Prognostic Value of Interferon-γ-Related Signature Involved in Tumor Immune Infiltration in Bladder Cancer

Prognostic Value of Interferon-γ-Related Signature Involved in Tumor Immune Infiltration in Bladder Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of Interferon-γ-Related Signature Involved in Tumor Immune Infiltration in Bladder Cancer yongchang 刘永昌, Wenhao Zhang, Xiaocheng Xu, Jiabin Tai, meiyun wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1343457/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The molecular biological characteristics and unique anatomical structure of bladder cancer (BC), become cancer immune reaction mechanism and a predictor of good model and help to improve the level of cancer immunotherapy. Interferon-γ (IFN-γ) plays a key role in activating cellular immunity and stimulating anti-tumor immune responses. However, the role of IFN-γ in BC is unclear. We aimed to clarify the biological occurrence and development of BC and identify reliable biomarkers. Method We downloaded data on patients with BC from The Cancer Genome Atlas (TCGA) database and constructed a prognostic model. We analyzed the relationships between clinicopathological features and the IFN-γ signature by univariate and multivariate Cox regression analyses and evaluated the prognostic and predictive values of the IFN-γ signature by survival analysis and nomogram construction. We also conducted Gene Ontology (GO) and Kyoto Encyclopedia of and Genes and Genomes (KEGG) pathway enrichment analyses to explore the potential biological pathways related to the IFN-γ signature in BC. Immune infiltration was evaluated using CIBERSORT algorithms and the correlation between the signature and antineoplastic drug sensitivity was analyzed using the CellMiner platform. Results Five genes ( RIPK2 , RBCK1 , PTPN6 , ITGB7 , LATS2 ) were selected to construct IFN-γ-related signatures. The pathological features were identified as an independent risk factor. Patients with BC were divided into high-risk and low-risk groups according to their signatures. Patients with higher risk scores had shorter overall survival and a worse prognosis. GO and KEGG enrichment and CIBERSORT analysis showed significant relationships between the signature and survival, independent risk factors, immune infiltration, and antineoplastic drug sensitivity. Conclusion We obtained a risk profile for a regression model consisting of IFN-γ signature genes to predict the prognosis of patients with BC. This model may be used to improve the prognostic accuracy of the immune microenvironment in BC. Interferon-γ-related Bladder cancer prognostic model genes immunity. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Bladder cancer (BC) is the fifth most common cancer and the most prevalent urinary tract cancer worldwide, with an estimated 81,400 new cases and 17,980 deaths in the United States in 2020[ 1 ]. A national registry of advanced cancer in Denmark (N = 31,771) reported that BC was associated with higher risks of pain and constipation and a lower quality of life than other cancers[ 2 ]. BC can present as non-muscle-invasive bladder cancer (NMIBC), muscle-invasive bladder cancer (MIBC), or as a metastatic form of the disease, of which MIBC (≥ T2) usually progresses to metastasis and has a poor prognosis, with a 5-year survival rate of < 50% [ 3 ]. BC is one the most common mutated cancers in humans, second only to lung and skin cancers in terms of mutation rates[ 4 , 5 ]. Sequencing and gene expression studies have revealed numerous DNA, RNA, and protein biomarkers for BC[ 6 ]. In addition, immune checkpoint inhibitors (ICIs) have demonstrated prognostic and therapeutic potential [ 7 , 8 ] and have thus been approved for the treatment of metastatic BC, leading to renewed interest in the immune components of the tumor microenvironment (TME). Further information on the mechanisms underlying BC biogenesis and development is thus required, and reliable response biomarkers are needed to allow the selection of patients likely to benefit from such treatments. Interferon-γ (IFN-γ) is the sole member of the type II IFN family discovered nearly 60 years ago. It is encoded by the IFNG gene and consists of two polypeptide chains joined in an antiparallel fashion [ 9 ]. Wheelock first described IFN- γ as a phytohemagglutinin-induced viral inhibitor produced by leukocyte stimulation[ 10 ]. In addition, a recent review reported that IFN-γ was involved in tumor progression and regression[ 11 ], with a key role in activating cellular immunity and subsequent anti-tumor immune responses. However, IFN-γ can also lead to immune escape by inhibiting the T cell immune response and can induce programmed death-ligand 1 (PD-L1) and indolamine-2,3-dioxygenase expression and regulate tumor immune-resistance mechanisms. The role of IFN-γ signaling in regulating the immune state and anti-tumor immunity is thus controversial. Patients with NMIBC have high rates of recurrence and progression[ 12 ]. Recent studies found that immunotherapy could help to avoid surgery in patients with high grade NMIBC, and pembrolizumab has been used in patients who have failed second-line therapy or who are intolerant to first-line platinum-based chemotherapy[ 13 ]. PD-L1 was induced by typical IFN-γ signaling in clear cell renal cell carcinoma-like cell lines, and a high level of PD-L1 mRNA in tumor tissues was positively correlated with IFN-γ signature and was associated with a beneficial prognosis in renal cell cancer[ 14 ]. Changes in Toll-like receptor 4 expression were associated with changes in the expression of key cytokines (transforming growth factor-β, tumor necrosis factor-α, and IFN-γ), which affect tumor progression and metastasis [ 15 ]. These results suggested that IFN-related genes may be used to guide the use of ICIs in patients with BC. We conducted this study to validate the hypothesis that IFN-γ promotes tumor immune infiltration in BC. Using The Cancer Genome Atlas (TCGA) database as a training set, we evaluated the mRNA expression data, clinical information, signaling pathways, and immune infiltration in patients with BC. We also constructed an optimized IFN-γ estimation model, and hypothesized that this could be used as a predictive marker for immunotherapy and as a prognostic biomarker for BC patients. We also verified the drug sensitivity of the model. Materials And Methods Patient Data Extraction Transcriptome profiles and clinical information, including sex, age, clinicopathological characteristics, stage, and survival data for patients with BC were obtained with the HTSeq-FPKM format from TCGA database via the GDC portal ( https://portal.gdc.cancer.gov/ ). Data collected from TCGA database were used as the training set for model construction. Exclusion criteria were patients with incomplete data. Drug sensitivity data were identified by NCI-60 and downloaded from the CellMiner dataset ( https://discover.nci.nih.gov/cellminer/ ). Construction of IFN-γ Signature Prediction Models We first constructed a signature prediction estimation model. IFN-related genes were identified from relevant research and prognostic genes were further identified by univariate Cox analysis. Significant genes with a cut-off point of P < 0.05 were selected and a stepwise Cox regression model was established. On the basis of the results, we calculated the risk score using the following formula: $$\text{R}\text{i}\text{s}\text{k} \text{S}\text{c}\text{o}\text{r}\text{e}={\sum }_{i=1}^{n}Coef\left(i\right)*x\left(i\right)$$ Coef(i) and x(i) represent the estimated regression values. Patients were then divided into high and low risk groups according to the median risk score. A Kaplan-Meier curve was plotted using the R package “survival” to compare survival differences between the two groups. A receiver operating characteristic (ROC) curve was drawn using the R package “survivalROC” to assess the predictive effect of the signature on overall survival (OS). Independent Risk Factor of the IFN-γ Signature Univariate and multivariate Cox regression analyses were used to determine if the IFN-γ-related signature was a risk factor independent of other clinicopathological information (sex, age, grade, and stage) in TCGA database. Patients were divided into subgroups according to age (> 65 and ≤ 65 years), sex (male and female), grade (G1/2 and G3/4), stage (I/II and III/IV), and risk (high and low risk) according to TCGA database. OS analysis was performed for each subgroup using the R package “survival”. Nomogram of the IFN-γ-related Signature We also constructed a nomogram of the most influential prognostic and clinical characteristics of IFN-γ-response genes, such as age and pathological TNM stage, which could be used to calculate the risk of recurrence in an individual patient using the “rms” R package. The nomogram was established based on the results of multivariate Cox proportional hazards analysis, to predict survival recurrence at 1, 3, and 5 years. Functional Enrichment and Signaling Pathway Analysis We evaluated immune infiltration in the model by dividing the BC patients in TCGA into high- and low-risk groups according to their IFN-γ-related signature, and applied Gene Ontology (GO) enrichment analysis to identify the related biological processes. The main signaling pathways regulated by the signature were established by KEGG analysis. Immune Cell Infiltration We explored cell infiltration among the immune subtypes of BC patients in TCGA database using the immune R package normalized via the “limma” package. CIBERSORT algorithms were used to evaluate immune infiltration. The correlations between target gene expression and immune cell infiltration levels were assessed using Spearman’s test. Differences in infiltration between