GNRH1 and LTB4R might be novel immune-related prognostic biomarkers in clear cell renal cell carcinoma (ccRCC).

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) occupied most of renal cell carcinoma (RCC), which associated with poor prognosis. The purpose of this study is to screen novel and prognostic biomarkers for patients with ccRCC. METHODS AND RESULTS: Firstly, Gene Expression Omnibus database was used to collect microarray data for weighted gene co-expression network construction. Gene modules related to prognosis which interest us most were picked out. 90 hub genes were further chosen in the key modules, two of which including gonadotropin releasing hormone 1 (GNRH1) and leukotriene B4 receptor (LTB4R) were screened and validated as immune-related prognostic biomarkers. Based on several public databases and ccRCC tissues collected by ourselves, we performed survival analysis, spearman correlation analysis, receiver operating characteristic (ROC) analysis, quantitative real-time PCR (qRT-PCR), western blotting, immunofluorescence (IF) and immunohistochemistry (IHC) staining for the validation of immune-related prognostic biomarkers. We further explored the relationship between immune-related prognostic biomarker expressions and immunocytes. Finally, gene set enrichment analysis (GSEA) demonstrated that the two immune-related prognostic biomarkers were significantly correlated with cell cycle. CONCLUSIONS: Generally speaking, the present study has identified two novel prognostic biomarkers for patients with ccRCC, which showed strong correlation with prognosis of patients with ccRCC, could further be used as potential prognostic biomarkers in ccRCC.

Neural stem cell transplantation decreases neuroinflammation in a transgenic mouse model of Alzheimer's disease.

Inflammatory processes are considered to play an important role in the progression of neurodegenerative changes in Alzheimer's disease (AD). A number of studies have reported that inflammatory processes are highly correlated with cognitive deficits in AD-like mice. Transplantation of neural stem cells (NSCs) has been considered as a potential new therapy for the treatment of AD because of its effects in improving cognitive ability. However, NSCs have not been evaluated for their protective effects against inflammatory changes in AD. Here, we injected NSCs into amyloid precursor protein (APP)/PS1 transgenic mice to analyse cognitive function and to measure glial fibrillary acidic protein (GFAP), ionized calcium-binding adaptor molecule-1 (Iba-1) and toll-like receptors 4(TLR4) activation. We also quantified TLR-4 pathway-related agents, Aß concentration and the levels of proinflammatory mediators. Our results showed that in NSC-injected APP/PS1 mice, activation of GFAP, Iba-1, TLR4 and TLR4 pathway-related agents (MyD88, TRIF, P38 MAPK and NF-κB P65) were significantly decreased with decreased expression of proinflammatory mediators (IL-1, IL-6, TNF-α and PGE2). These changes were associated with the amelioration of cognitive deficits, but no difference was found in Aß concentration. Our results provide novel evidence that NSC transplantation in APP/PS1 mice significantly improved cognitive deficits and was accompanied by the attenuation of inflammatory injury via suppression of glial and TLR4-mediated inflammatory pathway activation. Our data indicate that these pathways may potentially be important therapeutic targets to prevent or delay AD. This study investigated the neuroprotective effect of neural stem cell (NSC) transplantation against Alzheimer's disease (AD) inflammation. We found that NSC treatment in APP/PS1 mice significantly improved cognitive deficits and was accompanied by the attenuation of inflammatory injury via suppression of glial and toll-like receptor 4 (TLR4) activation and its downstream signalling pathways. Our findings indicate that these pathways may be potentially important therapeutic targets to prevent or delay AD.

Novel T-cell signature based on cell pair algorithm predicts survival and immunotherapy response for patients with bladder urothelial carcinoma.

