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ORIGINAL RESEARCH published: 25 November 2021 doi: 10.3389/fcell.2021.762478 Hypoxic Characteristic Genes Predict Response to Immunotherapy for Urothelial Carcinoma Shuo Hong 1†, Yueming Zhang 1†, Manming Cao 1†, Anqi Lin 1†, Qi Yang 1†, Jian Zhang 1*, Peng Luo 1* and Linlang Guo 2* 1 Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China, 2Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China Objective: Resistance to immune checkpoint inhibitors (ICIs) has been a massive obstacle Edited by: Dong-Hua Yang, to ICI treatment in metastatic urothelial carcinoma (MUC). Recently, increasing evidence St. John’s University, United States indicates the clinical importance of the association between hypoxia and immune status in Reviewed by: tumor patients. Therefore, it is necessary to investigate the relationship between hypoxia Zhengang Qiu, and prognosis in metastatic urothelial carcinoma. First Afﬁliated Hospital of Gannan Medical University, China Methods: Transcriptomic and clinical data from 348 MUC patients who underwent ICI Wenjie Shi, Pius-Hospital Oldenburg, Germany treatment from a large phase 2 trial (IMvigor210) were investigated in this study. The cohort was randomly divided into two datasets, a training set (n 213) and a testing set (n 135). *Correspondence: Linlang Guo Data of hypoxia-related genes were downloaded from the molecular signatures database

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(MSigDB), and screened by univariate and multivariate Cox regression analysis to Jian Zhang

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construct a prognosis-predictive model. The robustness of the model was evaluated in Peng Luo two melanoma cohorts. Furthermore, an external validation cohort, the bladder cancer

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cohort, from † These authors have contributed mechanism equally to this work and share ﬁrst authorship drug sensitivity. Results: Specialty section: This article was submitted to risk model Molecular and Cellular Oncology, signiﬁcantly a section of the journal Frontiers in Cell and Developmental prognostic Biology in the testing Received: 22 August 2021 clinical Accepted: 05 November 2021 TP53, TSC1 Published: 25 November 2021 analysis Citation: Hong S, Zhang Y, Cao M, Lin A, correlated Yang Q, Zhang J, Luo P and Guo L microenvironment (2021) Hypoxic Characteristic Genes Predict Response to Immunotherapy and immune-related for Urothelial Carcinoma. Front. Cell Dev. Biol. 9:762478. Conclusion: doi: 10.3389/fcell.2021.762478 evaluate Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. clinical relationship Keywords: microenvironment INTRODUCTION Metastatic urothelial carcinoma (MUC) is a common malignancy which occurs in the urothelial organs of the urinary system (Humphrey et al., 2016; Moch et al., 2016). According to a Global Data survey, bladder cancer ranks as the 10th most common cancer in the world, and it is four times more common in males than females (Bray et al., 2018). At present, platinum-based chemotherapy is the ﬁrst-line treatment for patients with metastatic urothelial carcinoma (Flaig et al., 2020; Witjes et al., 2021). However, it is not uncommon for chemotherapy to fail or for a positive treatment response to only be short term (Koshkin and Grivas, 2018). Therefore, in recent years, new focus has been given to developing treatments that use immune checkpoint inhibitors (ICIs). At present, some known immune checkpoint inhibitors, including antibodies against cytotoxic T lymphocyte associated protein 4 (CTLA-4) as well as programmed cell death 1 (PD-1) receptor and its ligand (PD-L1), have displayed promising efﬁcacy in metastatic urothelial carcinoma (MUC) (Darvin et al., 2018; Bagchi et al., 2021). According to the 2020 guidelines of the European Association of Urology (EAU), pembrolizumab and atezolizumab have become the ﬁrst-line treatment option for locally advanced or metastatic carcinoma after failure of platinum chemotherapy (Witjes 2021). In a phase III clinical trial involving 542 patients with advanced MUC after platinum chemotherapy, KEYNOTE-045 showed that the median overall survival (mOS) of the pembrolizumab group was longer than that of the second-line chemotherapy group by 3 months (10.3 vs 7.4 months; p 0.002) (Bellmunt et al., 2017). Long term results from the same phase III trial (>2-year follow-up) indicated that pembrolizumab had longer 1-year and 2-year overall survival (OS) results than chemotherapy (Fradet et al., 2019). However, only part of the population can beneﬁt from immunotherapy. In a phase II, multicenter, uncontrolled trial of nivolumab for platinum- resistant advanced urothelial carcinoma, the objective response rate was 19.6% in 265 patients (Sharma et al., 2017; Powles et al., 2018). A single-arm phase II trial evaluated the efﬁcacy of pembrolizumab as the ﬁrst-line treatment in 370 patients with advanced urothelial carcinoma who were not suitable for cisplatin based therapy. Only 5% of patients achieved complete remission (Balar et al., 2017). Thus, exploring the mechanism of immunosuppressant in patients with metastatic urothelial carcinoma and seeking accurate predictive biomarkers is warranted. Due to the rapid growth and abnormal proliferation of solid tumors, inadequate oxygen supply causes the tumor microenvironment to experience uneven blood distribution and immunosuppression (Choudhry and Harris, 2018; Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. carcinoma were extracted for subsequent analysis (Cancer Genome Atlas Research, 2014; Robertson et al., 2017). The GSE120736 bladder cancer data set was