the high and low risk groups were calculated by Wilcoxon’s rank-sum test and the results were presented using the “vioplot” package. Antineoplastic Drug Sensitivity of the Model We downloaded the gene expression file RNA-seq and NCI-60 drug sensitivity file via CellMiner ( https://discover.nci.nih.gov/cellminer/ ), and selected drugs with USA Food and Drug Administration approval to evaluate the relationship between the IFN-γ-related signature and the therapeutic effects of antineoplastic drugs in BC patients. Statistical Analysis Statistical analyses were performed using R software 4.0.2. All statistical analyses were two-sided, and a value of p < 0.05 was considered significant. The associations between gene expression and clinicopathological data were evaluated using Wilcoxon’s rank sum test and visualized using ggplot2 R package. Results Construction of the IFN-γ Response Gene Signature We selected 24 IFN-γ response genes. The patient characteristics from TCGA are shown in Table 1. We screened out nine IFN-γ response genes by univariate Cox regression analysis (Table 2). We selected genes with p < 0.05 and established a stepwise Cox regression model to optimize the signatures. Five genes were subsequently selected to construct IFN-γ-related signatures by stepwise Cox regression: RIPK2 , RBCK1 , PTPN6 , ITGB7 , and LATS2 . Among these five genes, LATS2 was a high-risk factor and RBCK1 , PTPN6 , ITGB7 , and RIPK2 were low risk factors. The risk score was formulated by the expression levels of the four genes and the Cox coefficient: risk score = -0.1972 × RIPK2–0 .2682 × RBCK1–2 .2664 × PTPN6–0 .6172 × ITGB7 + 0.3084 × LATS2. BC patients were divided into high risk and low risk groups based on the median risk score. The distribution characteristics and related risk scores of the five genes are shown in Figs. 1 and 2. Kaplan-Meier analysis was applied to evaluate the predictive value of OS in BC patients, and the results showed that OS was better in the low-risk group (Fig. 3A). The ROC curves for 5-year OS showing the prognostic accuracy for the IFN-γ response gene-related signature is shown in Fig. 3B (area under the curve [AUC] = 0.702). Independence of IFN-γ-related Signature as an Independent Risk Factor The clinical features of all BC patients were analyzed, including T, N, and M stage, grade, sex, and age (Fig. 4). The relationships between the five IFN-γ response signatures and the pathological features of BC were analyzed by univariate and multivariate Cox analyses according to TCGA database to confirm the independence of IFN-γ signature as a risk factor for BC (Fig. 5A, B). The pathological features age, lymph node (N), and risk score were significantly different in the high-risk group (age < 0.001, hazard ratio [HR] = 1.971; N < 0.001, HR = 2.007; risk sore < 0.001, HR = 1.818). Based on the above data, the risk score was valid and the IFN-γ-related signatures were identified as an independent risk factor. Construction of Nomogram Predicting the Prognosis of BC patients To further optimize the prediction model and prove the good predictive effect of the IFN-γ signature, we established a nomogram for the prognosis of BC using the four independent factors (age, grade, stage, risk) that were most significantly related to BC. Univariate and multivariate Cox regression analyses were used to prove that the IFN-γ-related signature was an independent risk factor and had a significant prognostic role in BC patients. The nomogram combining age, sex, stage, and risk score predicted the 1-, 3-, and 5-year survival outcomes for BC patients, and indicated the scores for each risk factor (Fig. 6). Biological Pathways Related to the IFN-γ-related Signature We performed edgeR filtration (false discovery rate 1|) to further study the potential functions of the IFN-γ-related features by GO and KEGG pathway enrichment analyses. In the Biological Process (BP) category, T cell activation, regulation of T cell activation, skin development, extracellular matrix organization, extracellular structure organization, regulation of T cell activation, antigen processing and presentation of exogenous peptide antigen via MHC class II, antigen processing and presentation of peptide antigen via MHC class II, and antigen processing and presentation of peptide or polysaccharide antigen via MHC class II were all related to the IFN-γ signature. In the Cellular Component (CC) category, the IFN-γ-related signature was highly enriched in collagen-containing extracellular matrix, external side of plasma membrane, and MHC class II protein complex, and the extracellular matrix structural constituent was relatively enriched in the Molecular Function (MF) category (Fig. 7A). KEGG pathway enrichment analysis showed that the IFN-γ-related signature was highly enriched in pathways related to cell adhesion molecules, Epstein-Barr virus infection, rheumatoid arthritis, hematopoietic cell lineage, human T-cell leukemia virus 1 infection, cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, and Th17 cell differentiation (Fig. 7B). Immune Infiltration of the IFN-γ-related Signature The above results demonstrated that the IFN-γ-related signature was associated with immunity. We then used CIBERSORT to derive and further verify the relationship between the IFN-γ response signatures and immune-infiltration status in all BC patients in the high and low risk groups. According to Wilcoxon’s rank-sum test, M0 and M2 macrophages and resting mast cells were positively associated with the risk score (p < 0.001), while CD8 T cells, CD4 memory resting T cells, CD4 memory activated T cells, follicular helper T cells, and resting natural killer (NK) cells were negatively correlated with the risk score (Fig. 8). Antineoplastic Drug Sensitivity of the IFN-γ Signature Our results suggested that the IFN-γ signature was associated with immune infiltration. We therefore further analyzed the correlation between the signature and antineoplastic drug sensitivity. Sensitivities to alectinib, denileukin diftitox (Ontak), LDK-378, isotretinoin, fluphenazine, estramustine, and irofulven were significantly correlated with the IFN-related ITGB7 gene signature (Spearman’s ρ = 0.662, 0.612, 0.579, 0.538, 0.531, 0.510, -0.505, respectively, p < 0.001). LATS2 was also significantly correlated with irofulven sensitivity (ρ = 0.504, p < 0.001), and hydroxyurea sensitivity was significantly correlated with PTPN6 (ρ = 0.499, p < 0.001) (Fig. 9). Discussion BC is one of the most common and aggressive malignant diseases. Due to the unique urinary-storage function of the bladder, intravesical instillation was used to treat NMIBC in a BC patient in 1976, thus establishing bacillus Calmette-Guérin (BCG) instillation as the gold standard adjunctive therapy for NMIBC[ 16 ], and opening a new chapter in the immunotherapy of BC. BCG instillation and anti-programmed cell death protein 1 (PD-1)/PD-L1 immune-checkpoint blocking have been used successfully to treat early and late BC via different immunotherapeutic approaches [ 17 ], thus providing a good model for studying the mechanism of tumor immune response and improving the efficiency of immunotherapy. The development of high-throughput sequencing and biomolecular technology has facilitated breakthroughs in immunotherapy, making it a promising therapeutic approach for cancers. However, only 25% of advanced/metastatic BCs respond to anti-PD-1/PD-L1 ICIs [ 18 ], indicating the need to develop new immunotherapy approaches and predict new biomarkers to fully explore the curative potential of immunotherapy in patients with BC. IFN-γ stimulates the immune editing of tumor cells and modulates the tumor immune-resistance mechanism, thus promoting tumor progression. Immune activation of IFN-γ in tumor cells can promote lymphocyte migration and inhibit angiogenesis, mainly due to the influence of tumor cells, monocytes, endothelial cells, and fibroblasts, to induce the expression of MHC and secrete CXCL9, CXCL10, and CXCL11 [ 19 – 21 ]. In order to design better therapeutic targets, differentiate immunotherapy populations, and balance the antitumor and immune-escape abilities of BC, we therefore established an IFN-γ signature containing five genes ( RIPK2 , RBCK1 , PTPN6 , ITGB7 , LATS2 ) to assess the prognosis of BC patients. We explored the efficacy of this signature by combining the five genes and examining the survival and ROC curves, which showed that the IFN-γ signature had good prognostic performance (AUC = 0.702). We then established a nomogram using four independent factors (age, grade, stage, risk) that were most significantly related to the prognosis of BC, which confirmed the good predictive effect of the IFN-γ signature. The IFN-γ signature-related genes play an important role in immunobiological pathways. For example, T cells are the key cells in cellular immunity [ 22 ], with important roles in immune tolerance and immune homeostasis. High infiltration by Treg cells has been associated with poor survival in various types of cancer [ 23 ]. M2 macrophages are closely related to the growth and survival of various tumor cells [ 24 , 25 ], and exhausted T cells in the TME are major targets of immunotherapies in BC [ 26 ]. The current results showed that M0 and M2 macrophages and resting mast cells were positively associated with the risk score, suggesting that M0 and M2 macrophages were significantly up-regulated in the high-risk group, while CD8 T cells, CD4 memory resting T cells, CD4 memory activated T cells, follicular helper T cells, and resting NK cells were negatively correlated with risk scores. In addition, GO enrichment analysis showed that, in the BP category, T cell activation, regulation of T cell activation, regulation of T cell activation and presentation