NMR Relaxation of Gas Adsorbed in Microporous Material

NMR relaxometry has been widely applied to characterize fluid confined in porous media because of its versatility, chemical selectivity, and noninvasive nature. Here we extend its usage to gas adsorbed in microporous materials by establishing a new quantitative model based on the molecular level NMR relaxation mechanism revealed by the molecular simulation of a prototypical adsorption system, CH4 adsorbed in ZIF-8. The model enables new NMR relaxometry-based characterization methods for thermodynamic, dynamic, and structural properties of adsorption systems, as demonstrated and validated by the experiments where the adsorption capacity and self-diffusivity of H2, CH4, and small alcohols adsorbed in ZIF-8 are deduced from the NMR relaxation data. The findings can serve for a better understanding of the composition–structure–properties relationships of a wide range of adsorption systems which is essential for the development and application of new functional microporous materials

Additional file 2 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 2: Table S2. The 51 m6A-related prognostic lncRNAs

Additional file 7 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 7: Fig. S4. Calibration curves of the risk score based on the nomogram. Calibration curves of the 3- and 5-year overall survival in the (a) training and (b) validation sets (bootstrap method, 1000 repetitions)

Additional file 5 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 5: Fig. S2. Verification of the expression and survival differences of several lncRNAs among the m6A-RLPS based on the GEO data. Differential expression of KCNQ1OT1 in the (a) GSE31189, (b) GSE51493, and (c) GSE31684 cohorts. (d) Kaplan–Meier curves indicating different OS of patients with different expression levels of KCNQ1OT1

Additional file 3 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 3: Fig. S1. Correlations between the selected immune checkpoints and 51 m6A-related lncRNAs. (a-h) Correlation between hub lncRNAs and CTLA-4, GAL9, LAG-3, PD-1, PD-L1, PD-L2, TIGIT, and TIM-3, respectively. *p < 0.05

Additional file 6 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 6: Fig. S3. Analysis of the m6A-RLPS stratified by risk level in the entire cohort. (a) Kaplan–Meier curves for the m6A-RLPS. (b) Distributions of risk scores, survival status, and relative lncRNA expressions. (c) ROC curves for predicting 1-, 3-, and 5-year OS rates

Additional file 8 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 8: Table S4. The relationship between risk score and survival in 33 kinds of tumors

Additional file 1 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 1: Table S1. Demographic and clinicopathological characteristics of patients with bladder cancer (n = 410)

Additional file 4 of An effective N6-methyladenosine-related long non-coding RNA prognostic signature for predicting the prognosis of patients with bladder cancer

Additional file 4: Table S3. Clinicopathological characteristics of patients with BLCA in the training and validation cohorts

Image2_N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma.JPEG

Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.</p