Min Yang1, Huandong Yang2, Lei Ji3, Xuan Hu1, Geng Tian3, Bing Wang4, Jialiang Yang5. 1. School of Electrical & Information Engineering, Anhui University of Technology, Anhui, 243002, China; Geneis Beijing Co., Ltd., Beijing, 100102, China. 2. Department of Gastrointestinal Surgery, Yidu Central Hospital of Weifang, Weifang, 261000, China. 3. Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China. 4. School of Electrical & Information Engineering, Anhui University of Technology, Anhui, 243002, China. Electronic address: wangbing@ustc.edu. 5. Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China. Electronic address: yangjl@geneis.cn.
Abstract
BACKGROUND: Colorectal cancer (CRC), the 3rd most universal cancer globally, accounts for approximately 10% of newly diagnosed cancer incidences each year. Identifying biomarkers associated with CRC survival and predicting the survival of CRC patients are critical for personalized therapy. Existing studies on CRC survival are mainly based on single omics, studies using multi-omics to predict CRC survival are still vacant. To fill in this gap, we aim to identify biomarkers associated with CRC survival at mRNA, miRNA and tissue microbiome levels, and to evaluate the accuracy of potential biomarkers in predicting CRC survival. METHODS: First, we collected 31 short-term survival (ST, less than 3 years) and 47 long-term survival (LT, longer than 3 years) CRC samples from the database, was named The Cancer Genome Atlas (TCGA). Then, we carried out bioinformatics analysis with collected multi-omics data: (1) comparing the bacterial community structures between ST and LT, (2) identifying differentially expressed mRNAs and miRNAs between ST and LT, and (3) exploring the relationship between bacteria and genes. Finally, we trained models based on multi-omics data to evaluate the performance of several omics data in predicting CRC survival. RESULTS: Among the compared omics data, microbiome of CRC tissue had the best predictive power on the three-year survival of CRC patients, the area under the receiver operating characteristic curve (AUC) is 0.755 with 10-fold Cross-Validation (CV). In addition, we screened out 26 differential microbial communities and 13 differential expression genes (DEGs) between ST and LT. Thermoanaerobacterium, Parabacteroides, Oceanicaulis, and Acetonema were more abundant in the ST, while Methylotenera, Candidatus_Riesia and Aquamicrobium were enriched in the LT. We also found that up-regulated genes were significantly enriched in ST group, but the down-regulated genes were enriched in the LT group. CONCLUSION: The tissue bacterial communities of CRC patients with different survival periods show significant differences, and the bacteria in tumour tissue of CRC are potential biomarkers for predicting the three-year survival of CRC patients.
BACKGROUND: Colorectal cancer (CRC), the 3rd most universal cancer globally, accounts for approximately 10% of newly diagnosed cancer incidences each year. Identifying biomarkers associated with CRC survival and predicting the survival of CRC patients are critical for personalized therapy. Existing studies on CRC survival are mainly based on single omics, studies using multi-omics to predict CRC survival are still vacant. To fill in this gap, we aim to identify biomarkers associated with CRC survival at mRNA, miRNA and tissue microbiome levels, and to evaluate the accuracy of potential biomarkers in predicting CRC survival. METHODS: First, we collected 31 short-term survival (ST, less than 3 years) and 47 long-term survival (LT, longer than 3 years) CRC samples from the database, was named The Cancer Genome Atlas (TCGA). Then, we carried out bioinformatics analysis with collected multi-omics data: (1) comparing the bacterial community structures between ST and LT, (2) identifying differentially expressed mRNAs and miRNAs between ST and LT, and (3) exploring the relationship between bacteria and genes. Finally, we trained models based on multi-omics data to evaluate the performance of several omics data in predicting CRC survival. RESULTS: Among the compared omics data, microbiome of CRC tissue had the best predictive power on the three-year survival of CRC patients, the area under the receiver operating characteristic curve (AUC) is 0.755 with 10-fold Cross-Validation (CV). In addition, we screened out 26 differential microbial communities and 13 differential expression genes (DEGs) between ST and LT. Thermoanaerobacterium, Parabacteroides, Oceanicaulis, and Acetonema were more abundant in the ST, while Methylotenera, Candidatus_Riesia and Aquamicrobium were enriched in the LT. We also found that up-regulated genes were significantly enriched in ST group, but the down-regulated genes were enriched in the LT group. CONCLUSION: The tissue bacterial communities of CRC patients with different survival periods show significant differences, and the bacteria in tumour tissue of CRC are potential biomarkers for predicting the three-year survival of CRC patients.