Background: Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.Methods: Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways.Results: PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3-and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer. Conclusions:In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

The progression of clear cell renal cell carcinoma (ccRCC) remains a major challenge in clinical practice, and elucidation of the molecular drivers of malignancy progression is critical for the development of effective therapeutic targets. Recent studies have demonstrated that N 6 -methyladenosine (m 6 A) is the most abundant modification of eukaryotic mRNA and plays a key role in tumorigenesis and progression. However, the biological roles and underlying mechanisms of m 6 A-mediated autophagy in cancers especially in ccRCC remain poorly elucidated. m 6 A dot blot assay, m 6 A RNA methylation assay kit and immunofluorescence analysis were used to profile m 6 A levels in tissue samples and their correlation with autophagic flux. Expression patterns and clinical significance of fat mass and obesity-associated protein (FTO) were determined through bioinformatics analysis, real-time PCR, western blotting, immunohistochemistry. RNA-seq, MeRIP-seq, MeRIP-qRT-PCR, RIP-qRT-PCR, transmission electron microscopy, immunofluorescence analysis and luciferase reporter assay were used to investigate the underlying mechanism of the FTO-autophagy axis. The role of FTO and autophagy in ccRCC progression was evaluated both in vitro and in vivo . Here we found that m 6 A modification was suppressed and closely related to autophagic flux in ccRCC. Elevated FTO was inhibited by rapamycin, whereas silencing FTO enhanced autophagic flux and impaired ccRCC growth and metastasis. SIK2 was identified as a functional target of m 6 A-mediated autophagy, thereby prompting FTO to play a conserved and important role in inhibiting autophagy and promoting tumorigenesis through an m 6 A-IGF2BP2 dependent mechanism. Moreover, the small molecule inhibitor FB23-2 targeting FTO inhibited tumor growth and prolonged survival in the patient-derived xenograft (PDX) model mice, suggesting that FTO is a potential effective therapeutic target for ccRCC. Our findings uncovered the crucial role of FTO/autophagy/SIK2 axis in modulating the progression of ccRCC, suggesting that FTO may serve as a valuable prognostic biomarker and promising therapeutic target in ccRCC.

Objectives. To reveal the molecular mechanisms of ulcerative colitis (UC) and provide potential biomarkers for UC gene therapy. Methods. We downloaded the GSE87473 microarray dataset from the Gene Expression Omnibus (GEO) and identified the differentially expressed genes (DEGs) between UC samples and normal samples. Then, a module partition analysis was performed based on a weighted gene coexpression network analysis (WGCNA), followed by pathway and functional enrichment analyses. Furthermore, we investigated the hub genes. At last, data validation was performed to ensure the reliability of the hub genes. Results. Between the UC group and normal group, 988 DEGs were investigated. The DEGs were clustered into 5 modules using WGCNA. These DEGs were mainly enriched in functions such as the immune response, the inflammatory response, and chemotaxis, and they were mainly enriched in KEGG pathways such as the cytokine-cytokine receptor interaction, chemokine signaling pathway, and complement and coagulation cascades. The hub genes, including dual oxidase maturation factor 2 (DUOXA2), serum amyloid A (SAA) 1 and SAA2, TNFAIP3-interacting protein 3 (TNIP3), C-X-C motif chemokine (CXCL1), solute carrier family 6 member 14 (SLC6A14), and complement decay-accelerating factor (CD antigen CD55), were revealed as potential tissue biomarkers for UC diagnosis or treatment. Conclusions. This study provides supportive evidence that DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14, and CD55 might be used as potential biomarkers for tissue biopsy of UC, especially SLC6A14 and DUOXA2, which may be new targets for UC gene therapy. Moreover, the DUOX2/DUOXA2 and CXCL1/CXCR2 pathways might play an important role in the progression of UC through the chemokine signaling pathway and inflammatory response.

S100 calcium‐binding protein A (S100A) family members regulate multiple biological functions related to pancreatic cancer (PC) progression and metastasis. However, the prognostic and oncologic values of S100A family have not been systematically investigated in PC. In the present study, the mRNA expression and potential functions of S100A family were investigated by bioinformatic analysis. Our results demonstrated that overexpression of S100A2, S100A6, S100A10, S100A11, S100A14 and S100A16 was significantly associated with higher T stage, advanced histologic grade and worse prognosis in PC. Besides, one CpG of S100A2, three CpG of S100A6, four CpG of S100A10, four CpG of S100A11, two CpG of S100A14 and five CpG of S100A16 were negatively associated with corresponding S100A family members expression and positively associated with overall survival (OS). The signature based on four CpGs showed good prediction ability of OS. Besides, S100A2 overexpression took part in the regulation of mitotic cell cycle, ECM‐receptor interaction and HIF‐1α transcription factor network. Overexpression of S100A6, S100A10, S100A11, S100A14 and S100A16 may impair the infiltration and cytolytic activity of CD8+ T cells through focal adhesion‐Ras‐stimulating signalling pathway in PC. Overall, this study explores the multiple prognostic values and oncologic functions of the S100A family in PC.