downloaded from the GEO database to analyze the immune microenvironment. We downloaded the GSE158632 dataset from the GEO database for subsequent functional validation analysis (Nersisyan et al., 2021). The GSE158632 dataset provides high-throughput sequencing data of 18 samples of Caco-2 and HT-29 cells, nine from the CACO-2 cell lines and nine from the HT-29 cell lines. Each cell line contains nine samples, among them, three samples are control groups and the other six samples are hypoxia groups. Identiﬁcation of Hypoxia-Related Genes Impacting Prognosis and Construction of the Risk Prognostic Model We collected the hallmark gene sets (Liberzon et al., molecular signatures database (MSigDB) (Liberzon et al., 2015). The single sample pathway enrichment (ssGSEA) score of the Hallmark gene sets of all samples in the MUC cohort was calculated using the “GSVA” R package (Hänzelmann et al., 2013). The relationship of the ssGSEA score of Hallmark gene sets and ICI efﬁcacy and survival was analyzed using the “LIMMA” and “survival” and “survimner” R packages. Then, the “survival” (https://github.com/therneau/survival) and “survminer” (https://rpkgs.datanovia.com/survminer/) packages were used to identify genes related to overall survival in the hypoxia gene sets in the MUC cohort. Based on the 44 genes screened as previously described, we established a four-gene hypoxia risk score model for risk stratiﬁcation using univariate and multivariate Cox regression for the MUC training set. n Risk score coefficientpgene expression n1 where N 4, gene expression was the expression value genes, and the coefﬁcient was the corresponding multivariab Cox regression coefﬁcient. A Kaplan–Meier survival analysis and the log-rank performed to evaluate the difference of patient survival According to median overall survival, patients with urothelial carcinoma were divided into two groups. Patients melanoma were grouped into two groups according to the best cutoff point calculated by the function “surv_cutpoint” “survminer” R package. The receiver operating characteristic (ROC) curve was used to analyze the sensitivity and speciﬁcity in the MUC training and testing sets. Analysis of Driver Gene Mutation A list of driver genes was taken from the cancer gene census (CGC) database, and panoramic maps of driver gene mutations were generated by “ComplexHeatmap” R package (Gu et al., 2016) for the MUC training set, MUC testing set, and TCGA BLCA set. The top 20 driver genes were selected for the MUC training set and the MUC testing set. As for the TCGA BLCA queue, we selected the top 20 driver genes and the other top 20 driver genes in the two queues of MUC to analyze. The “Maftools” R package (Mayakonda et al., Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 1 | The expression of hypoxia microenvironment pathway set was different in CR/PR vs PD/SD patients showed that patients with high score of hypoxia gene shows the genes and coefﬁcients of risk score. forty-four genes signiﬁcantly related to overall survival in the Hallmark hypoxia gene set as potential prognostic factors for further analysis (Supplementary Figure S1B). Next, four hypoxia genes, including TKTL1, JMJD6, IRS2 and ANXA2, were identiﬁed as independent prognostic factors for OS by multivariate analysis (Figure 1E). These identiﬁed prognostic factors were applied as basic indexes in the novel prognostic model. Finally, we developed a hypoxia risk score model to predict OS based on a low-risk hypoxia gene (TKTL1) and three high-risk genes (JMJD6, IRS2, ANXA2) (Figures 1F–I). Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 2 | Evaluation of hypoxia risk prognosis model. score was an independent prognostic factor for patients score and survival. (E,H) Kaplan Meier curve showed skcm and GSE78220 data sets. (I,L) ROC curve evaluated had better prediction effect (AUC 0.71). ROC curve testing set cohort. Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 3 | The interations of clinical features in MUC Training set high risk score subgroup was higher MUC Training set risk score with TMB and TNB. (D) Box with high hypoxia risk score. (E) Histogram showed in the low score subgroup. (F,G) KM curve showed the expression of risk score and PD-L1 in tumor cells of expression levels. (I–K) Violin diagram shows the showed that the risk score of TCGA III was higher than immunophenotyping was shown by violin diagram. were signiﬁcant (p-value < 0.05) (Figure 2A). Next, we found that the hypoxia risk scores (HR 2.75, 95% CI 1.683–4.306, p < 0.001) and TNB (HR 0.73,95% CI 0.571–0.982, p 0.0364) (Figure 2B) were independent predictors for ICIs treatment Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. The training and testing set risk curves (Figures 2C,D) also conﬁrmed the accuracy of the model. Patients were categorized into the high- and low-risk groups according to the median hypoxia risk score, and analyzed with Kaplan–Meier survival curves. As shown in Figures 2E,F, patients with high-risk scores had shorter overall survival time than those with low- risk scores (log-rank p < 0.0001, HR 2.11, 95% CI 1.51–2.95; log-rank p < 0.0001, HR 2.11, 95% CI 1.51–2.95; p <0.0001; log-rank p 0.019, HR 1.62, 95% CI 1.07–2.45). Although no signiﬁcant difference was observed across groups in the MSKCC skcm cohort treated with anti-CTLA-4 drug or the GSE78220 melanoma treated with anti-PD-1 drug, patients with high prognostic scores showed a tendency of short-term OS (Figures 2G,H). The area under the ROC curve (AUC) of three independent prognostic factors: hypoxia risk score, TMB, and TNB, was calculated to evaluate the efﬁcacy of the prediction model. The area under the curve (AUC) for the