Derivation and Comprehensive Analysis of Aging Patterns in Patients with Bladder Cancer.

Aging is an essential risk factor for cancer. However, aging-related genes (ARGs) have not been comprehensively analyzed in bladder cancer (BC). Therefore, the study is aimed at derivating a risk stratification system for BC patients based on ARGs. Public databases were used to acquire ARGs sets, transcriptome files, and clinical data. The "limma" package was then used to screen for differential ARGs while also using univariate Cox regression analysis to explore for prognostic ARGs. The "ConsensusClusterPlus" package was used to perform aging patterns in BC patients based on the above prognostic ARGs. Subsequently, aging patterns were investigated in survival prediction, mutation landscape, immunotherapy, immunological checkpoints, and immune microenvironment. We likewise utilized gene enrichment analysis to explore the biological functions that were behind the findings. To construct a risk signature and nonogram for prognostic prediction, we used LASSO and Cox regression analysis based on differential genes in aging patterns. In addition, we plotted a nomogram and validate the accuracy of the risk signature in GEO and TCGA cohorts. We explored the possible biological mechanism using GSEA analysis and preliminarily identified a hub gene using PPI network. Finally, we validated the expression of hub gene in BC cell lines. We screened 84 downregulated ARGs, 74 upregulated ARGs, and 32 prognostic ARGs in the human aging genome resource. The aging patterns based on prognostic genes had excellent survival prediction (p < 0.001) and discriminatory ability in 405 BC patients. In addition, we found no significant differences in aging patterns in mutation analysis, which were all characterized by TP53, TTN, and KMT2D mutations. It is worth noting that cluster B in the aging patterns has a better response to immunotherapy and a more active immune microenvironment (p < 0.05). In addition, gene enrichment analysis showed that aging patterns may be related to biological processes such as Staphylococcus aureus infection, phagosome, and cytokine-cytokine receptor interaction. Subsequently, we constructed a risk signature based on 16 differential genes from different aging patterns and had good survival prediction ability in both GEO and TCGA cohort. Specifically, survival analysis revealed a significantly shorter survival time in the high-risk group than in the low-risk group (TCGA and GEO, p < 0.001). In addition, AUC values in the ROC analysis predicted 1, 3, and 5 years in TCGA cohort that are 0.713, 0.714, and 0.738, respectively. AUC values predicted 1, 3, and 5 years in GEO cohort that are 0.606, 0.663, and 0.718, respectively. There is no doubt that risk score was an independent prognostic factor from results of multivariate Cox regression analysis in BC patients (p < 0.001). There were also significant differences in immune cell infiltration, immune checkpoint, and immune score between the two groups (p < 0.05), but it should not be ignored that the correlation with the HLA expression was weak. Finally, we identified and validated CLIC3 as a hub gene that may be involved in the Wnt signaling pathway, etc. We provided robust evidences that aging patterns based on ARGs can guide targeted therapy and survival prediction in BC patients.

Wang B ，Tong F ，Zhai C ，Wang L ，Liu Y ，Wang J ... -  《-》

Construction of a novel mRNA-signature prediction model for prognosis of bladder cancer based on a statistical analysis.

Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer. The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes. SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1-2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer. In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

Li J ，Cao J ，Li P ，Yao Z ，Deng R ，Ying L ，Tian J ... -  《BMC CANCER》

Establishment and validation of an aging-related risk signature associated with prognosis and tumor immune microenvironment in breast cancer.

Breast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated. The expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real-time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes. An 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR. In summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.

Wang Z ，Liu H ，Gong Y ，Cheng Y ... -  《-》

Comprehensive FGFR3 alteration-related transcriptomic characterization is involved in immune infiltration and correlated with prognosis and immunotherapy response of bladder cancer.

Bladder cancer (BC) threatens the health of human beings worldwide because of its high recurrence rate and mortality. As an actionable biomarker, fibroblast growth factor receptor 3 (FGFR3) alterations have been revealed as a vital biomarker and associated with favorable outcomes in BC. However, the comprehensive relationship between the FGFR3 alteration associated gene expression profile and the prognosis of BC remains ambiguous. Genomic alteration profile, gene expression data, and related clinical information of BC patients were downloaded from The Cancer Genomics database (TCGA), as a training cohort. Subsequently, the Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify the hub modules correlated with FGFR3 alteration. The univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to obtain an FGFR3 alteration-related gene (FARG) prognostic signature and FARG-based nomogram. The receiver operating characteristic (ROC) curve analysis was used for evaluation of the ability of prognosis prediction. The FARG signature was validated in four independent datasets, namely, GSE13507, GSE31684, GSE32548, and GSE48075, from Gene Expression Omnibus (GEO). Then, clinical feature association analysis, functional enrichment, genomic alteration enrichment, and tumor environment analysis were conducted to reveal differential clinical and molecular characterizations in different risk groups. Lastly, the treatment response was evaluated in the immunotherapy-related dataset of the IMvigor210 cohort and the frontline chemotherapy dataset of GSE48276, and the chemo-drug sensitivity was estimated via Genomics of Drug Sensitivity in Cancer (GDSC). There were a total of eleven genes (CERCAM, TPST1, OSBPL10, EMP1, CYTH3, NCRNA00201, PCDH10, GAP43, COLQ, DGKB, and SETBP1) identified in the FARG signature, which divided BC patients from the TCGA cohort into high- and low-risk groups. The Kaplan-Meier curve analysis demonstrated that BC patients in the low-risk group have superior overall survival (OS) than those in the high-risk group (median OS: 27.06 months vs. 104.65 months, p < 0.0001). Moreover, the FARG signature not only showed a good performance in prognosis prediction, but also could distinguish patients with different neoplasm disease stages, notably whether patients presented with muscle invasive phenotype. Compared to clinicopathological features, the FARG signature was found to be the only independent prognostic factor, and subsequently, a FARG-based prognostic nomogram was constructed with better ability of prognosis prediction, indicated by area under ROC curve (AUC) values for 1-, 3-, and 5-year OS of 0.69, 0.71, and 0.79, respectively. Underlying the FARG signature, multiple kinds of metabolism- and immune-related signaling pathways were enriched. Genomic alteration enrichment further identified that FGFR3 alterations, especially c.746C>G (p.Ser249Cys), were more prevalent in the low-risk group. Additionally, FARG score was positively correlated with ESTIMATE and TIDE scores, and the low-risk group had abundant enrichment of plasma B cells, CD8+ T cells, CD4+ naive T cells, and helper follicular T cells, implying that patients in the low-risk group were likely to make significant responses to immunotherapy, which was further supported by the analysis in the IMvigor210 cohort as there was a significantly higher response rate among patients with lower FARG scores. The analysis of the GDSC database finally demonstrated that low-risk samples were more sensitive to methotrexate and tipifarnib, whereas those in the high-risk group had higher sensitivities in cisplatin, docetaxel, and paclitaxel, instead. The novel established FARG signature based on a comprehensive FGFR3 alteration-related transcriptomic profile performed well in prognosis prediction and was also correlated with immunotherapy and chemotherapy treatment responses, which had great potential in future clinical applications.

Xu T ，Xu W ，Zheng Y ，Li X ，Cai H ，Xu Z ，Zou Q ，Yu B ... -  《Frontiers in Immunology》

Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer.

Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients. First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC. In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability. We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC.

Li X ，Fu S ，Huang Y ，Luan T ，Wang H ，Wang J ... -  《BMC CANCER》