Background: T-cell-T-cell interactions play important roles in the regulation of T-cells' cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. There is a lack of comprehensive studies of T-cell types in bladder urothelial carcinoma (BLCA) and T-cell-related signatures for predicting prognosis and monitoring immunotherapy efficacy. Methods: More than 3,400 BLCA patients were collected and used in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 19 T-cell types. A cell pair algorithm was applied to construct a T-cell-related prognostic index (TCRPI). Survival analysis was performed to measure the survival difference across TCRPI-risk groups. Spearman's correlation analysis was used for relevance assessment. The Wilcox test was used to measure the expression level difference. Results: Nineteen T-cell types were collected; 171 T-cell pairs (TCPs) were established, of which 26 were picked out by the least absolute shrinkage and selection operator (LASSO) analysis. Based on these TCPs, the TCRPI was constructed and validated to play crucial roles in survival stratification and the dynamic monitoring of immunotherapy effects. We also explored several candidate drugs targeting TCRPI. A composite TCRPI and clinical prognostic index (CTCPI) was then constructed, which achieved a more accurate estimation of BLCA's survival and was therefore a better choice for prognosis prediction in BLCA. Conclusions: All in all, we constructed and validated TCRPI based on cell pair algorithms in this study, which might put forward some new insights to increase the survival estimation and clinical response to immune therapy for individual BLCA patients and contribute to the personalized precision immunotherapy strategy of BLCA.

Construction and Validation of an Autophagy-Related Prognostic Signature and a Nomogram for Bladder Cancer.

OBJECTIVE: Bladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients' risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature. METHODS: We screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets. RESULTS: Three prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage). CONCLUSIONS: Three ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients.

Identification of transforming growth factor beta induced (TGFBI) as an immune-related prognostic factor in clear cell renal cell carcinoma (ccRCC).

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among kidney cancer, which has poor prognosis. The aim of this study was to screen out novel prognostic biomarkers and therapeutic targets for immunotherapy, and some novel molecule drugs for ccRCC treatment. Immune scores ranged from -1109.36 to 2920.81 and stromal scores ranged from -1530.11 to 1955.39 were firstly calculated by applying ESTIMATE algorithm. Then 17 DEGs associated with immune score and stromal score were further identified. 6 candidate hub genes were screened out by performing overall survival (OS) and disease-free survival analyses based on TCGA-KIRC data, one of which including TGFBI was further regarded as hub gene associated with prognosis by calculating the R2 (R2 = 0.011, P = 0.018) and AUC (AUC = 0.874). The prognostic value of TGFBI was validated by performing OS, CSS, and PFS analyses based on GSE29609 and E-MTAB-3267. CMap analysis suggested that 3 molecule drugs might be novel choice for ccRCC treatment. Further analysis demonstrated that CNVs of TGFBI was associated with OS of patients with ccRCC. TGFBI expression was also correlated with histologic grade, pathologic stage, and immune infiltration level, significantly. TGFBI was the most relevant gene with OS among the candidate hub genes, which might be novel DNA methylation biomarkers for ccRCC. In conclusion, our findings indicated that TGFBI was correlated with prognosis of patients with ccRCC, which might be novel prognostic biomarkers, and targets for immunotherapy in ccRCC. Three small molecule drugs were also identified, which showed strong potential for ccRCC treatment.

Complement C7 (C7), a Potential Tumor Suppressor, Is an Immune-Related Prognostic Biomarker in Prostate Cancer (PC).

Objectives: Prostate cancer (PC) is the second most frequent tumor in men, which has a high recurrence rate and poor prognosis. Therefore, this study aimed to identify novel prognostic biomarkers and therapeutic targets for immunotherapy and small molecule drugs for PC treatment. Materials and Methods: The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate immune scores and stromal scores of TCGA-PRAD data. Differentially expressed genes (DEGs) were identified using R package "limma." GO, KEGG, and DO analyses were performed to analyze DEGs. Overall survival and disease-free survival analyses were conducted for hub gene identification. To validate the hub gene at the mRNA and protein expression levels, genetic alterations were measured, and CCLE and Cox regression analyses were performed. Connectivity map (CMap) analysis and GSEA were performed for drug exploration and function analysis, respectively. Results: Immune scores ranged from -1795.98 to 2339.39, and stomal scores ranged from -1877.60 to 1659.96. In total, 45 tumor microenvironment (TME)-related DEGs were identified, of which Complement C7 (C7) was selected and validated as a hub gene. CMap analysis identified six small molecule drugs as potential agents for PC treatment. Further analysis demonstrated that C7 expression was significantly correlated with clinical T, pathological N, and immune infiltration level. Conclusions: In conclusion, of the 45 TME-related DEGs, C7 was shown to correlate with PC prognosis in patients, indicating it as a novel prognostic biomarker and immunotherapy target in PC. Additionally, six small molecule drugs showed strong therapeutic potential for PC treatment.