hypoxia-based model (AUC 0.71) was higher than that of both the TMB and TNB based prediction (AUC 0.33, 0.33) in the MUC training set. Similar tendencies were observed in the testing group, in which the areas under the curve (AUC) of hypoxia risk score, TMB, and TNB were 0.59, 0.37 and 0.29, respectively (Figures 2I,J). Furthermore, we found that the hypoxia risk score model had the best predictive value at 3 years (training set: 1-year AUC 0.707, 3-year AUC 0.722, Figure 2K; test set: 1-year AUC 0.547, 3- year AUC 0.574, Figure 2L). Prognostic Model and Clinical Features of Hypoxia Risk Next, we evaluated the possible associations between and clinical characteristics. We investigated the relationship among the following indicators: hypoxia scores, immunotherapy efﬁcacy, PD-L1 expression level on the tumor surface, PD-L1 expression level in immune cells, immunogenicity related factors, TCGA molecular phenotype, and tumor immune phenotype. As shown in Figure 3, patients with high-risk scores had an inadequate response to anti-PD-L1 inhibitors (p 0.000047, Figure 3A). Despite no statistical difference in the testing set, patients with high-risk scores illustrated a trend of poor response to ICI treatment (p 0.37, Figure 3B). We further performed survival analysis on hypoxia risk scores, TMB, and TNB, to explore the relationship between the survival rate and prediction factors that may affect the efﬁcacy of ICIs. We found that high-TMB and low-risk score patients have longer OS (paired log rank test p 0.00000067). Signiﬁcantly increased OS was observed in high-TNB and low-risk patients compared with low-TNB and high-risk patients (paired log-rank test p 0.00074, Figures 3B,C). In the MUC testing set, the high-TMB and low-risk group showed a similar trend of association with OS in the training set (paired log-rank test p 0.055). Additionally, patients with high TNB and a low hypoxia risk score had longer OS (paired log-rank test, p 0.046, Figures 3F,G). These results suggest that the risk score of hypoxia combined with characteristics of tumor immunogenicity are good predictors of survival and the efﬁcacy of anti-PD-L1 inhibitors. Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 4 | Panoramic view of metastatic urothelial view of the top20 driving gene mutation of the MUC mutation in the TCGA BLCA cohort. Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 5 | Changes of immune components in tumor hypoxia training set, testing set, TCGA BLCA and GSE120736 analysis results (p < 0.05), and the results show Tcm: CD4+ central memory T cells, CD8 + Tcm: CD8+ central had lower mutation frequencies of KDM6A (22%) and FGFR3 (10%) than that of the low-risk subgroup. Diagrams of mutation genes with computation and mutual exclusion analysis (Supplementary Figures S4A–C) show the results of three clinical cohorts. In the high-risk MUC training set, RB1 and TP53, ERB2 and TSC1, Rb1 and ARID1A had co-mutations. In the low-risk set, TP53 was mutable with FGFR3 and CDKN2A, TSC1 and EP300, Rb1 and ERBB2, PIK3CA and STAG2. In the TCGA BLCA data set with high hypoxia risk scores, Rb1 and TP53, KDM6A, Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 6 | The results of GSEA analysis of risk score regulated genes (p < 0.05, logfc < 0) in the pathways. (C,D) Results of GSEA analysis on inﬂammatory (FigC) and TCGA BLCA (FigD) cohort (p < 0.05). Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. FIGURE 7 | Results of drug sensitivity analysis of that the cell expression proﬁle treated with the expression proﬁle treated with the small molecule molecule drugs according to the above CMAP results. Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. associations between hypoxia and immune microenvironment. We used the xCell method to calculate the inﬁltration of immune cell components in the training set, testing set, TCGA BLCA cohort, and GSE120736 cohort downloaded from the GEO database. As is shown in Figure 5, we found that the number of CD4+ T cells was higher in the high-risk subgroup than in the low-risk subgroup. And the immunosuppression-related cells, such as Treg cells and macrophages, were signiﬁcantly higher in the high-risk subgroup than in the low-risk subgroup, whereas the immune cell scores tended to be lower in the high-risk subgroup. In the MUC training set, the high-risk subgroup showed high immune cell inﬁltration scores in the following cell types: Treg cells, Th2 cells, macrophages, macrophages M1, macrophages M2, CD4+ T cells, CD4+ central memory T cells (CD4 + Tcm). In contrast, naive B cells, CD8+ T cells, and CD8+ central memory T cells (CD8 + Tcm) remained at a relatively low level in the high- risk subgroup. In the TCGA BLCA cohort, signiﬁcantly decreased expression of naive CD4+ T cells, CD4+ central memory T cells (CD4 + Tcm), and naive CD8+ T cells was observed in the high-risk subgroup, while the expression of CD8 T cells, CD8+ central memory T cells (CD8 + Tcm), Th2 cells, and macrophages M1 tended to be low in the low-risk subgroup. In the GSE120736 bladder cancer cohort, B cells and CD4+ central memory (CD4 + Tcm) were lower in the high-risk subgroup than in the low-risk subgroup. In comparison, the number of natural killer T (NKT) cells was high in the high hypoxia risk score subgroup (Figures 5A–D). The differentially expressed genes were HLA- DPA1, HLA-DPA2, BTN3A2, GZMA, CD27, PDCD1, and TIGIT (Supplementary Figure S5A). GSEA Analysis and Functional Veriﬁcation Next, we performed Gene Ontology (GO) analysis and GSEA analysis to elucidate the biochemical functions. We selected differentially expressed genes (DEGs) in the high-risk groups compared with low-risk groups (p < 0.05, logFC > 0) for