of exogenous peptide antigen via MHC class II, antigen processing and presentation of peptide antigen via MHC class II, and antigen processing and presentation of peptide or polysaccharide antigen via MHC class II were related to the IFN-γ-related signature, while the signature was highly enriched in MHC class II protein complex in the CC category. These results were consistent with the analysis of the IFN signature, and further confirmed the effectiveness of the signature and its risk profile for predicting tumor-infiltrating immune cells and guiding the selection of clinical immunotherapies. Integrin β7 ( ITGB7 ) is associated with immune cell infiltration. It is expressed on the surface of leukocytes and plays an important role in the homing of immune cells to intestinal-related lymphoid tissues and facilitating the retention of lymphocytes in the gut epithelium. The role of ITGB7 expression in promoting tumor progression has also been reported in different types of tumors, such as colorectal cancer, fibrosarcoma, multiple myeloma, pancreatic cancer, and cervical cancer [ 26 – 30 ]. A study of patients with colorectal cancer found a significant reduction in the number of β7 + cells in the tumor tissue compared with the adjacent normal tissue. β7 expression was decreased in tumor-derived CD8 + T cells compared with normal tissue-derived CD8 + T cells. In addition, analysis of bulk RNA expression data from a public platform showed that high ITGB7 expression was associated with longer patient survival, higher cytotoxic immune cell infiltration, lower somatic copy number alterations, decreased mutation frequencies of APC and TP53 , and a better immunotherapy response[ 31 ]. ITGB7 deficiency reduced the infiltration of activated CD8 + T cells, effector memory CD8 + T cells, IFNγ + CD8 + T cells, IFNγ + NK cells, and CD103 + dendritic cells, and thus accelerated the development and progression of colorectal cancer in Apc min /+ spontaneous and MC38 orthotopic models[ 32 ]. ITGB7 downregulation also inhibited focal adhesion kinase and Src phosphorylation in a cell co-culture model of multiple myeloma [ 28 ]. In pancreatic cancer, ITGB7 transcription was shown to be regulated in a reactive oxygen species-related nuclear factor erythroid 2-related factor 2-dependent manner, and ITGB7 was inhibited by N-acetyl-L-cysteine in pancreatic cancer cells, thus accelerating the progression of pancreatic cancer[ 29 ]. All the above evidence suggests that ITGB7 may inhibit cancer pathogenesis via maintaining antitumor immunity. Protein tyrosine phosphatase nonreceptor type 6 ( PTPN6 ) is a nonreceptor protein tyrosine phosphatase, which mainly acts as a tumor suppressor through phosphorylation of carcinogenic kinases[ 32 ]. PTPN6 was shown to be associated with the prognosis and progression of gastric cancer and hepatocellular carcinoma[ 33 , 34 ], and can be used as a prognostic factor in peripheral T cell lymphomas[ 35 ]. Recent studies suggested that PTPN6 was overexpressed in BC tissues and was significantly correlated with grade, T stage, N stage, and low PTPN6 expression was significantly associated with poorer OS in BC patients. Based on analysis of TCGA database, PTPN6 may be a new prognostic biomarker of BC [ 36 ]. LATS2 encodes a serine/threonine protein kinase and has been reported to be a member of the LATS tumor-suppressor gene family involved in the hippocampus signaling pathway[ 37 ]. The above results confirmed that the genes included in our signature were significantly correlated with tumor development and prognosis. In addition, the current drug sensitivity results confirmed the correlation between these genes and antineoplastic drug sensitivity. In this study, we determined the prognostic value of an IFN-γ signature in BC based on TCGA database. We also discussed the relationship between this IFN-γ signature and immune cell infiltration in the BC microenvironment. However, the study had several limitations. First, the sample size of TCGA database was limited. Second, this was a retrospective study and lacked experimental verification of the findings. Further clinical trials are therefore needed to confirm our observations, and to clarify the mechanism responsible for the prognostic value of the IFN-γ-related signature in BC. Conclusion In conclusion, this study investigated the risk characteristics of an IFN-γ signature. We identified a promising model for prognostic risk assessment in BC patients and improved the prognostic accuracy of the immune BC microenvironment. The model could serve as a powerful tool for guiding immunotherapy in BC patients. Declarations Acknowledgements We sincerely thank all the experts who participated in this study for their time and for sharing their expertise. All experts qualify for authorship based on the fact their involvement in data collection and all authors critically appraised the final manuscript for important intellectual content. We thank International Science Editing (http://www.internationalscienceediting.com) for editing a draft of this manuscript. Author contributions Y.L. and M.W. contributed equally to the literature research, drafting, interpretation, and writing of the manuscript. W.Z contributed to the supervision and writing of the manuscript. J.T and X.X. contributed to the literature research of the manuscript. All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work. Competing Interests The authors declare no competing financial interests. 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Lung, et al., RASAL2 promotes tumor progression through LATS2/YAP1 axis of hippo signaling pathway in colorectal cancer. Mol Cancer, 2018. 17 (1): p. 102. Tables Table 1. Bladder cancer patient characteristics for TCGA Characteristics Variable Total Percentages (%) Age ≤ 65 161 39.36 ＞65 248 60.34 Gender Male 303 74.08 Female 106 25.92 Grade High 385 94.13 Low 21 5.14 Unknown 3 0.73 Stage StageI 2 0.49 StageII 130 31.78 StageIII 139 33.99 StageIV 136 33.25 Unknown 2 0.49 T T0 1 0.24 T1 3 0.73 T2 120 29.34 T3 194 47.43 T4 59 14.43 TX 1 0.24 Unknown 31 7.58 N N0 237 57.95 N1 47 11.49 N2 76 18.58 N3 8 1.96 NX 36 8.80 Unknown 5 1.22 M M0 194 47.43 M1 11 2.69 MX 202 49.39 Unknown 2 0.49 Survival rate Survival 251 61.37 Dead 158 38.63 Table 2. 24 IFN-γ Response Genes ssociated with patients’OS. gene HR z pvalue CD69 0.90576 -1.2449 0.213169 CD74 0.903791 -2.24966 0.02447 CD86 0.92398 -0.96258 0.335756 CDKN1A 0.993033 -0.09185 0.926818 CIITA 0.755691 -2.82052 0.004795 CSF2RB 1.054883 0.754494 0.450553 IL10RA 0.919944 -1.00846 0.313235 IRF4 0.738926 -1.9931 0.046251 IRF8 0.95445 -0.59747 0.550196 ITGB7 0.46529 -3.96765 7.26E-05 LATS2 1.307091 2.16621 0.030295 LCP2 0.899599 -1.13229 0.257513 MT2A 1.035004 0.87063 0.383956 NMI 0.834622 -1.82505 0.067993 NOD1 0.903036 -0.54127 0.588323 OAS3 0.875975 -1.81542 0.069459 PFKP 1.143493 1.557691 0.119306 PNP 1.25711 1.954446 0.050648 PTPN6 0.56505 -4.74742 2.06E-06 RBCK1 0.618663 -3.207 0.001341 RIPK2 0.758894 -2.16129 0.030673 SELP 0.992782 -0.08349 0.933466 SOD2 0.936558 -0.87892 0.379443 TRAFD1 0.617443 -3.28903 0.001005 HR,hazard ratio; Z, Z test. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1343457","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":85014437,"identity":"827c4c9b-5cd5-4101-ad5e-6d58ba0fd567","order_by":0,"name":"yongchang 刘永昌","email":"","orcid":"","institution":"Hangzhou First People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"yongchang","middleName":"","lastName":"刘永昌","suffix":""},{"id":85014438,"identity":"60cf8c68-0577-41e1-b130-1fa15ca8bd80","order_by":1,"name":"Wenhao Zhang","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Zhang","suffix":""},{"id":85014439,"identity":"d5a64a55-ab9b-4521-a1e8-8c8c86459376","order_by":2,"name":"Xiaocheng Xu","email":"","orcid":"","institution":"The First People's Hospital of Xiaoshan District, Hangzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaocheng","middleName":"","lastName":"Xu","suffix":""},{"id":85014440,"identity":"4d77fd3d-9208-4287-b21c-74fd84f123d1","order_by":3,"name":"Jiabin Tai","email":"","orcid":"","institution":"The First People's Hospital of Xiaoshan District, Hangzhou","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Tai","suffix":""},{"id":85014441,"identity":"796ce802-4f7e-4429-8958-885a05a241ff","order_by":4,"name":"meiyun wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYFACxgYQKcfP3nzgwIcKErQYS/YcSzw44wwJdiVuuJFjfJi3hQiluu3NbdK8OxiMGc6c+XCAt4FBnl/sAH4tZmcOArWcYZBjbO/dcEByB4PhzNkJBLTcSARqaWMwZuY5u+GA4RmGBIPbhLTcfwjWktgmkfPgQGIbMVpuMEK09EjkMBw4SJSWM4nNlnOBDpPgOWZwsOGMBBF+OX784Y23bQxy9sebH3/+U2Ejzy9NQAsQsEgwMPyHcSQIKgcB5g9EKRsFo2AUjIKRCwCTq0p9G9lwTgAAAABJRU5ErkJggg==","orcid":"","institution":"The First People's Hospital of Xiaoshan District, Hangzhou","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"meiyun","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2022-02-09 15:59:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1343457/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1343457/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18540613,"identity":"0d87ea5c-e457-446f-95d5-68a9d2faf71f","added_by":"auto","created_at":"2022-02-23 20:11:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19387,"visible":true,"origin":"","legend":"\u003cp\u003eIFN-γ related signatures were constructed from TCGA database. (A, B) Contribution of risk score and survival status.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/a11dbd88f5c9b770438b5f8f.png"},{"id":18541376,"identity":"319b5de1-2946-4b34-a8de-c0d782b7a2f2","added_by":"auto","created_at":"2022-02-23 20:17:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121232,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between survival status and gene expression of the IFN-γ signature. PTPN6 and RIPK2 were highly expressed in high-risk group. ITGB7，LATS2 and RBCK1 were highly expressed in low-risk group.