Identification of a six-gene prognostic signature for bladder cancer associated macrophage

Jiang, Yunzhong; Qu, Xiaowei; Zhang, Mengzhao; Zhang, Lu; Yang, Tao; Ma, Minghai; Jing, Minxuan; Zhang, Nan; Song, Rundong; Zhang, Yuanquan; Yang, Zezhong; Zhang, Yaodong; Pu, Yuanchun; Fan, Jinhai

doi:10.3389/fimmu.2022.930352

Cited by 20 publications

(15 citation statements)

References 39 publications

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“…Nine studies validated their hypothesis by in-house experiments [ 42 – 50 ]. Five studies validated their hypothesis by analyzing other databases [ 51 – 55 ].…”

Section: Resultsmentioning

confidence: 87%

Clinical implications of single cell sequencing for bladder cancer

YADOLLAHVANDMIANDOAB,

JALALIZADEH,

DIONATO

et al. 2024

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Bladder cancer (BC) is the 10th most common cancer worldwide, with about 0.5 million reported new cases and about 0.2 million deaths per year. In this scoping review, we summarize the current evidence regarding the clinical implications of single-cell sequencing for bladder cancer based on PRISMA guidelines. We searched PubMed, CENTRAL, Embase, and supplemented with manual searches through the Scopus, and Web of Science for published studies until February 2023. We included original studies that used at least one single-cell technology to study bladder cancer. Forty-one publications were included in the review. Twenty-nine studies showed that this technology can identify cell subtypes in the tumor microenvironment that may predict prognosis or response to immune checkpoint inhibition therapy. Two studies were able to diagnose BC by identifying neoplastic cells through single-cell sequencing urine samples. The remaining studies were mainly a preclinical exploration of tumor microenvironment at single cell level. Single-cell sequencing technology can discriminate heterogeneity in bladder tumor cells and determine the key molecular properties that can lead to the discovery of novel perspectives on cancer management. This nascent tool can advance the early diagnosis, prognosis judgment, and targeted therapy of bladder cancer.

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“…Nine studies validated their hypothesis by in-house experiments [ 42 – 50 ]. Five studies validated their hypothesis by analyzing other databases [ 51 – 55 ].…”

Section: Resultsmentioning

confidence: 87%

Clinical implications of single cell sequencing for bladder cancer

YADOLLAHVANDMIANDOAB,

JALALIZADEH,

DIONATO

et al. 2024

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“…Next, the 3-gene prognostic model was established by univariate Cox regression analysis and LASSO algorithm, involving: MAP1B, PCOLCE2, and ELN, with the ROC curve results demonstrating that it had promising predictive efficacy for prognosis and was an independent prognostic factor for OS in BLCA. Contrary to other models [ 33 ], our signatures were the results of integrating multiple datasets and algorithms, validated by internal and external validation sets inconsistent with the training set, and showed AUC values between 0.590–0.813, suggesting higher reliability and relevance, of which we also analyzed the relationship between the model and clinicopathological characteristics, and the results revealed that risk scores were significantly associated with patients' lymph node metastasis and tumor stage, indicating that the model is not limited to having predictive value for OS. Besides, we also observed 3 signature genes related to the tumor microenvironment.…”

Section: Discussionmentioning

confidence: 92%

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model

et al. 2023

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Background The prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA. Methods BLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated. Results scRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy. Conclusion By integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

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“…In addition, using the GSE13507 dataset as an external test set, we found that the risk score calculated by the MMRS model was an adverse prognostic factor for bladder cancer patients ( Figure 2 D). We also compared two published M2 macrophages-based prognostic models in bladder cancer, 17 , 18 and time-dependent area under curve (AUC) analysis showed that MMRS had higher predictive power ( Figure 2 E). Decision curve analysis (DCA) showed that MMRS had the highest net benefit compared with other models when predicting overall survival (OS) at 1, 3, and 5 years ( Figure 2 F).…”

Section: Resultsmentioning

confidence: 99%