a GO pathway enrichment analysis. In terms of the top15 GO pathways, the high-risk subgroup was mainly concentrated in the extracellular matrix remodeling related pathways (Figures 6A,B). To gain further insight into the potential mechanisms, GSEA to explore the relationship between hypoxia microenvironment and lipid metabolism, glucose metabolism, and immune pathways. In terms of metabolic pathways, of the high-risk subgroup were mainly enriched in the glucose metabolism-related pathway. Meanwhile, the genes of the low- risk subgroup were mainly concentrated in lipid metabolism- related pathways. T cells, macrophages, and NK cells were signiﬁcantly enriched in the high-risk subgroup in the immune process-related pathways but not in the low-risk subgroup. Meanwhile, inﬂammation-related pathways were enriched considerably in the high-risk subgroup (Figures 6C,D). To further verify the associated signaling pathways activated in hypoxia microenvironment, we downloaded the GSE158632 dataset and classiﬁed six samples from the hypoxia group into the high-risk group and the other three samples from the control Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. was evaluated on multiple cohorts. The results showed that the model effectively identiﬁed distinct subgroups with different hypoxia risk, indicating that high hypoxia was correlated with poor prognosis. Simultaneously, the potential mechanisms of action were determined by multi-omics studies. We found that TP53, Rb1, KDM6A, and TSC1 experience mutational inactivation in a hypoxic microenvironment by analyzing the mutations of driver genes. TP53 is a known tumor suppressor gene and expresses p53 protein to regulate the DNA repair system in cells under normal conditions (Zhang and Zhang, 2015). Rb1 is a known tumor suppressor gene that regulates cell cycle (Dyson, 2016). Arakawa et al. found that Rb1 mutational inactivation may cause lung cancer resistance to PD-1 inhibitors (Arakawa et al., 2021). Our study found that Rb1 and TP53 co-mutate in a hypoxic microenvironment, suggesting that Rb1 and TP53 mutational inactivation may cause tumor resistance to ICIs in a hypoxic microenvironment (Ku et al., 2017). KDM6A belongs to the KDM6 family of histone H3 lysine 27 (H3K27) demethylases. KDM6A deletion mutation induce a repressive H3K27 demethylation state and block differentiation (Chakraborty et al., 2019). Yuichiro et al. (Itoh et al., 2019) found that a KDM6A deletion mutation resulted in the activation of Th1 and Th2 cell pathways and the down regulation of an inﬂammatory response in CD4+ T cells, which is likely to contribute to a pro-tumor microenvironment. Thus, we consider that a hypoxic microenvironment may promotes the formation of KDM6A deletion mutation, enhancing the differentiation of immunosuppressive cells, and ultimately results in immunological resistance and poor prognosis. Our mutation analysis also found that KDM6A and FGFR3 have co- occurrence, suggesting that both may lead to immune resistance. The protein encoded by TSC1 and TSC2 constitutes tuberous sclerosis (TSC) complex, which negatively impacts the regulation of mTOR activity, and participates in the functional regulation of macrophages (Yang et al., 2011; Yang et al., 2013). Meanwhile, tumor- associated macrophages tend to differentiate into M2 type and inhibit immune response in a hypoxic microenvironment (Samanta and Semenza, 2018). Therefore, unique driver gene mutations in a hypoxic microenvironment may enhance the ability of tumor cells to escape being killed by immune cells or affect the function of immune-related cells. This may lead to a poor response to immunosuppressive therapy. The hypoxia microenvironment facilitates glucose uptake in malignant tumor cells, therefore affects the functions the most important immunologically active cells, especially recognition and clearance functions of T cells. We found that the inﬁltration of CD4+ T cells in MUC was slightly higher high-risk subgroup. Treg cells, macrophages, and Th2 cells play an inhibitory role in ICI treatment, also have a higher inﬁltration degree in the high-risk subset (Gajewski et al., 2013; Samanta and Semenza, 2018). The research of Chang et al. (2015) shows that tumor cells compete with T cells for glucose in a mouse model, and the ability of tumor cells to utilize glucose for energy is more potent than that of T cells, which results in the inhibition of nutritional metabolism in T cells. Hypoxia increases the activation of glucose metabolism-related pathways as well as Frontiers in Cell and Developmental Biology | www.frontiersin.org Hong et al. DATA AVAILABILITY STATEMENT The datasets supporting the conclusions of this article are available in the R package "IMvigor210CoreBiologies" (http://research-pub.gene. com/IMvigor210CoreBiologies), and GEOdatabase (https://www. ncbi.nlm.nih.gov/geo/query/acc.cgi?accGSE120736, https://www. ncbi.nlm.nih.gov/geo/query/acc.cgi?accGSE78220, https://www. ncbi.nlm.nih.gov/geo/query/acc.cgi?accGSE158632). AUTHOR CONTRIBUTIONS LG, JZ, and PL conceived and supervised the article. the data and wrote the manuscript. YZ and AL revised the manuscript. MC and QY veriﬁed the ﬁgures. 