Global heat wave records from 1971 to 2100

A long-term global heat wave record dataset was constructed using three historical hourly climate data of CRU-JRA, ERA5 and GLDAS and future daily climate data of GFDL-ESM4 under three SSPS (Shared Socioeconomic Pathways). These datasets are based on constant thresholds over 35 °C, percentile thresholds over 90%, and duration thresholds over 3 days. The Python file is the source code for the Global Heat Wave Toolbox, which can be used to generate heat wave records based on custom thresholds

Additional file 1 of Development and validation of a prognostic nomogram for predicting overall survival in patients with primary bladder sarcoma: a SEER-based retrospective study

Additional file 1: Figure S1. Nomogram is used to evaluate a 70-year-old patient with T2N0M0 and a tumor size of 5 cm. Based on the total score, the survival probability 1-year, 3-year, and 5-year of the patient is 57.5%, 42%, and 34.5%, respectively

Table_1_Reduced Preoperative Glomerular Filtration Rate Is Associated With Adverse Postoperative Oncological Prognosis in Patients Undergoing Radical Nephroureterectomy for Upper Tract Urothelial Carcinoma: A Retrospective Cohort Study.docx

ObjectiveTo evaluate the association between perioperative estimated glomerular filtration rate (eGFR) and postoperative oncological outcomes in patients with upper tract urothelial carcinoma (UTUC) treated with radical nephroureterectomy (RNU),and to evaluate the effect of sex on this association.MethodsThe medical records of patients with UTUC who underwent RNU between January 2012 and December 2017 at our hospital were retrospectively reviewed. Patients were divided into three groups based on preoperative eGFRs: normal eGFR (>60 mL/min/1.73 m2; n = 179), moderately reduced eGFR (45–60 mL/min/1.73 m2; n = 45), and severely reduced eGFR (≤ 45 mL/min/1.73 m2; n = 36). Statistical analyses were performed to evaluate the prognostic impact of preoperative eGFR on prognosis.ResultsPatient mean age was 66.7 ± 9.6 years, and 47.9% were female. Multivariate regression analysis based on Cox proportional risk models and Kaplan-Meier survival rates showed that lower preoperative eGFR was associated with decreased OS, PFS, and CSS. In the adjusted Cox regression model, patients with normal and moderately reduced eGFRs had a decreased hazard for mortality, with adjusted hazard ratios of 0.13 [95% confidence interval (CI): 0.07–0.26] and 0.36 (95% CI: 0.18–0.73), respectively (P ConclusionPreoperative renal insufficiency is strongly associated with a higher risk of cancer progression and a lower survival probability. It is important to identify preoperative renal insufficiency in patients with UTUC, particularly female patients.</p

Additional file 10 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

Additional file 10. The raw data from TCGA asnew raw data and data related to biochemical recurrence of prostate cancerpatients. There are three sheets in this file, the firstsheet “TCGA raw data” was the data directly obtained from TCGA dataset. In thesecond sheet “BCR state and time” we included data types we used for study.After excluding patients with missing “A8_New_Event_Time” data,we obtained 464 patients in sheet “data of464 patients”. BCR: Biochemical recurrence

Additional file 1 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

Additional file 1. Clinical information of patients in thetraining cohort (TCGA-PRAD)

Additional file 7 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

Additional file 7. Metabolism pathwaysand their corresponding core enrichment genes significantly enriched in normalsamples (A) and tumor samples (B). KEGG: Kyoto Encyclopedia ofGenes and Genomes

Additional file 5 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

Additional file 5: Objectives, methodsand package namesof different Rpackages used in different steps in our analysis

Image_1_Identification of Biomarkers for Controlling Cancer Stem Cell Characteristics in Bladder Cancer by Network Analysis of Transcriptome Data Stemness Indices.JPEG