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/7d6fae612322eaf831727740.png"},{"id":18540611,"identity":"2299a5b7-5f7f-4969-a358-0db7a4b8bb12","added_by":"auto","created_at":"2022-02-23 20:11:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28454,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the IFN-γ related signature by the TCGA database. (A) Kaplan-Meier survival curve of total patients OS for BC patients were divided into high and low risk groups based on IFN-γ related signatures. (B) ROC curve showing the values of the signature for 5-year OS amonAg BC patients\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/3de4946b82924140355fa034.png"},{"id":18540616,"identity":"25f2710d-e814-4f81-aae5-800d3ba194cf","added_by":"auto","created_at":"2022-02-23 20:11:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":100805,"visible":true,"origin":"","legend":"\u003cp\u003eAll 5 survival-related genes were significantly higher expressed (p \u0026lt; 0.001). The clinicopathological features, which included N, M,T, stage, grade, gender, age, fustat and futime, and the expressions of survival-related genes distributed in the heatmap of 2 defined clusters of BC patients. *p\u0026lt;0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/d789d9b98c8299e0d6c4bc9c.png"},{"id":18540989,"identity":"782fec81-ac27-4835-882b-d91041f5bb8f","added_by":"auto","created_at":"2022-02-23 20:14:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21833,"visible":true,"origin":"","legend":"\u003cp\u003eThe signature identified as an independent risk factor in BC patients. (A) Univariate regression analysis was used to calculate risk ratio (HR), risk score, and 95% confidence intervals for all clinical characteristics. (B) Multivariate regression analysis calculated risk ratio (HR), risk score, and 95% confidence intervals for all clinical characteristics.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/c582d31acde7b5cbc2f656be.png"},{"id":18541468,"identity":"14e400d5-d68a-46f6-8a3d-97107e386156","added_by":"auto","created_at":"2022-02-23 20:20:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":47288,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram predicting the prognosis of BC. (A) The contrast figure of between fraction surviving 1 years and predicted 1 year survivial with IFN-γ related signatures. (B) The contrast figure of between fraction surviving 3 years and predicted 3 year survivial with IFN-γ related signatures. (C) The contrast figure of between fraction surviving 5 years and predicted 5year survivial with IFN-γ related signatures.(D) The Nomogram shows the clinicopathological features and IFN-γ signatures response of the patients.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/1dd58b95c2e5c3ccbbf5be58.png"},{"id":18540987,"identity":"60a2c771-64f0-43ff-83ca-fe939a0dea3b","added_by":"auto","created_at":"2022-02-23 20:14:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":97158,"visible":true,"origin":"","legend":"\u003cp\u003eExplore of the Biological Pathway About the IFN-γ-Related Signature (A) GO enrichment analysis of the top 30 corresponding genes. (B) KEGG pathways was enriched in the top 30 corresponding genes.\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/001b9784540a83cf738c4614.png"},{"id":18540619,"identity":"04228e28-76c8-48db-9e74-cf7b2a7d694c","added_by":"auto","created_at":"2022-02-23 20:11:33","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":39296,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in the abundance of immune cell infiltrates between high-risk and low-risk patients. Red represents high-risk patients. Green is low-risk patients.\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/64ced02ebc78d9f2001c15f6.png"},{"id":18541378,"identity":"37e06729-0a2c-4eb4-a5dd-4532d9efe2dd","added_by":"auto","created_at":"2022-02-23 20:17:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":41385,"visible":true,"origin":"","legend":"\u003cp\u003eAntineoplastic Drug sensitivity of IFN-γ Signature\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/25f79d09c7454f5058b0f715.png"},{"id":19603760,"identity":"d7187b8f-f14e-4fc6-babf-99b4218fade7","added_by":"auto","created_at":"2022-03-25 08:59:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1135454,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1343457/v1/d3b6aa4e-dd96-4aba-9a40-686f0a8f31c9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic Value of Interferon-γ-Related Signature Involved in Tumor Immune Infiltration in Bladder Cancer\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder cancer (BC) is the fifth most common cancer and the most prevalent urinary tract cancer worldwide, with an estimated 81,400 new cases and 17,980 deaths in the United States in 2020[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A national registry of advanced cancer in Denmark (N\u0026thinsp;=\u0026thinsp;31,771) reported that BC was associated with higher risks of pain and constipation and a lower quality of life than other cancers[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. BC can present as non-muscle-invasive bladder cancer (NMIBC), muscle-invasive bladder cancer (MIBC), or as a metastatic form of the disease, of which MIBC (\u0026ge;\u0026thinsp;T2) usually progresses to metastasis and has a poor prognosis, with a 5-year survival rate of \u0026lt;\u0026thinsp;50% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBC is one the most common mutated cancers in humans, second only to lung and skin cancers in terms of mutation rates[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Sequencing and gene expression studies have revealed numerous DNA, RNA, and protein biomarkers for BC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, immune checkpoint inhibitors (ICIs) have demonstrated prognostic and therapeutic potential [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and have thus been approved for the treatment of metastatic BC, leading to renewed interest in the immune components of the tumor microenvironment (TME). Further information on the mechanisms underlying BC biogenesis and development is thus required, and reliable response biomarkers are needed to allow the selection of patients likely to benefit from such treatments.\u003c/p\u003e \u003cp\u003eInterferon-γ (IFN-γ) is the sole member of the type II IFN family discovered nearly 60 years ago. It is encoded by the \u003cem\u003eIFNG\u003c/em\u003e gene and consists of two polypeptide chains joined in an antiparallel fashion [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Wheelock first described IFN- γ as a phytohemagglutinin-induced viral inhibitor produced by leukocyte stimulation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, a recent review reported that IFN-γ was involved in tumor progression and regression[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], with a key role in activating cellular immunity and subsequent anti-tumor immune responses. However, IFN-γ can also lead to immune escape by inhibiting the T cell immune response and can induce programmed death-ligand 1 (PD-L1) and indolamine-2,3-dioxygenase expression and regulate tumor immune-resistance mechanisms. The role of IFN-γ signaling in regulating the immune state and anti-tumor immunity is thus controversial.\u003c/p\u003e \u003cp\u003ePatients with NMIBC have high rates of recurrence and progression[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies found that immunotherapy could help to avoid surgery in patients with high grade NMIBC, and pembrolizumab has been used in patients who have failed second-line therapy or who are intolerant to first-line platinum-based chemotherapy[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. PD-L1 was induced by typical IFN-γ signaling in clear cell renal cell carcinoma-like cell lines, and a high level of PD-L1 mRNA in tumor tissues was positively correlated with IFN-γ signature and was associated with a beneficial prognosis in renal cell cancer[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Changes in Toll-like receptor 4 expression were associated with changes in the expression of key cytokines (transforming growth factor-β, tumor necrosis factor-α, and IFN-γ), which affect tumor progression and metastasis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These results suggested that IFN-related genes may be used to guide the use of ICIs in patients with BC.\u003c/p\u003e \u003cp\u003eWe conducted this study to validate the hypothesis that IFN-γ promotes tumor immune infiltration in BC. Using The Cancer Genome Atlas (TCGA) database as a training set, we evaluated the mRNA expression data, clinical information, signaling pathways, and immune infiltration in patients with BC. We also constructed an optimized IFN-γ estimation model, and hypothesized that this could be used as a predictive marker for immunotherapy and as a prognostic biomarker for BC patients. We also verified the drug sensitivity of the model.