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Mitochondrial Frontiers in Cell and Developmental Biology | www.frontiersin.org the Cancer Genome Atlas (TCGA) database, was t used to explore the of gene mutation, immune cell inﬁltration, signaling pathway enrichment, and We categorized patients as the high- or low- risk group using a four-gene hypoxia which we constructed. It was found that patients with high-risk scores had worse overall survival (OS) compared with those with low-risk scores. The model covers 0.71 of the area under the ROC curve in the training set and 0.59 set, which is better than the survival prediction of MUC patients using the characteristics. Mutation analysis results showed that deletion mutations in RB1, and KDM6A were correlated with hypoxic status. Immune cell inﬁltration illustrated that the inﬁltration T cells, B cells, Treg cells, and macrophages was with hypoxia. Functional enrichment analysis revealed that a hypoxic activated inﬂammatory pathways, glucose metabolism pathways, pathways. In this investigation, a four-gene hypoxia risk model was developed to the degree of hypoxia and prognosis of ICI treatment, which showed a promising 1 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC prediction value in MUC. Furthermore, the hypoxia risk model revealed a close between hypoxia and the tumor immune microenvironment. metastatic urothelial carcinoma, prognosis model, immune checkpoint inhibitors, hypoxia, immune Krzywinska and Stockmann, 2018). Furthermore, the hypoxic microenvironment of the tumor prevents adequate oxidation of glucose, promotes tumor cells to derive energy not only from oxidative phosphorylation but also from glycolysis, and results in the accumulation of lactic acid and adenosine, that lead to abnormal T cell ratio and T cell dysfunction (Beavis et al., 2015). A growing number of studies have revealed that the hypoxic microenvironment reduces the stimulation of T cells, increase the recruitment of immunosuppressive cells such as regulatory T (Treg) cells, and reduce the mobility of antigen- presenting cells, monocytes, and dendritic cells (Ohta, 2016; Yuen and Wong, 2020). Another study suggests that poor prognosis caused by the immune-desert-type colon cancer may be associated with tumor hypoxia (Craig et al., 2020). However, there is a lack of research focused on hypoxic microenvironments in immunotherapy for patients with MUC. In this study, we constructed a prognostic risk model to evaluate the impact of hypoxia on the efﬁcacy of immune checkpoint inhibitor (ICI) therapy in patients with MUC. In addition, we explore the possible mechanisms of hypoxic microenvironments and the efﬁcacy of ICI in patients with MUC by looking at biological markers, tumor immune typing, gene mutation, and immune microenvironment. urothelial et al., METHODS Data Collection and Preprocessing Genomic, transcriptomic, and clinical data of patients with metastatic urothelial carcinoma treated with an anti-PD-L1 drug (Atezolizumab) were downloaded from a study conducted by Mariathasan et al. (2018). We split the data into a training set and a testing set, with 60% of samples for the training set, and the remaining 40% of samples for the testing set according to hierarchical random grouping. These groupings were used to construct and evaluate the prognostic model for risk of oxygen deﬁciency. From the Memorial Sloan Kettering Cancer Center (MSKCC) database, a cohort of cutaneous skin melanoma (MSKCC skcm) patients receiving anti-CTLA-4 immunotherapy was recorded in the data published by Samstein et al. (Snyder et al., 2014). We also downloaded the transcriptomic data for PD-1 treatment in the GSE78220 data set from the Gene Expression Omnibus (GEO) database (Hugo et al., 2016) to further analyze the prognostic effect of the hypoxia risk model. The “TCGAbiolinks” R package (Colaprico et al., 2016) was used to download the TCGA BLCA bladder cancer data set (n 412) from The Cancer Genome Atlas (TCGA) database. This data set included clinical information, genomic data, and transcriptomic data. Patients with metastatic urothelial 2 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC 2018) was used for mutual exclusion analysis of the driver mutation genes in the above cohorts. Analysis of the Immune Microenvironment Gene length was calculated using the “TxDb.Hsapiens.UCSC.hg38.knownGene” (https://bioconductor. org/packages/) R package. Raw count gene expression data of the MUC and TCGA BLCA data set and the GSE120736 data set were standardized and converted into the transcripts per million (TPM) data format. The “xCell” R package was utilized to evaluate the inﬁltration abundance of immune cells in the tumor microenvironment (TME) (Aran et al., 2017). Differences were considered signiﬁcant with a p-value less than 0.05. Differential expression analysis in immune-related genes between the MUC and TCGA BLCA data sets was performed using the “LIMMA” R package (Ritchie et al., 2015). The list of immune gene sets was sourced from research by Thorsson et al. (2018) and Hao et al. (2018). 2011) from the Pathway Enrichment Analysis The “clusterProﬁler” R package (Yu et al., 2012) was used to conduct gene ontology (GO) analysis (|logFC > 0|, p 0.05) as well as gene set enrichment analysis (GSEA) (Subramanian et al., 2005) (|logFC > 0|). Gene sets for GSEA enrichment analysis were downloaded from the Hallmark gene sets (H), curated gene sets (C2), and ontology gene sets (C5) in the molecular signatures database (MSigDB). R Statistical Analysis Overall survival was estimated using the Kaplan–Meier method, and the difference between the high- and low- risk groups was examined using log-rank test in each data set. The association of risk scores with tumor mutational burden (TMB) and tumor neoantigen burden (TNB) was analyzed using Pearson correlation analysis. Wilcoxon’s test was used to test the risk score difference between TCGA phenotype and immune phenotype. The risk scores were then compared between the immune phenotypes through Kruskal- of hypoxia Wallis test. Associations between risk scores and ICI efﬁcacy or le driver gene mutation frequency were assessed, using Fisher’s exact