Background: Stem cells characterized by self-renewal and therapeutic resistance play crucial roles in bladder cancer (BLCA). However, the genes modulating the maintenance and proliferation of BLCA stem cells are still unclear. In this study, we aimed to characterize the expression of stem cell-related genes in BLCA.Methods: The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Corrected mRNAsi were further analyzed with regard to muscle-invasive bladder cancer molecular subtypes, survival analysis, pathological staging characteristics, and outcomes after primary treatment. Next, weighted gene co-expression network analysis was used to find modules of interest and key genes. Functional enrichment analysis was performed to functionally annotate the modules and key genes. The expression levels of key genes in all cancers were validated using Oncomine and Gene Expression Omnibus (GEO) database containing molecular subtypes in BLCA. Protein interaction networks were used to identify upstream genes, and the relationships between genes were analyzed at the protein and transcription levels.Findings: mRNAsi was significantly upregulated in cancer tissues. Corrected mRNAsi in BLCA increased as tumor stage increased, with T3 having the highest stem cell characteristics. Lower corrected mRNAsi scores had better overall survival and treatment outcome. The modules of interest and key genes were determined based on topological overlap measurement clustering results and the inclusion criteria. For 13 key genes (AURKA, BUB1B, CDCA5, CDCA8, KIF11, KIF18B, KIF2C, KIFC1, KPNA2, NCAPG, NEK2, NUSAP1, and RACGAP1), enriched gene ontology terms related to cell proliferation (e.g., mitotic nuclear division, spindle, and microtubule binding) were determined. Their expression did not differ according to the pathological stages of BLCA, and these genes were clearly overexpressed in many types of cancers. In GEO database, the expression levels of 13 key genes were higher in basal subtype with the highest stem cell characteristics than in luminal and its subtypes. AURKB and PLK1 may be regulated upstream of other key genes, and the key genes were found to be strongly correlated with each other and with upstream genes.Interpretation: The 13 key genes identified in this study were found to play important roles in the maintenance of BLCA stem cells. Controlling the upstream genes AURKB and PLK1 may have applications in the treatment of BLCA. These genes may act as therapeutic targets for inhibiting the stemness characteristics of BLCA.</p

Additional file 6 of Predicting biochemical-recurrence-free survival using a three-metabolic-gene risk score model in prostate cancer patients

Additional file 6. GSEA identifying KEGG pathways enriched in normal prostate tissues (A–K) and prostate cancer tissues (L, M). Gene Set Enrichment Analysis: Gene Set Enrichment Analysis; KEGG: Kyoto Encyclopedia of Genes andGenomes

Image_1_Bladder Cancer Exhibiting High Immune Infiltration Shows the Lowest Response Rate to Immune Checkpoint Inhibitors.JPEG

Background: Bladder urothelial cancer (BLCA) treatment using immune checkpoint inhibitors (IMCIs) can result in long-lasting clinical benefits. However, only a fraction of patients respond to such treatment. In this study, we aimed to identify the relationships between immune cell infiltration levels (ICILs) and IMCIs and identify markers for ICILs.Methods: ICILs were estimated based on single-sample gene set enrichment analysis. The response rates of different ICILs to IMCIs were calculated by combining the ICILs of molecular subtypes in BLCA with the response rates of different molecular subtypes of IMvigor 210 trials to a programmed cell death ligand-1 inhibitor. Weighted gene co-expression network analysis was used to identify modules of interest with ICILs. Functional enrichment analysis was performed to functionally annotate the modules. Screening of key genes and unsupervised clustering were used to identify candidate biomarkers. Tumor IMmune Estimation Resource was used to validate the relationships between the biomarkers and ICILs. Finally, we verified the expression of key genes in molecular subtypes of different response rates for IMCIs.Findings: The basal squamous subtype and luminal infiltrated subtype, which showed low response rates for IMCIs, had the highest levels of immune infiltration. The neuronal subtypes, which showed the highest response rates to IMCIs, had low ICILs. The modules of interest and key genes were determined based on topological overlap measurement, clustering results, and inclusion criteria. Modules highly correlated with ICILs were mainly enriched in immune responses and epithelial–mesenchymal transition. After screening the key genes in the modules, five candidate biomarkers (CD48, SEPT1, ACAP1, PPP1R16B, and IL16) were selected by unsupervised clustering. The key genes were inversely associated with tumor purity and were mostly expressed in the basal squamous subtype and luminal infiltrated subtypes.Interpretation: Patients with high ICILs may benefit the least from treatment with IMCIs. Five key genes could predict ICILs in BLCA, and their high expression suggested that the response rate to IMCIs may decrease.</p