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Data Extraction\u003c/h2\u003e \u003cp\u003eTranscriptome profiles and clinical information, including sex, age, clinicopathological characteristics, stage, and survival data for patients with BC were obtained with the HTSeq-FPKM format from TCGA database via the GDC portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e). Data collected from TCGA database were used as the training set for model construction. Exclusion criteria were patients with incomplete data. Drug sensitivity data were identified by NCI-60 and downloaded from the CellMiner dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of IFN-γ Signature Prediction Models\u003c/h2\u003e \u003cp\u003eWe first constructed a signature prediction estimation model. IFN-related genes were identified from relevant research and prognostic genes were further identified by univariate Cox analysis. Significant genes with a cut-off point of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected and a stepwise Cox regression model was established. On the basis of the results, we calculated the risk score using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{R}\\text{i}\\text{s}\\text{k} \\text{S}\\text{c}\\text{o}\\text{r}\\text{e}={\\sum }_{i=1}^{n}Coef\\left(i\\right)*x\\left(i\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCoef(i) and x(i) represent the estimated regression values. Patients were then divided into high and low risk groups according to the median risk score. A Kaplan-Meier curve was plotted using the R package \u0026ldquo;survival\u0026rdquo; to compare survival differences between the two groups. A receiver operating characteristic (ROC) curve was drawn using the R package \u0026ldquo;survivalROC\u0026rdquo; to assess the predictive effect of the signature on overall survival (OS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Risk Factor of the IFN-γ Signature\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate Cox regression analyses were used to determine if the IFN-γ-related signature was a risk factor independent of other clinicopathological information (sex, age, grade, and stage) in TCGA database. Patients were divided into subgroups according to age (\u0026gt;\u0026thinsp;65 and \u0026le;\u0026thinsp;65 years), sex (male and female), grade (G1/2 and G3/4), stage (I/II and III/IV), and risk (high and low risk) according to TCGA database. OS analysis was performed for each subgroup using the R package \u0026ldquo;survival\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNomogram of the IFN-γ-related Signature\u003c/h2\u003e \u003cp\u003eWe also constructed a nomogram of the most influential prognostic and clinical characteristics of IFN-γ-response genes, such as age and pathological TNM stage, which could be used to calculate the risk of recurrence in an individual patient using the \u0026ldquo;rms\u0026rdquo; R package. The nomogram was established based on the results of multivariate Cox proportional hazards analysis, to predict survival recurrence at 1, 3, and 5 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment and Signaling Pathway Analysis\u003c/h2\u003e \u003cp\u003eWe evaluated immune infiltration in the model by dividing the BC patients in TCGA into high- and low-risk groups according to their IFN-γ-related signature, and applied Gene Ontology (GO) enrichment analysis to identify the related biological processes. The main signaling pathways regulated by the signature were established by KEGG analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune Cell Infiltration\u003c/h2\u003e \u003cp\u003eWe explored cell infiltration among the immune subtypes of BC patients in TCGA database using the immune R package normalized via the \u0026ldquo;limma\u0026rdquo; package. CIBERSORT algorithms were used to evaluate immune infiltration. The correlations between target gene expression and immune cell infiltration levels were assessed using Spearman\u0026rsquo;s test. Differences in infiltration between the high and low risk groups were calculated by Wilcoxon\u0026rsquo;s rank-sum test and the results were presented using the \u0026ldquo;vioplot\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAntineoplastic Drug Sensitivity of the Model\u003c/h2\u003e \u003cp\u003eWe downloaded the gene expression file RNA-seq and NCI-60 drug sensitivity file via CellMiner (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/\u003c/span\u003e\u003c/span\u003e), and selected drugs with USA Food and Drug Administration approval to evaluate the relationship between the IFN-γ-related signature and the therapeutic effects of antineoplastic drugs in BC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software 4.0.2. All statistical analyses were two-sided, and a value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. The associations between gene expression and clinicopathological data were evaluated using Wilcoxon\u0026rsquo;s rank sum test and visualized using ggplot2 R package.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the IFN-γ Response Gene Signature\u003c/h2\u003e \u003cp\u003eWe selected 24 IFN-γ response genes. The patient characteristics from TCGA are shown in Table\u0026nbsp;1. We screened out nine IFN-γ response genes by univariate Cox regression analysis (Table\u0026nbsp;2). We selected genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and established a stepwise Cox regression model to optimize the signatures. Five genes were subsequently selected to construct IFN-γ-related signatures by stepwise Cox regression: \u003cem\u003eRIPK2\u003c/em\u003e, \u003cem\u003eRBCK1\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, \u003cem\u003eITGB7\u003c/em\u003e, and \u003cem\u003eLATS2\u003c/em\u003e. Among these five genes, \u003cem\u003eLATS2\u003c/em\u003e was a high-risk factor and \u003cem\u003eRBCK1\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, \u003cem\u003eITGB7\u003c/em\u003e, and \u003cem\u003eRIPK2\u003c/em\u003e were low risk factors. The risk score was formulated by the expression levels of the four genes and the Cox coefficient: risk score = -0.1972 \u0026times; \u003cem\u003eRIPK2\u0026ndash;0\u003c/em\u003e.2682 \u0026times; \u003cem\u003eRBCK1\u0026ndash;2\u003c/em\u003e.2664 \u0026times; \u003cem\u003ePTPN6\u0026ndash;0\u003c/em\u003e.6172 \u0026times; \u003cem\u003eITGB7\u003c/em\u003e\u0026thinsp;+\u0026thinsp;0.3084 \u0026times; \u003cem\u003eLATS2.\u003c/em\u003e BC patients were divided into high risk and low risk groups based on the median risk score. The distribution characteristics and related risk scores of the five genes are shown in Figs.\u0026nbsp;1 and 2. Kaplan-Meier analysis was applied to evaluate the predictive value of OS in BC patients, and the results showed that OS was better in the low-risk group (Fig.\u0026nbsp;3A). The ROC curves for 5-year OS showing the prognostic accuracy for the IFN-γ response gene-related signature is shown in Fig.\u0026nbsp;3B (area under the curve [AUC]\u0026thinsp;=\u0026thinsp;0.702).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIndependence of IFN-γ-related Signature as an Independent Risk Factor\u003c/h2\u003e \u003cp\u003eThe clinical features of all BC patients were analyzed, including T, N, and M stage, grade, sex, and age (Fig.\u0026nbsp;4). The relationships between the five IFN-γ response signatures and the pathological features of BC were analyzed by univariate and multivariate Cox analyses according to TCGA database to confirm the independence of IFN-γ signature as a risk factor for BC (Fig.\u0026nbsp;5A, B). The pathological features age, lymph node (N), and risk score were significantly different in the high-risk group (age\u0026thinsp;\u0026lt;\u0026thinsp;0.001, hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.971; N\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;2.007; risk sore\u0026thinsp;\u0026lt;\u0026thinsp;0.001, HR\u0026thinsp;=\u0026thinsp;1.818). Based on the above data, the risk score was valid and the IFN-γ-related signatures were identified as an independent risk factor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Nomogram Predicting the Prognosis of BC patients\u003c/h2\u003e \u003cp\u003eTo further optimize the prediction model and prove the good predictive effect of the IFN-γ signature, we established a nomogram for the prognosis of BC using the four independent factors (age, grade, stage, risk) that were most significantly related to BC. Univariate and multivariate Cox regression analyses were used to prove that the IFN-γ-related signature was an independent risk factor and had a significant prognostic role in BC patients. The nomogram combining age, sex, stage, and risk score predicted the 1-, 3-, and 5-year survival outcomes for BC patients, and indicated the scores for each risk factor (Fig.\u0026nbsp;6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiological Pathways Related to the IFN-γ-related Signature\u003c/h2\u003e \u003cp\u003eWe performed edgeR filtration (false discovery rate\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003efold change\u0026thinsp;\u0026gt;\u0026thinsp;1|) to further study the potential functions of the IFN-γ-related features by GO and KEGG pathway enrichment analyses. In the Biological Process (BP) category, T cell activation, regulation of T cell activation, skin development, extracellular matrix organization, extracellular structure organization, regulation of T cell activation, antigen processing and presentation of exogenous peptide antigen via MHC class II, antigen processing and presentation of peptide antigen via MHC class II, and antigen processing and presentation of peptide or polysaccharide antigen via MHC class II were all related to the IFN-γ signature. In the Cellular Component (CC) category, the IFN-γ-related signature was highly enriched in collagen-containing extracellular matrix, external side of plasma membrane, and MHC class II protein complex, and the extracellular matrix structural constituent was relatively enriched in the Molecular Function (MF) category (Fig.\u0026nbsp;7A). KEGG pathway enrichment analysis showed that the IFN-γ-related signature was highly enriched in pathways related to cell adhesion molecules, Epstein-Barr virus infection, rheumatoid arthritis, hematopoietic cell lineage, human T-cell leukemia virus 1 infection, cytokine-cytokine receptor interaction, Th1 and Th2 cell differentiation, and Th17 cell differentiation (Fig.