test. The “LIMMA” R package was used to analyze the differential test were genes in the high- and low- risk score groups (p < 0.05). All analyses time. were performed using R software (v4.0.5), and the p-value was metastatic bilateral. with of the RESULTS Establishment of the Hypoxia Signature Model To investigate the role of hypoxia in ICI treatment, we calculated ssGSEA scores of the 50 pathways set from the GSEA Hallmark gene set. We found that genes in the hypoxia pathway was upregulated in patients without response (p < 0.05, Figure 1A). Meanwhile, results of univariate Cox regression revealed that high score of hypoxia ssGSEA was correlated with poor prognosis in patients with MUC (p 0.00049, HR 18, 95% CI 3.6–95, Figure 1B). Kaplan-Meier survival curves, illustrated that patients with poor prognosis had high expression of hypoxia signature genes (log-rank p 0.0013, HR 1.52, 95% CI 1.17–1.97, Figure 1C). Univariate analysis identiﬁed 3 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC in metastatic urothelial carcinoma (mUC). (A) Histogram showed that the ssGSEA score of the Hallmark (logFC < 0, p < 0.05). (B) The enrichment scores of hypoxia gene set were different in forest map. (C) KM curve set had poor OS (log rank p < 0.05). (D) The gene in each data set was shown by Wayne map. The forest map Validation of the Hypoxia Risk Prognostic Model To further evaluate the performance of the hypoxia model, we compared our hypoxia prognostic model with the routine clinical prognostic characteristics to ascertain whether our risk assessment model was a feasible prognostic tool for MUC patients. We subjected the predictor variables of hypoxia risk score, clinical signatures, TMB, and TNB of patients in the MUC training set to univariate Cox regression analyses. It was found that three factors, including hypoxia risk score, TMB, and TNB, 4 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC (A,B) shows the results of a univariate Cox regression and multivariate Cox regression showed that risk with mUC treated by ICIs. (C,D) Risk curve showed the relationship between gene expression, hypoxia risk that the high scores of hypoxia risk were associated with poor prognosis in MUC training set, testing set, MSKCC the AUC values of risk score, TMB and TNB in the training set and testing set queue, and the training set was used to evaluate the AUC value of risk score predicting 1-year and 3-year survival rate in the training set and 5 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC MUC and hypoxia risk score. (A) The stacked histogram showed that the number of non-responders to ICI in the than that in the low score subgroup. (B,C) Kaplan Meier curve showed the survival results of the joint analysis of plot showed that the PD-L1 expression in tumor cells of the MUC training set. PD-L1 was higher in tumor cells that the number of non-responders to ICI in the MUC testing set high risk score subgroup was higher than that survival results of risk score combined with TMB and TNB in the MUC testing set. (H) Box plot showed the the MUC testing set. There was no signiﬁcant difference in the expression level of risk score among different PD-L1 distribution of MUC training set, MUC testing set and TCGA BLCA risk score in TCGA molecular subtypes. The results that of other types. The distribution of the MUC training set, MUC testing set and TCGA BLCA risk score in efﬁcacy in patients with MUC. Collectively, the above results indicated that the hypoxia risk score model was a favorable predictor of ICIs treatment efﬁcacy (HR 2.692, 95% CI 1.683–4.306, p < 0.001). 6 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC According to previous studies, the MUC cohort was divided into three grades according to the results of the PD-L1 immunohistochemical staining of tumor cells. TC0 indicated that the level of PD-L1 was lower than 1%, TC1 was between 1 and 5%, and TC2+ was more than 5%. The results showed that the hypoxia risk score of TC2+ was signiﬁcantly higher than that of TC0 (Figure 3D). However, in the MUC testing set, there was no signiﬁcant difference in the hypoxia risk score at different TC levels (Figure 3H). Based on the results of immune cell PD-L1 staining, the MUC cohort was also divided into three grades. IC0 indicated that the PD-L1 level was lower than 1%, IC1 indicated that the PD-L1 expression level was between 1 and 5%, and IC2+ indicated that the PD-L1 expression level was more than 5%. There was no signiﬁcant difference in PD-L1 expression between the MUC training set and the testing set on hypoxia risk scores (Supplementary Figures S3C,F). In a study by Thorson et al. (Ritchie et al., 2015), the TCGA BLCA data set was classiﬁed into six subtypes according to tumor molecular characteristics. These were C1 (wounding healing) and C2 (IFN-γ Dominant), C3 (Inﬂammatory), C4 (Lymphocyte Depleted), C5 (Immunologically Quiet), and C6 (TGF-βDominant). The results show that the risk scores of hypoxia for the C2 type are the highest among all immune subtypes (Figure 3J). The TCGA phenotype is based on the TCGA molecular phenotype of bladder cancer (Cancer Genome Atlas Research, 2014). We observed that the TCGA type III (basic/square-like) had a high hypoxia risk score, and overexpression of KRT14 and KRT5 proteins (Figures 3I,K). hypoxia scores Hypoxia Risk Prognostic Model, Gene Mutation, Co-Occurrence and Mutual Exclusion Analysis of Mutated Genes Previous studies have shown that the chronic hypoxic microenvironment reduces DNA repair ability and impairs the DNA repair system. Therefore, we further distinguish the function of hypoxic microenvironment on gene mutations in metastatic urothelial carcinoma (Figures 