of macrophages [20]. Recent evidence indicates that peroxynitrite contributes most of the cytotoxicity of resident macrophages. Peroxynitrite interacts with lipids, DNA, and proteins via direct oxidative reactions or indirect radical-mediated mechanisms. These reactions trigger cellular responses ranging from subtle modulations of cell signaling to overwhelming oxidative injury. In vivo, peroxynitrite generation has been attributed to inflammatory diseases such as stroke, myocardial infarction, chronic heart failure, diabetes, circulatory shock, cancer, and neurodegenerative disorders [21]. Identification and identification of phagocytic regulatory factors are of great significance in the analysis of tumor cell phagocytic mechanism.
In this paper, the aim is to fully describe the dysfunction and regulatory role of phagocytosis regulators in tumor and to analyze the regulatory pattern in tumor. To find DEGs, we combined two bladder cancer data sets from the GEO database. Overall survival-associated DE-MPOP were identified using single variable and Lasso-Cox regression analysis, and predictive gene features were proposed using TCGA BLCA data set and clinical data. The prognostic genetic characteristics were validated using external data sets. The molecular mechanism of gene identity, the correlation of tumor immunity, and its potential in conducting immunotherapy were also studied. Multivariate Cox survival analysis was adopted to identify independent predictive markers for overall survival. Overall survival was predicted using a prognostic nomogram that combined prognostic gene signatures and clinical prognostic variables. Overall, our prognostic gene signature and nomogram are able to reliably predict BCa overall survival.
2. Materials and Methods
2.1. Gene Expression and Clinical Data Collection
Search and download bladder cancer mRNA expression and clinical data from GEO (https://www.ncbi.nlm.nih.gov/GEO/) using the keywords “bladder cancer,” “BLCA,” and “Bca.” The following stage of screening compromised “Homo sapiens” and “Expression analysis by array.” The search also eliminated “cell lines” and “xenografts.” For DEG analysis, the gene expression microarray data sets GSE13507 and GSE69795 were chosen and downloaded. The above data sets satisfy the following requirements: (1) human BCa tissue samples, (2) tumor and nontumor bladder control tissue samples, and (3) a total of 30 samples. The associated follow-up information of GSE13507 and GSE69795 with 246 and 61 BCa tissues, respectively, was downloaded for subsequent prognostic gene signature verification [22, 23]. Using the annotation files given by the manufacturer, match probes to gene symbols. If multiple probes match a gene, the median rank values account for the expression values. Data normalized by Robust multiarray average (RMA) are logarithmically converted for further analysis. The Cancer Genome Atlas (TCGA) data sets (https://portal.gdc.Cancer.gov/; 2019.05.20) were adopted to get clinical information on normalized RNA sequencing data as Transcript per million (TPM) and BLCA samples, which consist of 418 tumor samples and 38 normal tissue samples (Supplementary Table 1). For subsequent analysis, the normalized gene expression data from TCGA BLCA data set were transformed logarithmically.
2.2. Identification of Phagocytosis Regulatory Factor Data Sets
Phagocytosis regulatory factor data sets have been collected due to the great significance to identify and characterize phagocytosis regulators in tumors [24, 25]. 271 genes were obtained by crossing with TCGA-BLCA gene set for further analysis. Using the ssGSEA algorithm to obtain the macrophage enrichment score in samples and the Pearson correlation score as well as the expression of phagocytosis regulatory factor.
2.3. Functional Analysis of Phagocytosis Regulators
DEG’s putative biological processes, cellular components, and molecular activities were investigated using GO enrichment and KEGG pathway analysis. The signal pathways were strongly correlated with what David (https://DAVID.ncifcrf.gov/) found [26]. This research makes use of annotation, visualization, and a large discovery database (David did a functional enrichment analysis on MPOP variables). Furthermore, using the R package “cluster Profiler” and data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), functional analysis of biological processes (BP), molecular functions (MF), and cellular components (CC) regulated by macrophage phagocytosis regulators was done. The cut-off for values was established at .
2.4. Differential Phagocytosis Regulator Expression in Tumors
To investigate the differential expression of phagocytosis regulators in malignancies, we evaluated the expression levels of 233 cellular phagocytosis regulators in tumor tissue and normal tissue. The “limma” package of R language version 4.1.1 was adopted to find differentially expressed phagocytosis regulators between BLCA and normal bladder specimens, and the screening requirements were and the . The expression matrices of differentially expressed lncRNAs were visualized by the heatmap software package This paper then performed PCA analysis based on the R packages “FactoMineR” and “factoextra” to explore the discriminatory power of phagocytosis regulators in tumors.