\u0026nbsp;7B).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImmune Infiltration of the IFN-γ-related Signature\u003c/h2\u003e \u003cp\u003eThe above results demonstrated that the IFN-γ-related signature was associated with immunity. We then used CIBERSORT to derive and further verify the relationship between the IFN-γ response signatures and immune-infiltration status in all BC patients in the high and low risk groups. According to Wilcoxon\u0026rsquo;s rank-sum test, M0 and M2 macrophages and resting mast cells were positively associated with the risk score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while CD8 T cells, CD4 memory resting T cells, CD4 memory activated T cells, follicular helper T cells, and resting natural killer (NK) cells were negatively correlated with the risk score (Fig.\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAntineoplastic Drug Sensitivity of the IFN-γ Signature\u003c/h2\u003e \u003cp\u003eOur results suggested that the IFN-γ signature was associated with immune infiltration. We therefore further analyzed the correlation between the signature and antineoplastic drug sensitivity. Sensitivities to alectinib, denileukin diftitox (Ontak), LDK-378, isotretinoin, fluphenazine, estramustine, and irofulven were significantly correlated with the IFN-related \u003cem\u003eITGB7\u003c/em\u003e gene signature (Spearman\u0026rsquo;s ρ\u0026thinsp;=\u0026thinsp;0.662, 0.612, 0.579, 0.538, 0.531, 0.510, -0.505, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eLATS2\u003c/em\u003e was also significantly correlated with irofulven sensitivity (ρ\u0026thinsp;=\u0026thinsp;0.504, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and hydroxyurea sensitivity was significantly correlated with \u003cem\u003ePTPN6\u003c/em\u003e (ρ\u0026thinsp;=\u0026thinsp;0.499, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;9).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBC is one of the most common and aggressive malignant diseases. Due to the unique urinary-storage function of the bladder, intravesical instillation was used to treat NMIBC in a BC patient in 1976, thus establishing bacillus Calmette-Gu\u0026eacute;rin (BCG) instillation as the gold standard adjunctive therapy for NMIBC[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and opening a new chapter in the immunotherapy of BC. BCG instillation and anti-programmed cell death protein 1 (PD-1)/PD-L1 immune-checkpoint blocking have been used successfully to treat early and late BC via different immunotherapeutic approaches [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], thus providing a good model for studying the mechanism of tumor immune response and improving the efficiency of immunotherapy.\u003c/p\u003e \u003cp\u003eThe development of high-throughput sequencing and biomolecular technology has facilitated breakthroughs in immunotherapy, making it a promising therapeutic approach for cancers. However, only 25% of advanced/metastatic BCs respond to anti-PD-1/PD-L1 ICIs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], indicating the need to develop new immunotherapy approaches and predict new biomarkers to fully explore the curative potential of immunotherapy in patients with BC.\u003c/p\u003e \u003cp\u003eIFN-γ stimulates the immune editing of tumor cells and modulates the tumor immune-resistance mechanism, thus promoting tumor progression. Immune activation of IFN-γ in tumor cells can promote lymphocyte migration and inhibit angiogenesis, mainly due to the influence of tumor cells, monocytes, endothelial cells, and fibroblasts, to induce the expression of MHC and secrete CXCL9, CXCL10, and CXCL11 [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In order to design better therapeutic targets, differentiate immunotherapy populations, and balance the antitumor and immune-escape abilities of BC, we therefore established an IFN-γ signature containing five genes (\u003cem\u003eRIPK2\u003c/em\u003e, \u003cem\u003eRBCK1\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, \u003cem\u003eITGB7\u003c/em\u003e, \u003cem\u003eLATS2\u003c/em\u003e) to assess the prognosis of BC patients.\u003c/p\u003e \u003cp\u003eWe explored the efficacy of this signature by combining the five genes and examining the survival and ROC curves, which showed that the IFN-γ signature had good prognostic performance (AUC\u0026thinsp;=\u0026thinsp;0.702). We then established a nomogram using four independent factors (age, grade, stage, risk) that were most significantly related to the prognosis of BC, which confirmed the good predictive effect of the IFN-γ signature.\u003c/p\u003e \u003cp\u003eThe IFN-γ signature-related genes play an important role in immunobiological pathways. For example, T cells are the key cells in cellular immunity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with important roles in immune tolerance and immune homeostasis. High infiltration by Treg cells has been associated with poor survival in various types of cancer [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. M2 macrophages are closely related to the growth and survival of various tumor cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and exhausted T cells in the TME are major targets of immunotherapies in BC [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The current results showed that M0 and M2 macrophages and resting mast cells were positively associated with the risk score, suggesting that M0 and M2 macrophages were significantly up-regulated in the high-risk group, while CD8 T cells, CD4 memory resting T cells, CD4 memory activated T cells, follicular helper T cells, and resting NK cells were negatively correlated with risk scores. In addition, GO enrichment analysis showed that, in the BP category, T cell activation, regulation of T cell activation, regulation of T cell activation and presentation of exogenous peptide antigen via MHC class II, antigen processing and presentation of peptide antigen via MHC class II, and antigen processing and presentation of peptide or polysaccharide antigen via MHC class II were related to the IFN-γ-related signature, while the signature was highly enriched in MHC class II protein complex in the CC category. These results were consistent with the analysis of the IFN signature, and further confirmed the effectiveness of the signature and its risk profile for predicting tumor-infiltrating immune cells and guiding the selection of clinical immunotherapies.\u003c/p\u003e \u003cp\u003eIntegrin β7 (\u003cem\u003eITGB7\u003c/em\u003e) is associated with immune cell infiltration. It is expressed on the surface of leukocytes and plays an important role in the homing of immune cells to intestinal-related lymphoid tissues and facilitating the retention of lymphocytes in the gut epithelium. The role of \u003cem\u003eITGB7\u003c/em\u003e expression in promoting tumor progression has also been reported in different types of tumors, such as colorectal cancer, fibrosarcoma, multiple myeloma, pancreatic cancer, and cervical cancer [\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A study of patients with colorectal cancer found a significant reduction in the number of β7\u0026thinsp;+\u0026thinsp;cells in the tumor tissue compared with the adjacent normal tissue. β7 expression was decreased in tumor-derived CD8\u0026thinsp;+\u0026thinsp;T cells compared with normal tissue-derived CD8\u0026thinsp;+\u0026thinsp;T cells. In addition, analysis of bulk RNA expression data from a public platform showed that high \u003cem\u003eITGB7\u003c/em\u003e expression was associated with longer patient survival, higher cytotoxic immune cell infiltration, lower somatic copy number alterations, decreased mutation frequencies of \u003cem\u003eAPC\u003c/em\u003e and \u003cem\u003eTP53\u003c/em\u003e, and a better immunotherapy response[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. \u003cem\u003eITGB7\u003c/em\u003e deficiency reduced the infiltration of activated CD8\u0026thinsp;+\u0026thinsp;T cells, effector memory CD8\u0026thinsp;+\u0026thinsp;T cells, IFNγ\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells, IFNγ\u0026thinsp;+\u0026thinsp;NK cells, and CD103\u0026thinsp;+\u0026thinsp;dendritic cells, and thus accelerated the development and progression of colorectal cancer in \u003cem\u003eApc\u003c/em\u003e\u003csup\u003e\u003cem\u003emin\u003c/em\u003e/+\u003c/sup\u003e spontaneous and MC38 orthotopic models[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eITGB7\u003c/em\u003e downregulation also inhibited focal adhesion kinase and Src phosphorylation in a cell co-culture model of multiple myeloma [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In pancreatic cancer, \u003cem\u003eITGB7\u003c/em\u003e transcription was shown to be regulated in a reactive oxygen species-related nuclear factor erythroid 2-related factor 2-dependent manner, and \u003cem\u003eITGB7\u003c/em\u003e was inhibited by N-acetyl-L-cysteine in pancreatic cancer cells, thus accelerating the progression of pancreatic cancer[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. All the above evidence suggests that \u003cem\u003eITGB7\u003c/em\u003e may inhibit cancer pathogenesis via maintaining antitumor immunity.