4A–C). In the MUC training set, TERT (64 vs 65%) and TP53 (49 vs 51%) were in the high- and low- risk groups, among which TERT and TP53 were known to have short mutations as the major mutation. The mutation frequencies of ARID1A, KDM6A, FGFR3, and Rb1 were low in the subgroup with high-risk scores. Notably, there was no signiﬁcant difference in the mutation frequency of these genes in any group. In the MUC testing set, TERT (70 vs 60%) and TP53 (49 vs 57%) had high frequencies of known short mutations in both the high-risk and low-risk subgroups. However, in contrast to the MUC training group, the mutation frequency of ARID1A, KDM6A, and Rb1 was higher in the subgroup with high-risk scores. There was no signiﬁcant difference in the mutation frequency of these genes in any group. In the TCGA BLCA cohort, Rb1, KDM6A, and FGFR3 showed signiﬁcant differences in mutation frequency among the hypoxia risk groups. The high- risk group had a higher mutation frequency of Rb1 (26%) than that of the low-risk subgroup (11%), whereas the high-risk group 7 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC carcinoma and TCGA BLCA mutation gene, gene co mutation and mutual exclusion analysis. (A) Panoramic training set. (B) Panorama of top20 driving gene mutation of the MUC testing set. (C) Panorama of top driving gene 8 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC microenvironment. (A–D) Diagram shows the immune microenvironment inﬁltration score of the datasets in metastatic urothelial carcinoma. The ﬁgure shows the immune cells with statistical difference in the that T cells, B cells, macrophages, and Treg cells were more inﬁltrated in the high hypoxia risk score subgroup. (CD4 + memory T cells, NKT: natural killer T cells). and FGFR3 had co-mutations. TP53 and FGFR3 had mutually exclusive mutations. In the TCGA BLCA cohort with low hypoxia risk scores, Rb1 showed co-mutation with TP53 and ARID1A, and Rb1 had a mutually exclusive mutation with FGFR3. Immune Correlation Analysis Some studies suggest that a hypoxic microenvironment might affect the activation of inﬁltrating immune cells and the immune response of tumor cells. Therefore, we examined the possible 9 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC group. (A,B) Histogram showed the enrichment of up-regulated genes (p < 0.05, logFC > 0) and down top15 pathways of the GO gene set (p < 0.05, logFC < 0). The pathways were mainly the intercellular and hypoxia related pathway, immune pathway, glucose metabolism and lipid metabolism in metastatic urothelial carcinoma 10 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC GDSC and CMAP. (A) Thermogram showed the small molecule drugs in the CMAP analysis results. Blue means small molecule drugs has similar effect with the low hypoxia score subgroup, and red represents that the effect of the cell drugs is similar to that of the high hypoxia score subgroup (|score| > 60). (B) The drug molecular mechanism of small 11 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC group into the low-risk group. Then, we performed GO analysis comparing the high- and low-risk groups. As is shown in Supplementary Figures S8A,C, RNA and mRNA catabolic processes were activated under hypoxia in the CACO2 cell line. Similarly, the GO analysis demonstrated that the hypoxia microenvironment was signiﬁcantly associated with cellular response to topologically incorrect protein and regulation of autophagy in the HT29 cell line. These results indicated that the low level of oxygen during hypoxic conditions leading to increased abnormal protein expression and cell membrane instability of tumor cells. Next, we calculated the activation degree of the glucose metabolic pathway through the GSEA algorithm to verify the role of glucose metabolism in the hypoxic tumor microenvironment. Pathway enrichment analysis indicated that glucose catabolic pathway was signiﬁcantly enriched under hypoxic microenvironment both in the CACO2 and HT29 cell lines. This result was in consistent to a previous study from Denko (2008). Immune-related genes were reported to orchestrate tumor-associated immune responses; therefore, we further investigated the differences in the expression of immune-related genes. As is shown in Supplementary Figure S8G, the expression of immune-related genes ACTN4, TMBIM6, DAZAP2 and CAMTA1 were signiﬁcantly up-regulated in the hypoxic groups. T cells Drug Sensitivity Analysis To explore the drug sensitivity using our hypoxia risk stratiﬁcation system, we conducted a drug sensitivity analysis of 138 small and medium molecular drugs through the GDSC database. Clinically, cisplatin, gemcitabine, and methotrexate are commonly used for chemotherapy in urothelial carcinoma. These drugs did not show superior drug sensitivity in the subgroup with high hypoxia scores (p > 0.05) (Supplementary Figure S1D). We also used CMAP to analyze the similarities in gene expression proﬁles between other drugs and the MUC hypoxia risk score group. The results show that protein synthesis inhibitor, bacterial cell wall synthesis inhibitor, farnesyltransferase inhibitor, glycogen synthase kinase inhibitor, and HSP inhibitor were effective in patients with a high hypoxia score. These drugs may reverse the hypoxia microenvironment of patients and changing it into a state of mild hypoxia. we used DISCUSSION the genes Metastatic urothelial carcinoma usually responds poorly to treatment with immunosuppressive agents. In this study, we speculate that the insensitivity of some MUCs to ICI may be related to the hypoxic microenvironment. Therefore, we screened the hypoxia-related genes to construct a risk model to evaluate hypoxia microenvironment and