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Heart failure (HF) is an epidemic disease affecting a large population worldwide. Traditional Chinese medicine (TCM) is playing an increasingly important role in the clinical treatment of HF. According to the TCM theory, HF could be classified into Yang deficiency and Qi-yin deficiency; however, there are few objective and biological lines of evidence for differentiation of TCM HF syndromes to date. In this study, data-independent acquisition (DIA) mass spectrometry was applied to comparatively analyze the protein expression in serum samples obtained from 12 Yang deficiency patients, 12 Qi-yin deficiency patients, and 12 healthy volunteers. Compared to the healthy controls, a total of 121 differentially expressed proteins (DEPs) (77 upregulated and 44 downregulated proteins) were identified in Yang deficiency samples, while 59 DEPs (49 upregulated and 10 downregulated proteins) were detected in Qi-yin deficiency samples. Enrichment analyses of these DEPs based on the GO and KEGG databases revealed functional clusters associated with the immune system, signal transduction, and infectious disease. Several previously reported HF biomarker proteins were found to be the hub proteins in a protein-protein interaction network analysis. Three novel hub DEPs were identified as potential biomarkers for differentiation between different TCM syndromes of HF. The results provide biological insight into the differences of different TCM HF syndromes and an opportunity for specific biomarker identification for different TCM HF syndromes. [ABSTRACT FROM AUTHOR]

Background. Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied. Methods. A total of 718 immune-related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integrated bioinformatics analyses was performed to build the final predictive prognosis model. Results. The 178 survival-associated genes (P < 0.05) participated in important immune functions, including immune cell activation and migration. Multivariate regression analysis using 93 genes (P < 0.01), together with survival-associated clinicopathological parameters, identified 35 independent prognostic factors. The most significant 8 independent factors were CD3E, CD40LG, TNFRSF4, CD3G, CD5, ITGA2B, ABCB1, and TNFRSF13b. The final prognostic model achieved outstanding predictive efficiency with the highest AUC of 0.963. Conclusion. Our prognostic model based on the immune signature could effectively predict the prognosis of HNSCC patients, providing novel predictive biomarkers and potential therapeutic targets for HNSCC patients. [ABSTRACT FROM AUTHOR]