\u003c/p\u003e \u003cp\u003eProtein tyrosine phosphatase nonreceptor type 6 (\u003cem\u003ePTPN6\u003c/em\u003e) is a nonreceptor protein tyrosine phosphatase, which mainly acts as a tumor suppressor through phosphorylation of carcinogenic kinases[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003ePTPN6\u003c/em\u003e was shown to be associated with the prognosis and progression of gastric cancer and hepatocellular carcinoma[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and can be used as a prognostic factor in peripheral T cell lymphomas[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Recent studies suggested that \u003cem\u003ePTPN6\u003c/em\u003e was overexpressed in BC tissues and was significantly correlated with grade, T stage, N stage, and low \u003cem\u003ePTPN6\u003c/em\u003e expression was significantly associated with poorer OS in BC patients. Based on analysis of TCGA database, \u003cem\u003ePTPN6\u003c/em\u003e may be a new prognostic biomarker of BC [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. \u003cem\u003eLATS2\u003c/em\u003e encodes a serine/threonine protein kinase and has been reported to be a member of the \u003cem\u003eLATS\u003c/em\u003e tumor-suppressor gene family involved in the hippocampus signaling pathway[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The above results confirmed that the genes included in our signature were significantly correlated with tumor development and prognosis. In addition, the current drug sensitivity results confirmed the correlation between these genes and antineoplastic drug sensitivity.\u003c/p\u003e \u003cp\u003eIn this study, we determined the prognostic value of an IFN-γ signature in BC based on TCGA database. We also discussed the relationship between this IFN-γ signature and immune cell infiltration in the BC microenvironment. However, the study had several limitations. First, the sample size of TCGA database was limited. Second, this was a retrospective study and lacked experimental verification of the findings. Further clinical trials are therefore needed to confirm our observations, and to clarify the mechanism responsible for the prognostic value of the IFN-γ-related signature in BC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study investigated the risk characteristics of an IFN-γ signature. We identified a promising model for prognostic risk assessment in BC patients and improved the prognostic accuracy of the immune BC microenvironment. The model could serve as a powerful tool for guiding immunotherapy in BC patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the experts who participated in this study for their time and for sharing their expertise. All experts qualify for authorship based on the fact their involvement in data collection and all authors critically appraised the final manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eWe thank International Science Editing (http://www.internationalscienceediting.com) for editing a draft of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L. and M.W. contributed equally to the literature research, drafting, interpretation, and writing of the manuscript. W.Z contributed to the supervision and writing of the manuscript. J.T and X.X. contributed to the literature research of the manuscript. All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R.L., K.D. Miller, and A. Jemal, \u003cem\u003eCancer statistics, 2020.\u003c/em\u003e CA Cancer J Clin, 2020. \u003cstrong\u003e70\u003c/strong\u003e(1): p. 7-30.\u003c/li\u003e\n\u003cli\u003eHansen, M.B., L. 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Onyshchenko, et al., \u003cem\u003eConserved Interferon-gamma Signaling Drives Clinical Response to Immune Checkpoint Blockade Therapy in Melanoma.\u003c/em\u003e Cancer Cell, 2020. \u003cstrong\u003e38\u003c/strong\u003e(4): p. 500-515 e3.\u003c/li\u003e\n\u003cli\u003eKanda, N., T. Shimizu, Y. Tada, et al., \u003cem\u003eIL-18 enhances IFN-gamma-induced production of CXCL9, CXCL10, and CXCL11 in human keratinocytes.\u003c/em\u003e Eur J Immunol, 2007. \u003cstrong\u003e37\u003c/strong\u003e(2): p. 338-50.\u003c/li\u003e\n\u003cli\u003eMuntjewerff, E.M., L.D. Meesters, G. van den Bogaart, et al., \u003cem\u003eReverse Signaling by MHC-I Molecules in Immune and Non-Immune Cell Types.\u003c/em\u003e Front Immunol, 2020. \u003cstrong\u003e11\u003c/strong\u003e: p. 605958.\u003c/li\u003e\n\u003cli\u003eWinter, H., N.K. van den Engel, D. Ruttinger, et al., \u003cem\u003eTherapeutic T cells induce tumor-directed chemotaxis of innate immune cells through tumor-specific secretion of chemokines and stimulation of B16BL6 melanoma to secrete chemokines.\u003c/em\u003e J Transl Med, 2007. \u003cstrong\u003e5\u003c/strong\u003e: p. 56.\u003c/li\u003e\n\u003cli\u003eOhue, Y. and H. Nishikawa, \u003cem\u003eRegulatory T (Treg) cells in cancer: Can Treg cells be a new therapeutic target?\u003c/em\u003e Cancer Sci, 2019. \u003cstrong\u003e110\u003c/strong\u003e(7): p. 2080-2089.\u003c/li\u003e\n\u003cli\u003eChang, C.I., J.C. Liao, and L. Kuo, \u003cem\u003eMacrophage arginase promotes tumor cell growth and suppresses nitric oxide-mediated tumor cytotoxicity.\u003c/em\u003e Cancer Res, 2001. \u003cstrong\u003e61\u003c/strong\u003e(3): p. 1100-6.\u003c/li\u003e\n\u003cli\u003eLan, J., L. Sun, F. Xu, et al., \u003cem\u003eM2 Macrophage-Derived Exosomes Promote Cell Migration and Invasion in Colon Cancer.\u003c/em\u003e Cancer Res, 2019. \u003cstrong\u003e79\u003c/strong\u003e(1): p. 146-158.\u003c/li\u003e\n\u003cli\u003eHan, H.S., S. Jeong, H. Kim, et al., \u003cem\u003eTOX-expressing terminally exhausted tumor-infiltrating CD8(+) T cells are reinvigorated by co-blockade of PD-1 and TIGIT in bladder cancer.\u003c/em\u003e Cancer Lett, 2021. \u003cstrong\u003e499\u003c/strong\u003e: p. 137-147.\u003c/li\u003e\n\u003cli\u003eKielosto, M., P. Nummela, K. Jarvinen, et al., \u003cem\u003eIdentification of integrins alpha6 and beta7 as c-Jun- and transformation-relevant genes in highly invasive fibrosarcoma cells.\u003c/em\u003e Int J Cancer, 2009. \u003cstrong\u003e125\u003c/strong\u003e(5): p. 1065-73.\u003c/li\u003e\n\u003cli\u003eNeri, P., L. Ren, A.K. Azab, et al., \u003cem\u003eIntegrin beta7-mediated regulation of multiple myeloma cell adhesion, migration, and invasion.\u003c/em\u003e Blood, 2011. \u003cstrong\u003e117\u003c/strong\u003e(23): p. 6202-13.\u003c/li\u003e\n\u003cli\u003eSun, Q., Z. Ye, Y. Qin, et al., \u003cem\u003eOncogenic function of TRIM2 in pancreatic cancer by activating ROS-related NRF2/ITGB7/FAK axis.\u003c/em\u003e Oncogene, 2020. \u003cstrong\u003e39\u003c/strong\u003e(42): p. 6572-6588.\u003c/li\u003e\n\u003cli\u003eChai, Z., Y. Yang, Z. Gu, et al., \u003cem\u003eRecombinant Viral Capsid Protein L2 (rVL2) of HPV 16 Suppresses Cell Proliferation and Glucose Metabolism via ITGB7/C/EBPbeta Signaling Pathway in Cervical Cancer Cell Lines.\u003c/em\u003e Onco Targets Ther, 2019. \u003cstrong\u003e12\u003c/strong\u003e: p. 10415-10425.\u003c/li\u003e\n\u003cli\u003eZhang, Y., R. Xie, H. Zhang, et al., \u003cem\u003eIntegrin beta7 Inhibits Colorectal Cancer Pathogenesis via Maintaining Antitumor Immunity.\u003c/em\u003e Cancer Immunol Res, 2021. \u003cstrong\u003e9\u003c/strong\u003e(8): p. 967-980.\u003c/li\u003e\n\u003cli\u003eLiu, C.Y., J.C. Su, T.T. Huang, et al., \u003cem\u003eSorafenib analogue SC-60 induces apoptosis through the SHP-1/STAT3 pathway and enhances docetaxel cytotoxicity in triple-negative breast cancer cells.\u003c/em\u003e Mol Oncol, 2017. \u003cstrong\u003e11\u003c/strong\u003e(3): p. 266-279.\u003c/li\u003e\n\u003cli\u003eWen, L.Z., K. Ding, Z.R. Wang, et al., \u003cem\u003eSHP-1 Acts as a Tumor Suppressor in Hepatocarcinogenesis and HCC Progression.\u003c/em\u003e Cancer Res, 2018. \u003cstrong\u003e78\u003c/strong\u003e(16): p. 4680-4691.\u003c/li\u003e\n\u003cli\u003eHuang, Z., Y. Cai, C. Yang, et al., \u003cem\u003eKnockdown of RNF6 inhibits gastric cancer cell growth by suppressing STAT3 signaling.\u003c/em\u003e Onco Targets Ther, 2018. \u003cstrong\u003e11\u003c/strong\u003e: p. 6579-6587.\u003c/li\u003e\n\u003cli\u003eHan, J.J., M. O'Byrne, M.J. Stenson, et al., \u003cem\u003ePrognostic and therapeutic significance of phosphorylated STAT3 and protein tyrosine phosphatase-6 in peripheral-T cell lymphoma.\u003c/em\u003e Blood Cancer J, 2018. \u003cstrong\u003e8\u003c/strong\u003e(11): p. 110.\u003c/li\u003e\n\u003cli\u003eShen, C., J. Liu, J. Wang, et al., \u003cem\u003eThe Analysis of PTPN6 for Bladder Cancer: An Exploratory Study Based on TCGA.\u003c/em\u003e Dis Markers, 2020. \u003cstrong\u003e2020\u003c/strong\u003e: p. 4312629.\u003c/li\u003e\n\u003cli\u003ePan, Y., J.H.M. Tong, R.W.M. Lung, et al., \u003cem\u003eRASAL2 promotes tumor progression through LATS2/YAP1 axis of hippo signaling pathway in colorectal cancer.\u003c/em\u003e Mol Cancer, 2018. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 102.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Bladder cancer patient characteristics for TCGA\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eCharacteristics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003ePercentages (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026le; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e39.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e＞65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e60.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e74.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e25.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e94.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eStageI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eStageII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e31.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eStageIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e33.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eStageIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e33.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e29.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e47.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eTX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e57.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e18.