predict the survival of patients with MUC undergoing ICI treatment. We constructed a hypoxia risk prognostic model based on four hypoxia-related genes (TKTL1, JMJD6, IRS2, ANXA2) through multivariate Cox regression analysis. TKTL1 was negatively correlated with survival rate, while JMJD6, IRS2 and ANXA2 showed a positive correlation. The robustness of the proposed model 12 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC the competitive uptake of glucose by tumor cells, which promotes T cells to a unfavorable nutritional status and inhibits T cells from carrying out their immune functions or clearing out tumor cells. Additionally, the enhanced glycolysis activity of tumor cells in a hypoxic environment leads to an acidic microenvironment, which also affects the function of T cells (Leone and Powell, 2020). The PDCD1 gene was up-regulated in the high hypoxia risk subgroup. PDCD1 encodes PD-1 protein, an essential protein on the surface of T cells (and other immune cells) that recognizes abnormal cells. It has been found that in an inﬂammatory environment, PD-1 protein is overexpressed on the surface of T cells. This overexpression inhibits the expression of PD-1 protein in T cells around the tumor and suppresses a T cell- mediated immune response (Baumeister et al., 2016; Multhoff and Vaupel, 2020). In addition, Nicole E. et al. established a mouse model with a hypoxia microenvironment to observe possible changes in T cells under hypoxic conditions (Scharping et al., 2021). Their research conﬁrmed that the metabolic stress under hypoxia could accelerate the terminal cell differentiation, increase reactive oxygen species (ROS) level of T cells, and eventually cause severe T-cell dysfunction, which further veriﬁed our conjecture. One of the crucial mechanisms of tumor invasion and metastasis mediated by a hypoxic microenvironment is the degradation of ﬁbrin and collagen (Ricard-Blum, 2011; Dragoš and Kovács, 2017). The results of the GO analysis showed that genes related to extracellular matrix remodeling exist in the subgroup with a high hypoxia score. The destruction of stromal cells can easily lead to abnormal differentiation and malfunction of T cells, resulting in immunosuppression (Vito et al., 2020). Therefore, we believe that hypoxia can promote the decomposition and reconstruction of the matrix structure of tumor cells. Additionally, it may inhibit the differentiation of T cells into Th1 cells and promote the differentiation of T cells into Treg cells, thus leading to immunosuppression (Schito and Semenza, 2016). Several limitations to the present study should be considered. First, although we have included as many ICI treatment cohorts as possible to verify the accuracy of the model, the sample size of some cohort is small. In addition, the validation cohort used is based on retrospective data, so the model needs to be further validated in large-scale clinical studies. Finally, the theoretical mechanisms need to be further veriﬁed by biological experiments. of some of the CONCLUSION than in the Our study establishes a novel four-gene risk stratiﬁcation system that could inform ICIs treatment response in urothelial carcinoma with by evaluating hypoxic microenvironment. In addition, we systematically explained the reasons for the poor efﬁcacy of ICI treatment in MUC with a hypoxic microenvironment from the perspectives of genome, transcriptome sequencing data, and the immune microenvironment. The validation of our results and the underlying mechanisms remain to be studied further. 13 November 2021 | Volume 9 | Article 762478 Hypoxia Risk Model for MUC FUNDING This work was supported by the Natural Science Foundation of Guangdong Province (Grant No. 2018A030313846 and 2021A1515012593), the Science and Technology Planning Project of Guangdong Province (Grant No. 2019A030317020) and the National Natural Science Foundation of China (Grant No. 81802257, 81871859, 81772457, 82172750, and 82172811). SUPPLEMENTARY MATERIAL SH analyzed The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcell.2021.762478/ full#supplementary-material Colaprico, A., Silva, T. C., Olsen, C., Garofano, L., Cava, C., Garolini, D., et al. (2016). TCGAbiolinks: an R/Bioconductor Package for Integrative Analysis of TCGA Data. Nucleic Acids Res. 44 (8), e71. doi:10.1093/nar/gkv1507 D., Yagishita, S., Motoi, N., Craig, S. G., Humphries, M. 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J., De Giorgi, in Patients with Platinum- Conﬂict of Interest: The authors declare that the research was conducted in the Carcinoma (IMvigor211): a absence of any commercial or ﬁnancial relationships that could be construed as a Trial. The Lancet. potential conﬂict of interest. Harbor Perspect. Biol. 3 Publisher’s Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their afﬁliated organizations, or those of W., Shi, W., et al. (2015). Limma the publisher, the editors and the reviewers. Any product that may be evaluated in and Microarray this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. J., Guo, G., Cherniack, A. D., of Muscle-Invasive Copyright © 2021 Hong, Zhang, Cao, Lin, Yang, Zhang, Luo and Guo. This is an j.cell.2017.09.007 open-access article distributed under the terms of the Creative Commons Attribution of Cancer and License (CC BY). The use, distribution or reproduction in other forums is permitted, Biochim. Biophys. Acta provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic P. D. A., Ford, B. R., practice. No use, distribution or reproduction is permitted which does not comply Stress Induced by Continuous with these terms. 15 November 2021 | Volume 9 | Article 762478

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