2.5. Identification of Prognostic-Associated Phagocytosis Regulators and Construction of Prognostic Signature
MPOP correlated with overall survival were found using TCGA-BLCA data set. A complete examination of the GEO data set was utilized to evaluate the expression levels of macrophage phagocytosis regulators using univariate Cox regression analysis. MPOP with a statistical significance of were included in further analyses. Lasso-Cox regression analysis was conducted using 10-fold cross-validation based on the “glmnet” package in R to further minimize the number of MPOP with the optimal predictive performance in the selected panel. The regression coefficients from the Lasso-Cox regression model multiplied by the mRNA expression levels of patients with bladder cancer were adopted to create a predictive genetic profile. The specific model equation was as follows:
Patients were assigned into the low-risk and low-risk groups in accordance with the optimal cut-off time for prognostic gene signatures [27]. The performance of the prognostic genetic signature was evaluated using Kaplan-Meier analysis, area under the receiver operating characteristic (ROC) curve (AUC), and calibration plots comparing predicted and observed overall survival. As a control, the relationship between AJCC (e.g., stage, grade, T-stage, sex, age, and smoking) expression and clinical characteristics was analyzed. The predictive gene signature’s performance was compared to three previously identified gene signatures [28–30]. For external validation, the GSE13507 and GSE69795 data sets with comprehensive clinical information were employed. The prognostic gene signatures were adopted to compute risk ratings. The risk score’s ability to predict overall survival was confirmed using AJCC staging as a control.
2.6. Predictive Nomogram Construction and Validation
After examining the association between all independent prognostic indicators and relevant clinical parameters, a stepwise Cox regression model was adopted to predict 1-, 2-, and 3-year overall survival rates for patients with bladder cancer in TCGA data set. The performance of nomograms in predicting overall survival rate was evaluated when compared to the AJCC stage. The performance of the prognostic nomograms was evaluated using Kaplan-Meier analysis and the AUC of the ROC curve. The Harel consistency score was adopted to obtain the discriminability of the nomination charts, and Kaplan-Meier analysis was adopted to plot the survival curves of the high-risk and low-risk groups.
2.7. Construction and Validation of Predictive Nomograms
To predict 1-, 2-, and 3-year overall survival of pancreatic cancer patients in TCGA data set, all independent prognostic characteristics and relevant clinical parameters were included in the building of a prognostic nomogram via a stepwise Cox regression model after testing for collinearity. The performance of the nomogram in predicting overall survival was confirmed using the AJCC stage as a control. The performance of the prognostic nomogram was evaluated using Kaplan-Meier analysis, AUC of the ROC curve, Harrell’s concordance index, and a calibration plot comparing predicted and observed overall survival. Using a bootstrap method with 1,000 resamples, Harrell’s concordance index was produced for the measurement of nomogram discrimination. The nomogram calibration curve was plotted for the evaluation of anticipated vs. observed overall survival. The patients were separated into three groups based on the total points of the nomogram and optimal cutoffs computed in X-Tile. Kaplan-Meier analysis was adopted to plot survival curves for the high-, medium-, and low-risk groups.
2.8. The Patient’s Immune Microenvironment and Immunotherapy Are Correlated with the Signature of Phagocytosis Regulators
The ESTIMATE (estimating matrix and immune cells in malignant tumor tissue using expression data) program was adopted to obtain the matrix, immunity, and estimated scores (https://bioinformatics.mdanderson.org/public-software/estimate/) [31]. B, CD4+T, CD8+T, and dendritic cells; neutrophil; and macrophage abundance) Box and line diagrams depict the substantial difference in immunological scores between the high and low score groups, as well as the computed risk score and signature. The correlation analysis of phagocytosis regulators in bladder cancer by the TIMER (Tumor Immunity Estimation Resource) algorithm was conducted for evaluation (https://cistrome.shinvapps.io/TIMER/) [32]. The correlation analysis of phagocytosis regulators in the high-risk groups and the low-risk groups was estimated by TIMER and presented as a box diagram.
2.9. Analytical Statistics
R 3.4.3 and GraphPad Prism v.8.01 were used for statistical analysis (GraphPad Software, La Jolla, CA, USA). The 2 or Fisher’s exact test was performed to examine categorical variables. In terms of paired samples, Student’s -test was performed to evaluate continuous variables. One-way ANOVA was adopted to examine multiple groups of continuous variables. Univariate and multivariate Cox regression models were used to evaluate survival. The hazard ratio (HR) and 95 percent confidence interval (CI) were adopted to find genes correlated with overall survival. indicated a difference with statistical significance unless otherwise stated.
3. Results
3.1. MPOP Have a Role in Tumor Formation by Regulating Macrophage Phagocytosis
The work flow of this study is illustrated as Figure 1. To explore the mechanism of phagocytosis regulators in tumor development, we used ssGSEA to obtain the sample macrophage [24, 25] enrichment score and obtained the Pearson correlation between enrichment score and phagocytosis regulator expression, and the results showed that among 271 phagocytosis regulators, 88 gene expressions were correlated with macrophage enrichment score (Supplementary Figure 1, Supplementary Table 2-3). Then, to further explore the regulation of macrophage phagocytosis by MPOP, differences in macrophage phagocytosis regulatory factor expression and between-group differences were demonstrated by differential analysis (Figure 2). The above results show a regulatory role of MPOP on macrophage phagocytosis.