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eNX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e47.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eMX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e49.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eSurvival rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e61.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25%\"\u003e\n \u003cp\u003e38.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. 24 IFN-\u0026gamma; Response Genes ssociated with patients\u0026rsquo;OS.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003egene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eCD69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.90576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-1.2449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.213169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eCD74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.903791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-2.24966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.02447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eCD86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.92398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-0.96258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.335756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eCDKN1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.993033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-0.09185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.926818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eCIITA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.755691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-2.82052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.004795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eCSF2RB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1.054883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e0.754494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.450553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eIL10RA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.919944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-1.00846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.313235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eIRF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.738926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-1.9931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.046251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eIRF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.95445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-0.59747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.550196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eITGB7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.46529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-3.96765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e7.26E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eLATS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1.307091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e2.16621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.030295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eLCP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.899599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-1.13229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.257513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eMT2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1.035004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e0.87063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.383956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eNMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.834622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-1.82505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.067993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eNOD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.903036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-0.54127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.588323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eOAS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.875975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-1.81542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.069459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003ePFKP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1.143493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e1.557691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.119306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003ePNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e1.25711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e1.954446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.050648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003ePTPN6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.56505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-4.74742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e2.06E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eRBCK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.618663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-3.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.001341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eRIPK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.758894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-2.16129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.030673\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eSELP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.992782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-0.08349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.933466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eSOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.936558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-0.87892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.379443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eTRAFD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.21212121212121%\"\u003e\n \u003cp\u003e0.617443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.303030303030305%\"\u003e\n \u003cp\u003e-3.28903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.001005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHR,hazard ratio; Z, Z test.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Interferon-γ-related, Bladder cancer, prognostic model, genes, immunity.","lastPublishedDoi":"10.21203/rs.3.rs-1343457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1343457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe molecular biological characteristics and unique anatomical structure of bladder cancer (BC), become cancer immune reaction mechanism and a predictor of good model and help to improve the level of cancer immunotherapy. Interferon-γ (IFN-γ) plays a key role in activating cellular immunity and stimulating anti-tumor immune responses. However, the role of IFN-γ in BC is unclear. We aimed to clarify the biological occurrence and development of BC and identify reliable biomarkers.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eWe downloaded data on patients with BC from The Cancer Genome Atlas (TCGA) database and constructed a prognostic model. We analyzed the relationships between clinicopathological features and the IFN-γ signature by univariate and multivariate Cox regression analyses and evaluated the prognostic and predictive values of the IFN-γ signature by survival analysis and nomogram construction. We also conducted Gene Ontology (GO) and Kyoto Encyclopedia of and Genes and Genomes (KEGG) pathway enrichment analyses to explore the potential biological pathways related to the IFN-γ signature in BC. Immune infiltration was evaluated using CIBERSORT algorithms and the correlation between the signature and antineoplastic drug sensitivity was analyzed using the CellMiner platform.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFive genes (\u003cem\u003eRIPK2\u003c/em\u003e, \u003cem\u003eRBCK1\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, \u003cem\u003eITGB7\u003c/em\u003e, \u003cem\u003eLATS2\u003c/em\u003e) were selected to construct IFN-γ-related signatures. The pathological features were identified as an independent risk factor. Patients with BC were divided into high-risk and low-risk groups according to their signatures. Patients with higher risk scores had shorter overall survival and a worse prognosis. GO and KEGG enrichment and CIBERSORT analysis showed significant relationships between the signature and survival, independent risk factors, immune infiltration, and antineoplastic drug sensitivity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe obtained a risk profile for a regression model consisting of IFN-γ signature genes to predict the prognosis of patients with BC. This model may be used to improve the prognostic accuracy of the immune microenvironment in BC.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Interferon-γ-Related Signature Involved in Tumor Immune Infiltration in Bladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-23 20:11:31","doi":"10.21203/rs.3.rs-1343457/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"966d666d-3a64-4e25-967e-10aa256820fd","owner":[],"postedDate":"February 23rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-25T08:59:25+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-23 20:11:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1343457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1343457","identity":"rs-1343457","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}