Coronary artery disease (CAD) causes more than 700,000 deaths each year in China. Previous genome-wide association studies (GWAS) in populations of European ancestry identified several genetic loci for CAD, but no such study has yet been reported in the Chinese population. Here we report a three-stage GWAS in the Chinese Han population. We identified a new association between rs6903956 in a putative gene denoted as C6orf105 on chromosome 6p24.1 and CAD (P = 5.00 × 10⁻³, stage 2 validation; P = 3.00 × 10⁻³, P = 1.19 × 10⁻⁸ and P = 4.00 × 10⁻³ in three independent stage 3 replication populations; P = 4.87 × 10⁻¹², odds ratio = 1.51 in the combined population). The minor risk allele A of rs6903956 is associated with decreased C6orf105 mRNA expression. We report the first GWAS for CAD in the Chinese Han population and identify a SNP, rs6903956, in C6orf105 associated with susceptibility to CAD in this population.

Breast cancer is one of the most common malignancies among females, and its prognosis is affected by a complex network of gene interactions. In this study, we constructed free-scale gene co-expression networks using weighted gene co-expression network analysis (WGCNA). The gene expression profiles of GSE25055 were downloaded from the Gene Expression Omnibus (GEO) database to identify potential biomarkers associated with breast cancer progression. GSE42568 was downloaded for validation. A total of 9 modules were established via the average linkage hierarchical clustering. We identified 3 hub genes (ASPM, CDC20, and TTK) in the significant module ( R 2 = 0.52), which were significantly correlated with poor prognosis both in test and validation datasets. In the datasets GSE25055 and GSE42568, higher expression levels of ASPM, CDC20, and TTK correlated with advanced tumor grades. Immunohistochemistry data from the Human Protein Atlas also demonstrated that their protein levels were higher in tumor samples. According to gene set enrichment analysis, 4 commonly enriched pathways were identified: cell cycle pathway, DNA replication pathway, homologous recombination pathway, and P53 signaling pathway. In addition, strong correlations were found among their expression levels. In conclusion, our WGCNA analysis identified candidate prognostic biomarkers for further basic and clinical researches.

As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R 2 = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors.In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms. K E Y W O R D S breast cancer prognosis, CCL8, HERC5, RSAD2, weighted gene correlation network analysis How to cite this article: Tang J, Yang Q, Cui Q, et al. Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer.

Growing evidence highlighted the tumor mutational burden (TMB) as an important feature of carcinogenesis and therapeutic efficacy in esophageal cancer (EC). Our study aimed to explore the genomic landscape and the correlation between TMB and immune cell infiltration in EC patients with or without radiotherapy. The EC patients were categorized into high TMB (TMB-H) and low TMB (TMB-L) groups by the ESTIMATE algorithm, and subgroup analysis was performed based on receiving radiotherapy or not. Univariate regression analysis indicated TMB and TNM stages as high-risk prognostic factors (Hazard ratio > 1 and P < 0.05). Multivariate regression analysis suggested TMB as an independent prognostic factor (Hazard ratio = 1.051, P = 0.003). Kaplan-Meier analysis showed no significant difference of the overall survival (OS) between TMB-H and TMB-L groups (P = 0.082). However, EC patients without radiotherapy in the TMB-H group had significantly decreased OS (P = 0.038) and increased Tregs cell infiltration (P = 0.033). These results suggested TMB as a prognostic marker for EC patients. Especially for patients who did not receive radiotherapy, the prognosis of TMB-H patients was significantly poorer than that of TMB-L patients, which might result from the different regulatory T cell infiltration.

Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer death among females. Multiple molecular alterations are observed in breast cancer. LncRNA transcripts were proved to play important roles in the biology of tumorigenesis. In this study, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival. We developed a 10‐lncRNA signature‐based risk score which was used to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. Receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that this 10‐lncRNA signature was an independent risk factor when adjusting for several clinical signatures such as age, tumour size and lymph node status. The prognostic value of risk scores was validated in the validation set. In addition, a nomogram was established and the calibration plots analysis indicated the good performance and clinical utility of the nomogram. In conclusion, our results demonstrated that this 10‐lncRNA signature effectively grouped patients at low and high risk of disease recurrence.