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Single-cell transcriptome highlights a multilayer regulatory network on an invasive trajectory within colorectal cancer progression.

ABSTRACT: Colorectal cancer (CRC) is one of the most common and fatal gastrointestinal malignancies, in which cancer stem cells (CSCs) were identified to enable tumor heterogeneity and initiate tumor formation. However, the process from CSCs to invasion cells is unconfirmed. Several bioinformatics methods, including clustering, pseudotime analysis, gene set variation analysis and gene ontology enrichment, were used to construct a path of gradual transformation of CSCs to invasive cells, called "stem-to-invasion path". A large amount of signaling interactions were collated to build the multilayer regulatory network. Kaplan-Meier curve and time-dependent ROC method were applied to reveal prognostic values. We validated the heterogeneity of cells in the tumor microenvironment and revealed the presence of malignant epithelial cells with high invasive potential within primary colonic carcinomas. Next, the "stem-to-invasion path" was identified through constructing a branching trajectory with cancer cells arranged in order. A multilayer regulatory network considered as the vital factor involved in acquiring invasion characteristics underlying the path was built to elucidate the interactions between tumor cell and tumor-associated microenvironment. Then we further identified a novel combinatorial biomarker that can be used to assess the prognosis for CRC patients, and validated its predictive robustness on the independent dataset. Our work provides new insights into the acquisition of invasive potential in primary tumor cells, as well as potential therapeutic targets for CRC invasiveness, which may be useful for the cancer research and clinical treatment.

SUBMITTER: Zhou T

PROVIDER: S-EPMC9075720 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn diphyodont mammals, the additional molars (permanent molars) bud off from the posterior-free end of the primary dental lamina compared with successional teeth (replacement teeth) budding off from the secondary dental lamina. The diphyodont miniature pig has proved to be a valuable model for studying human molar morphogenesis. The additional molars show a sequential initiation pattern related to the specific tooth development stage of additional molars in miniature pigs during the morphogenesis of additional molars. However, the molecular mechanisms of the regulatory network of mRNAs and long non-coding RNAs during sequential formation of additional molars remain poorly characterized in diphyodont mammals. Here, we performed RNA-seq and microarray on miniature pigs at three key molar developmental stages to examine their differential gene expression profiles and potential regulatory networks during additional molar morphogenesis.ResultsWe have profiled the differential transcript expression and functional networks during morphogenesis of additional molars in miniature pigs. We also have identified the coding and long non-coding transcripts using Coding-Non-Coding Index (CNCI) and annotated transcripts through mapping to the porcine, Wuzhishan miniature pig, mice, cow and human genomes. Many new unannotated genes plus 450 putative long intergenic non-coding RNAs (lincRNAs) were identified. Detailed regulatory network analyses reveal that WNT and TGF-β pathways are critical in regulating sequential morphogenesis of additional molars.ConclusionsThis is the first study to comprehensively analyze the spatiotemporal dynamics of coding and long non-coding transcripts during morphogenesis of additional molars in diphyodont mammals. The miniature pig serves as a large model animal to elucidate the relationship between morphogenesis and transcript level during the cascade initiation of additional molars. Our data provide fundamental knowledge and a basis for understanding the molecular mechanisms governing cascade initiation of additional molars, but also provide an important resource for developmental biology research.

Project description:BackgroundCircular RNAs (circRNAs), a novel class of non-coding RNAs, have been found to act as microRNA (miRNA) sponges and thus play key roles in biological processes and pathogenesis. However, studies regarding circRNAs in colorectal cancer (CRC) remain inadequate.ResultsBy differential expression analysis, 10 candidate circRNAs (6 upregulated and 4 downregulated circRNAs) were chosen. 9 of 10 circRNAs were available on CSCD and their structure showed the binding potential of miRNA. Intersection analysis revealed that miR-145-5p, miR-3127-5p, miR-761, miR-4766-3p, miR-135a-5p, miR-135b-5p, miR-374a-3p and miR-330-3p were 8 miRNAs with the most potential in binding circRNAs. Further expression validation and correlation analysis demonstrated hsa_circ_0001955/miR-145-5p and hsa_circ_0000977/miR-135b-5p axes as key pathways in CRC. Subsequently, target gene prediction, differential expression analysis, intersection analysis and correlation analysis showed that CDK6, MMP12 and RAB3IP were the three potential downstream targets of hsa_circ_0001955/miR-145-5p axis and FOXO1, MBNL1, MEF2C, RECK, PPM1E, TTLL7 and PCP4L1 were the seven potential downstream targets of hsa_circ_0000977/miR-135b-5p axis in CRC. Finally, we also confirmed that expression of hsa_circ_0001955 or hsa_circ_0000977 was significantly positively correlated with their individual targets in CRC.ConclusionsIn the present work, we constructed a potential hsa_circ_0001955/hsa_circ_0000977-mediated circRNA-miRNA-mRNA regulatory network in CRC by a series of in silico analysis and experimental validation.MethodsWhole-transcriptome microarrays from CRC and matched normal samples were obtained from GEO. The structure of circRNA was identified by CSCD. starBase and miRNet were successively used to predict miRNA of circRNA and target gene of miRNA. Expression correlation between RNA-RNA interactions was assessed using GEO and TCGA data. Finally, a potential circRNA-miRNA-mRNA network was established based on competing endogenous RNA (ceRNA) hypothesis.

Project description:Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 104 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data (Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

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Single-cell transcriptome highlights a multilayer regulatory network on an invasive trajectory within colorectal cancer progression.

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