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OBJECTIVE: To reach consensus on the diagnostic criteria for Yin deficiency syndrome in hypertension (YDSH) patients by a modified Delphi method. METHODS: Our study was consistent with T/CACM 1032-2017. The methodology of RAND/UCLA appr-opriateness was used to develop consensus guidance statements. A nationwide panel of experienced clinical experts from 19 provinces was constructed. These experts were all prominent in Traditional Chinese Medicine (TCM) of cardiovascular diseases. This con-sensus process consisted of two rounds of ques-tionnaires and a final round of consultation to analyze the weight score of each item. Moreover, the data extraction process is carried out independently by third-party researchers (LIANG Junya, SUN Yang, and DU Xiaona). When there is disagreement in all three rounds, the expert panel group (odd number) are invited to vote, and the one with more votes wins. In the questionnaires, participants were asked to rate the appropriateness of each syndrome item using a nine-point Likert scale. The consensus was defined as a panel median rating 1-3 or 7-9 without disagreement. And then the diagnostic criteria of YDSH were formed according to the weight score in the final round. RESULTS: Twenty-eight experts (84.8%) participated in the first round, and thirty-one (93.9%) finished the second round. After two rounds, the consensus of YDSH was reached on 11 items (25.6%), including symptoms, signs, and pulse condition. Twenty-one experts (63.6%) com-pleted the final round in which they used a grading system for each item. Red tongue with scanty fur had the highest weighting (22.8%), followed by heat in the palms and soles (20.1%). CONCLUSIONS: The consensus-based diagnostic criteria for YDSH, formed by a modified Delphi method, can be widely incorporated in TCM. A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our YDSH criteria.

Background Epithelial ovarian cancer (EOC) is a female malignant tumor. Bioinformatics has been widely utilized to analyze genes related to cancer progression. Targeted therapy for specific biological factors has become more valuable. Materials and methods Gene expression profiles of GSE18520 and GSE27651 were downloaded from Gene Expression Omnibus. We used the “limma” package to screen differentially expressed genes (DEGs) between EOC and normal ovarian tissue samples and then used Clusterprofiler to do functional and pathway enrichment analyses. We utilized Search Tool for the Retrieval of Interacting Genes Database to assess protein–protein interaction (PPI) information and the plug-in Molecular Complex Detection to screen hub modules of PPI network in Cytoscape, and then performed functional analysis on the genes in the hub module. Next, we utilized the Weighted Gene Expression Network Analysis package to establish a co-expression network. Validation of the key genes in databases and Gene Expression Profiling Interactive Analysis (GEPIA) were completed. Finally, we used quantitative real-time PCR to validate hub gene expression in clinical tissue samples. Results We analyzed the DEGs (96 samples of EOC tissue and 16 samples of normal ovarian tissue) for functional analysis, which showed that upregulated DEGs were strikingly enriched in phosphate ion binding and the downregulated DEGs were significantly enriched in glycosaminoglycan binding. In the PPI network, CDK1 was screened as the most relevant protein. In the co-expression network, one EOC-related module was identified. For survival analysis, database and clinical sample validation of genes in the turquoise module, we found that ITLN1 was positively correlated with EOC prognosis and had lower level in EOC than in normal tissues, which was consistent with the results predicted in GEPIA. Conclusion In this study, we exhibited the key genes and pathways involved in EOC and speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis.

Figure S1. Quality control processes for DNA methylation chip data. Quality control measures and exclusion criteria as applied to Harvard, Spain, Norway, Sweden, and The Cancer Genome Atlas (TCGA) sample cohorts. (PDF 642 kb)

Figure S2. Analysis work flow. Adenocarcinoma and squamous cell carcinoma samples from Harvard, Spain, Norway, and Sweden cohorts were used for the discovery phase of analysis. Data from The Cancer Genome Atlas (TCGA) were used for Validation. Ranger is a weighted version of random forest for controlling for the covariates including age, gender, smoking status, and histological stage. Variable importance score (VIS) was estimated for each CpG site and was ranked in descending order. CpG sites ranked in top 10% in both discovery and validation sets were selected for further evaluation by Cox regression. Multiple testing correction by false discovery rate (FDR) method was used if necessary. (PDF 357 kb)

Figure S3. DNA methylation probes identified by flexible criterion in squamous cell carcinoma and their correlation with corresponding gene expression. Three more sites were identified in SCC samples under flexible criterion: cg06646494 in KDM2B, cg00121158 in KDM4C, and cg06615743 in KDM4B. The cg00121158 (A) and cg06615743 (B) showed a statistical correlation with the corresponding gene expression. DNA methylation level was categorized to six quantiles and box plot for gene expression was drawn for each quantile. Pearson correlation was used to estimate the correlation coefficient (r) and the P value, Gene expression was log2 transformed before analysis. (PDF 571 kb)