BackgroundPhysical performance is reported to have various beneficial effects on human health, especially in older individuals. Although such effects are associated with body mass index (BMI), the relationship between BMI and physical performance has not been clarified.DesignWe conducted a cross-sectional study of 966 suburb-dwelling Tianjin individuals aged ≥ 60 years (average age 67.5±6.02, men 435, women 531). Mobility, balance, and muscle strength were assessed by walking speed, timed up-and-go test (TUGT), and grip strength, respectively. The subjects were categorized into three groups based on BMI (kg/m2) as follows: normal weight, 18.5 ≤ BMI ≤ 23.9; overweight, 24.0 ≤ BMI ≤ 27.9; and obese, BMI ≥ 28.0.ResultAfter adjusting for all other variables, relative grip strength decreased when BMI increased in both men and women (P for trend <0.001 and <0.001, respectively). BMI may be negatively associated with TUGT performance in the women only. There was no apparent association between walking speed and BMI in either sex, but after adjusting for age, walking speed was faster when BMI increased in women (P for trend= 0.0162).ConclusionThis study suggests that in older individuals, higher BMI is associated with poor muscle strength in both sexes.

Background: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China, but the pharmacological mechanisms remain unclear. This research was designed to clarify the underlying pharmacological mechanism of GGQLD against UC. Method: In this research, a GGQLD-compound-target-UC network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein-protein interaction network was constructed to screen and evaluate hub genes and key active ingredients. Molecular docking was used to verify the activities of binding between hub targets and ingredients. Results: Finally, 83 potential therapeutic targets and 118 corresponding active ingredients were obtained by network pharmacology. Quercetin, kaempferol, wogonin, baicalein, and naringenin were identified as potential candidate ingredients. GO and KEGG enrichment analyses revealed that GGQLD had anti-inflammatory, antioxidative, and immunomodulatory effects. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines (e.g., IL-6, TNF, IL-1β, CXCL8, CCL2) in the immune system and inflammation-related pathways, such as the IL-17 pathway and the Th17 cell differentiation pathway. In addition, molecular docking results demonstrated that the main active ingredient, quercetin, exhibited good affinity to hub targets. Conclusion: This research fully reflects the multicomponent and multitarget characteristics of GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC.

Immune checkpoint inhibitors (ICIs) are currently a first-line treatment option for clear cell renal cell carcinoma (ccRCC). However, recent clinical studies have shown that a large number of patients do not respond to ICIs. Moreover, only a few patients achieve a stable and durable response even with combination therapy based on ICIs. Available studies have concluded that the response to immunotherapy and targeted therapy in patients with ccRCC is affected by the tumor immune microenvironment (TIME), which can be manipulated by targeted therapy and tumor genomic characteristics. Therefore, an in-depth understanding of the dynamic nature of the TIME is important for improving the efficacy of immunotherapy or combination therapy in patients with advanced ccRCC. Here, we explore the possible mechanisms by which the TIME affects the efficacy of immunotherapy and targeted therapy, as well as the factors that drive dynamic changes in the TIME in ccRCC, including the immunomodulatory effect of targeted therapy and genomic changes. We also describe the progress on novel therapeutic modalities for advanced ccRCC based on the TIME. Overall, this review provides valuable information on the optimization of combination therapy and development of individualized therapy for advanced ccRCC.

Acute rejection (AR) after kidney transplant is one of the major obstacles to obtain ideal graft survival. Reliable molecular biomarkers for AR and renal allograft loss are lacking. This study was performed to identify novel long noncoding RNAs (lncRNAs) for diagnosing AR and predicting the risk of graft loss. The several microarray datasets with AR and nonrejection specimens of renal allograft downloaded from Gene Expression Omnibus database were analyzed to screen differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Univariate and multivariate Cox regression analyses were used to identify optimal prognosis-related DElncRNAs for constructing a risk score model. 39 common DElncRNAs and 185 common DEmRNAs were identified to construct a lncRNA-mRNA regulatory relationship network. DElncRNAs were revealed to regulate immune cell activation and proliferation. Then, 4 optimal DElncRNAs, ATP1A1-AS1, CTD-3080P12.3, EMX2OS, and LINC00645, were selected from 17 prognostic DElncRNAs to establish the 4-lncRNA risk score model. In the training set, the high-risk patients were more inclined to graft loss than the low-risk patients. Time-dependent receiver operating characteristics analysis revealed the model had good sensitivity and specificity in prediction of 1-, 2-, and 3-year graft survival after biopsy ( AUC = 0.891 , 0.836 , and   0.733 , respectively). The internal testing set verified the result well. Gene set enrichment analysis which expounded NOD-like receptor, the Toll-like receptor signaling pathways, and other else playing important role in immune response was enriched by the 4 lncRNAs. Allograft-infiltrating immune cells analysis elucidated the expression of 4 lncRNAs correlated with gamma delta T cells and eosinophils, etc. Our study identified 4 novel lncRNAs as potential biomarkers for AR of renal allograft and constructed a lncRNA-based model for predicting the risk of graft loss, which would provide new insights into mechanisms of AR.