Immune-related basement membrane genes predict the immunotherapy and prognosis of ccRCC patients

Abstract

Background Basement membrane (BM) genes are an important factor in the process of clear cell renal cell carcinoma (ccRCC). Thus, identifying BM genes with prognostic values in ccRCC is critical. Methods The samples from TCGA were separated randomly into 2 cohorts, the training cohort, and the validation cohort. Univariate and multivariate Cox regression analyses were applied to identify prognostic BM genes. Then, nomogram was applied to predict prognosis at different clinicopathological stages and risk scores. GO and KEGG analyses were applied to identify the differentially expressed genes. Moreover, CIBERSORT and ESTIMATE scores were calculated between the high- and low-risk cohort. Results A prognostic risk model of four BM genes, including ADAMTS14, COL7A1, HSPG2, and TIMP3, was constructed. There were also significant differences in survival time between the high- and low-risk cohort for the validation cohort and the entire cohort. The risk model was validated as a new independent prognostic factor for ccRCC by univariate and multivariate Cox regression together with clinicopathological characteristics. The model can also analyze the possibility of immune escape and response to immunotherapy in ccRCC patients. In addition, the results of a pan-cancer analysis showed that these four model genes were associated with immune-related genes in a variety of cancers. Conclusion The signature of four BM genes had a significant prognostic value for ccRCC. They may be promising targets for therapy, especially immune therapy.

Detecting the gravitational wave memory effect with TianQin

“…Detecting the memory effect is essential, both from a theoretical aspect and for precise parameter estimation. From a theoretical perspective, the detection of the memory effect can provide a way to test asymptotic symmetries and soft gravitons [36] (referred to as the infrared triangle [37]), as well as providing an alternative approach to testing general relativity [38][39][40][41][42][43]. From a parameter estimation perspective, considering the memory effect leads to more precise parameter estimation results and also provides a means of verifying the accuracy of the waveform [44].…”