Fig. 2

Separate WGCNA networks were constructed for the tumor microbiome data and genomic data. A Network topology analysis of various soft threshold powers in OV, HNSC, ESCA, STAD, GBM, BLCA, BRCA, CESC and COAD in microbiome (left two columns) and genome (right two columns). The first column panel shows the scale-free fit index (y-axis) as a function of the soft-thresholding power(x-axis) in the microbiome data. The second column panel displays the mean connectivity (degree, y-axis) as a function of the soft-thresholding power (x-axis) in the microbiome data. The third column panel shows the scale-free fit index (y-axis) as a function of the soft-thresholding power (x-axis) in the genomic data. The forth column panel displays the mean connectivity (degree, y-axis) as a function of the soft-thresholding power (x-axis) in the genomic data. The selection of the soft threshold β is shown in Additional file 5: Table S1. B Correlation analysis between the microbial (y-axis) and mRNA (x-axis) modules in OV, HNSC, ESCA, STAD and GBM. The circle size represents the correlation between the microbiome module and the genome module. A p value < 0.05 was considered statistically significant. Red, blue, and yellow circles represent p values less than 0.001, 0.01, and 0.05, respectively. WGCNA weighted gene coexpression network analysis, OV ovarian cancer, HNSC head and neck cancer, ESCA esophageal cancer, STAD stomach cancer, GBM glioblastoma, BLCA bladder cancer, BRCA breast cancer, CESC cervical cancer, COAD colon cancer, mRNA messenger ribonucleic acid