Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer.

Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients.

Zhu K ，Xiaoqiang L ，Deng W ，Wang G ，Fu B ... -  《Lipids in Health and Disease》

Identification of a novel signature based on unfolded protein response-related gene for predicting prognosis in bladder cancer.

The unfolded protein response (UPR) served as a vital role in the progression of tumors, but the molecule mechanisms of UPR in bladder cancer (BLCA) have been not fully investigated. We identified differentially expressed unfolded protein response-related genes (UPRRGs) between BLCA samples and normal bladder samples in the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis and the least absolute shrinkage and selection operator penalized Cox regression analysis were used to construct a prognostic signature in the TCGA set. We implemented the validation of the prognostic signature in GSE13507 from the Gene Expression Omnibus database. The ESTIMATE, CIBERSORT, and ssGSEA algorithms were used to explore the correlation between the prognostic signature and immune cells infiltration as well as key immune checkpoints (PD-1, PD-L1, CTLA-4, and HAVCR2). GDSC database analyses were conducted to investigate the chemotherapy sensitivity among different groups. GSEA analysis was used to explore the potential mechanisms of UPR-based signature. A prognostic signature comprising of seven genes (CALR, CRYAB, DNAJB4, KDELR3, CREB3L3, HSPB6, and FBXO6) was constructed to predict the outcome of BLCA. Based on the UPRRGs signature, the patients with BLCA could be classified into low-risk groups and high-risk groups. Patients with BLCA in the low-risk groups showed the more favorable outcomes than those in the high-risk groups, which was verified in GSE13507 set. This signature could serve as an autocephalous prognostic factor in BLCA. A nomogram based on risk score and clinical characteristics was established to predict the over survival of BLCA patients. Furthermore, the signature was closely related to immune checkpoints (PD-L1, CTLA-4, and HAVCR2) and immune cells infiltration including CD8+ T cells, follicular helper T cells, activated dendritic cells, and M2 macrophages. GSEA analysis indicated that immune and carcinogenic pathways were enriched in high-risk group. We identified a novel unfolded protein response-related gene signature which could predict the over survival, immune microenvironment, and chemotherapy response of patients with bladder cancer.

Zhu K ，Xiaoqiang L ，Deng W ，Wang G ，Fu B ... -  《-》

Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer.

Bladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis. We downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P < 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne's. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs. In this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy. Our study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

Xiong X ，Chen C ，Li X ，Yang J ，Zhang W ，Wang X ，Zhang H ，Peng M ，Li L ，Luo P ... -  《Frontiers in Oncology》

TFRC, associated with hypoxia and immune, is a prognostic factor and potential therapeutic target for bladder cancer.

Bladder cancer is a common malignancy of the urinary system, and the survival rate and recurrence rate of patients with muscular aggressive (MIBC) bladder cancer are not ideal. Hypoxia is a pathological process in which cells acquire special characteristics to adapt to anoxic environment, which can directly affect the proliferation, invasion and immune response of bladder cancer cells. Understanding the exact effects of hypoxia and immune-related genes in BLCA is helpful for early assessment of the prognosis of BLCA. However, the prognostic model of BLCA based on hypoxia and immune-related genes has not been reported. Hypoxia and immune cell have important role in the prognosis of bladder cancer (BLCA). The aim of this study was to investigate whether hypoxia and immune related genes could be a novel tools to predict the overall survival and immunotherapy of BLCA patients. First, we downloaded transcriptomic data and clinical information of BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A combined hypoxia and immune signature was then constructed on the basis of the training cohort via least absolute shrinkage and selection operator (LASSO) analysis and validated in test cohort. Afterwards, Kaplan-Meier curves, univariate and multivariate Cox and subgroup analysis were employed to assess the accuracy of our signature. Immune cell infiltration, checkpoint and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were used to investigate the immune environment and immunotherapy of BLCA patients. Furthermore, we confirmed the role of TFRC in bladder cancer cell lines T24 and UMUC-3 through cell experiments. A combined hypoxia and immune signature containing 8 genes were successfully established. High-risk group in both training and test cohorts had significantly poorer OS than low-risk group. Univariate and multivariate Cox analysis indicated our signature could be regarded as an independent prognostic factor. Different checkpoint was differently expressed between two groups, including CTLA4, HAVCR2, LAG3, PD-L1 and PDCD1. TIDE analysis indicated high-risk patients had poor response to immunotherapy and easier to have immune escape. The drug sensitivity analysis showed that high-risk group patients were more potentially sensitive to many drugs. Meanwhile, TFRC could inhibit the proliferation and invasion ability of T24 and UMUC-3 cells. A combined hypoxia and immune-related gene could be a novel predictive model for OS and immunotherapy estimation of BLCA patients and TFRC could be used as a potential therapeutic target in the future.

Tang R ，Wang H ，Liu J ，Song L ，Hou H ，Liu M ，Wang J ，Wang J ... -  《-》

Identifying a Novel Defined Pyroptosis-Associated Long Noncoding RNA Signature Contributes to Predicting Prognosis and Tumor Microenvironment of Bladder Cancer.

Bladder cancer (BLCA) is a common malignant tumor of the urinary tract, which is the sixth most common cancer among men. Numerous studies suggested that pyroptosis and long noncoding RNAs (lncRNAs) played an essential role in the development of cancers. However, the role of pyroptosis-related lncRNAs in BLCA and their prognostic value are still unclear. In this study, we constructed a signature model through least absolute shrinkage and selection operator (LASSO) Cox regression analysis and Cox univariate analysis based on The Cancer Genome Atlas (TCGA) database. The expression of 12 pyroptosis-related lncRNAs was also confirmed by qRT-PCR in BLCA cell lines. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT R script were applied to quantify the relative proportions of infiltrating immune cells. Correlation coefficients were computed by Spearman analyses. The Kaplan-Meier method, Cox regression model, and log-rank tests were used to evaluate the prognostic value. The R package of pRRophetic was used to predict IC50 of common chemotherapeutic agents. A total of 12 pyroptosis-related lncRNAs with great prognosis value were identified. The expression was investigated by qRT-PCR in four BLCA cell lines. Then, 126 cases were identified as high-risk group, and 277 cases were identified as low-risk group based on the cutoff point. Patients in the low-risk group showed a significant survival advantage. Furthermore, we found that clinical features were significantly related to the risk score. As well, based on the C-index values, a nomogram was constructed. The gene set enrichment analysis (GSEA) results showed that mitogen-activated protein kinase (MAPK) signaling, transforming growth factor (TGF)-β signaling, and WNT signaling were with important significance in the high-risk group. Moreover, we found that riskscore was positively correlated with M0 macrophages and M2 macrophages. In conclusion, our study indicated that pyroptosis is closely connected to BLCA. The riskscore generated from the expression of 12 pyroptosis-related lncRNAs was evaluated by various clinical features including survival status, tumor microenvironment, clinicopathological characteristic, and chemotherapy. It may offer a significant basis for future studies.

Lu H ，Wu J ，Liang L ，Wang X ，Cai H ... -  《Frontiers in Immunology》