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ABSTRACT. The aims of this study were to identify the common gene signatures of clear cell renal cell carcinoma (CCRCC), and to expand the respective protein-protein interaction networks associated with CCRCC regulation. For the latter, we utilized multiple gene expression data sets from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), with which we could analyze the aberrant gene expression patterns at the transcriptome level that distinguish cancer from normal samples. We obtained the GSE781 and GSE6344 clear cell renal cell carcinoma gene expression datasets from GEO, which contained a total of 37 cancer and 37 normal samples. Subsequent R language analysis allowed identification of the differentially expressed genes. The genes that exhibited significant up or downregulation in cancers were entered into the Database for Annotation, Visualization, and Integrated Discovery to perform analysis of gene functional annotations, resulting in the generation of two protein-protein interaction networks that included the most significantly up or downregulated genes in CCRCC. These allowed us to identify the key factor genes, which could potentially be utilized Common key signatures associated with renal cell carcinoma to separate cancer versus normal samples. The differentially regulated genes are also highly likely to be functionally important regulatory factors in renal cell carcinoma: cell functions showing enrichment of these genes include amine biosynthetic and vitamin metabolic processes, ion binding, extracellular transport function, and regulation of biosynthesis. Together, the results from our study offer further reason to pursue diagnosis and therapy of CCRCC at the molecular level.

Alzheimer's disease (AD) is a progressive illness, and early diagnosis and treatment can help delay its progression. However, clinics still lack high‐throughput, low‐invasive, precise, and objective diagnostic strategies. Herein, the Au nanoparticles anchored porous perovskite oxide microrods (CTO@Au) with designed superior properties is developed to construct a high‐throughput detection platform. Specifically, a single metabolic fingerprinting is obtained from only 30 nL of serum within seconds, enabling the rapid acquisition of 239 × 8 high‐quality fingerprints in ≈ 2 h. AD is distinguish from health controls and Parkinson's disease with an area under the curve (AUC) of 1.000. Moreover, eight specific metabolites are identified as a biomarker panel, based on which precise diagnosis of AD is achieved, with an AUC of 1.000 in blind test. The possible relevant pathways and potential mechanism involved in these biomarkers are investigated and discussed. This work provides a high‐performance platform for metabolic diagnostic analysis.