Gene-Microbiome Co-expression Networks in Colon Cancer

Abstract

It is known that cancer onset and development arise from complex, multi-factorial phenomena spanning from the molecular, functional, micro-environmental, and cellular up to the tissular and organismal levels. Important advances have been made in the systematic analysis of the molecular (mostly genomic and transcriptomic) within large studies of high throughput data such as The Cancer Genome Atlas collaboration. However, the role of the microbiome in the induction of biological changes needed to reach these pathological states remains to be explored, largely because of scarce experimental data. In recent work a non-standard bioinformatics strategy was used to indirectly quantify microbial abundance from TCGA RNA-seq data, allowing the evaluation of the microbiome in well-characterized cancer patients, thus opening the way to studies incorporating the molecular and microbiome dimensions altogether. In this work, we used such recently described approaches for the quantification of microbial species alongside with gene expression. With this, we will reconstruct bipartite networks linking microbial abundance and gene expression in the context of colon cancer, by resorting to network reconstruction based on measures from information theory. The rationale is that microbial communities may induce biological changes important for the cancerous state. We analyzed changes in microbiome-gene interactions in the context of early (stages I and II) and late (stages III and IV) colon cancer, studied changes in network descriptors, and identify key discriminating features for early and late stage colon cancer. We found that early stage bipartite network is associated with the establishment of structural features in the tumor cells, whereas late stage is related to more advance signaling and metabolic features. This functional divergence thus arise as a consequence of changes in the organization of the corresponding gene-microorganism co-expression networks.

The Intratumor Microbiota Signatures Associate With Subtype, Tumor Stage, and Survival Status of Esophageal Carcinoma

“…Studies on the bacterial or viral composition of human tumors using sequencing data from databases such as the TCGA have recently emerged ( 14 , 20 ). Investigation of the intratumor microbiota will provide valuable information for better understanding the occurrence and progression of tumors.…”

“…Previously, we explored the microbiota signature in four major types of gastrointestinal cancer, and the results demonstrated that the microbial profile is highly site-specific and notably differed between upper and lower gastrointestinal tumors ( 12 ). Several other studies have also investigated the intratumor microbiota derived from TCGA sequences of different cancer types ( 13 , 14 ). In the study of a TCGA breast cancer cohort, the results indicated an increased Proteobacteria presence in tumor tissues, while the composition of Actinobacteria was elevated in the adjacent normal tissues ( 15 ).…”

Host Genetics and Microbiota Interactions in Colorectal Cancer: Shared or Independent Risk?

“…Moreover, a study analyzed publicly available data to determine if the composition of microbiota affected host gene expression in early-stage CRC and advanced-stage CRC [59]. The bacterial taxa with more connections with the expression of host genes in early-stage CRC were Ilumatobacter, Rhodospirillum, and Nitrosospira, while in advanced-stage CRC they were Desulfurella, Nitriliruptor, and Jeotgalicoccus [59]. Indeed, these taxa are not the usual taxa associated with CRC.…”

“…Thus, this kind of novel approach is useful to unveil hidden biological mechanisms. In addition, the connected genes with these bacteria belonged to biological functions related to proliferation, biogenesis, and cell cycles in early-stage CRC, and related to migration and angiogenesis in advanced-stage CRC [59].…”

“…Host variants associated with CRC could shape microbiota composition [55] E. coli pks+ could cause somatic mutations in host cells [56] Transcription Host gene expression shape microbiota composition in CRC [58] Fusobacteria disrupts host gene expression [30] Microbiota composition shapes host gene expression in CRC stages [59] Methylation Hungatella hathewayi and Fusobacterium nucleatum disrupt the methylation of tumor suppressor genes [60] Parvimonas abundance is higher in CRC patients with altered methylation [57] Metabolites Correlation between certain metabolites and abundance of microbiota [28] Bacterial toxins and host genetic risk are not linked [63] Multilevel Host variation shapes some microbial taxa and metabolites but each layer explains part of the risk [62] As we have reviewed, there are biological mechanisms that are involved in both genetic CRC risk and microbiota composition, but not all the role of microbiota in CRC is influenced by the genetics of the host. It must pointed out that the composition of the microbiota is influenced by diet [64] or lifestyle [65].…”