Large differences in global transcriptional regulatory programs of normal and tumor colon cells

Host Transcriptional Regulatory Genes and Microbiome Networks Crosstalk through Immune Receptors Establishing Normal and Tumor Multiomics Metafirm of the Oral-Gut-Lung Axis

The microbiome has shown a correlation with the diet and lifestyle of each population in health and disease, the ability to communicate at the cellular level with the host through innate and adaptative immune receptors, and therefore an important role in modulating inflammatory process related to the establishment and progression of cancer. The oral cavity is one of the most important interaction windows between the human body and the environment, allowing the entry of an important number of microorganisms and their passage across the gastrointestinal tract and lungs. In this review, the contribution of the microbiome network to the establishment of systemic diseases like cancer is analyzed through their synergistic interactions and bidirectional crosstalk in the oral-gut-lung axis as well as its communication with the host cells. Moreover, the impact of the characteristic microbiota of each population in the formation of the multiomics molecular metafirm of the oral-gut-lung axis is also analyzed through state-of-the-art sequencing techniques, which allow a global study of the molecular processes involved of the flow of the microbiota environmental signals through cancer-related cells and its relationship with the establishment of the transcription factor network responsible for the control of regulatory processes involved with tumorigenesis.

Nanomedicine as potential cancer therapy via targeting dysregulated transcription factors

Cancer as a disease possess quite complicated pathophysiological implications and is among the prominent causes of morbidity and mortality on global scales. Anti-cancer chemotherapy, surgery, and radiation therapy are some of the present-day conventional treatment options. However, these therapeutic paradigms own several retreats, including lack of specificity, non-targeted toxicological implications, inefficient drug delivery to targeted cells, and emergence of cancer resistance, ultimately causing ineffective cancer management. Owing to the advanced and better biophysical characteristic features and potentiality for the tailoring and customizations and in several fashions, nanotechnology can entirely transubstantiate the cancer identification and its managements. Additionally, nanotechnology also renders several answers to present-day mainstream limitations springing-up in anti-cancer therapeutics. Nanocarriers, owing to their outstanding physicochemical features including but not limited to their particle size, surface morphological features viz. shape etc., have been employed in nanomedicinal platforms for targeting various transcription factors leading to worthy pharmacological outcomes. This transcription targeting activates the wide array of cellular and molecular events like antioxidant enzyme-induction, apoptotic cell death, cell-cycle arrest etc. These outcomes are obtained after the activation or inactivation of several transcription factors and cellular pathways. Further, nanoformulations have been precisely calibrated and functionalized with peculiar targeting groups for improving their efficiency to deliver the drug-payload to specified and targeted cancerous cells and tissues. This review undertakes an extensive, across-the-board and all-inclusive approach consisting of various studies encompassing different types of tailored and customized nanoformulations and nanomaterials designed for targeting the transcription factors implicated in the process of carcinogenesis, tumor-maturation, growth and metastasis. Various transcription factors viz. nuclear factor kappa (NF-κB), signal transducer and activators of transcription (STAT), Cmyc and Twist-related protein 1 (TWIST1) along with several types of nanoparticles targeting these transcription factors have been summarized here. A section has also been dedicated to the different types of nanoparticles targeting the hypoxia inducing factors. Efforts have been made to summarize several other transcription factors implicated in various stages of cancer development, growth, progression and invasion, and their targeting with different kinds of nanomedicinal agents.

Endoplasmic reticulum-resident protein Sec62 drives colorectal cancer metastasis via MAPK/ATF2/UCA1 axis

OBJECTIVE

Metastasis is responsible for the poor prognosis of patients with colorectal cancer (CRC), and the role of aberrant expression of endoplasmic reticulum (ER) receptors in tumour metastasis has not been fully elucidated. The aim of the study is to ensure the role of ER-resident protein Sec62 in CRC metastasis and illuminate associated molecular mechanisms.

MATERIALS AND METHODS

Bioinformatics analysis, qRT-PCR, western blot and immunohistochemistry assays were performed to evaluate the expression level and clinical significance of Sec62 in CRC. The specific role of Sec62 in CRC was identified by a series of functional experiments. We conducted RNA sequencing and rescue experiments to analyse the differentially expressed genes and identified UCA1 as a novel pro-metastasis target of Sec62 in CRC. Besides, the efficacy of MAPK/JNK inhibitor or agonist on Sec62-mediated CRC metastasis was evaluated by trans-well and wound healing assays. Finally, luciferase reporter and ChIP assay were employed to further explore the potential mechanisms.

RESULTS

The abnormally elevated expression of Sec62 predicted poor prognosis of CRC patients and facilitated malignant metastasis of CRC cells. Mechanistically, Sec62 enhanced UCA1 expression through activating MAPK/JNK signalling pathway. And the p-JNK activating ATF2 could transcriptionally regulate UCA1 expression. Furthermore, blocking or activating MAPK/JNK signalling with JNK inhibitor or agonist potently suppressed or enhanced Sec62 mediated CRC metastatic process.

CONCLUSIONS

Our study reports for the first time that the Sec62/MAPK/ATF2 /UCA1 axis exists in CRC metastatic process, which could be a potential treatment target of metastatic CRC.

Identification of four novel hub genes as monitoring biomarkers for colorectal cancer

Background

It must be admitted that the incidence of colorectal cancer (CRC) was on the rise all over the world, but the related treatment had not caught up. Further research on the underlying pathogenesis of CRC was conducive to improving the survival status of current CRC patients.

Methods

Differentially expressed genes (DEGs) screening were conducted based on “limma” and “RobustRankAggreg” package of R software. Weighted gene co-expression network analysis (WGCNA) was performed in the integrated DEGs that from The Cancer Genome Atlas (TCGA), and all samples of validation were from Gene Expression Omnlbus (GEO) dataset.

Results

The terms obtained in the functional annotation for primary DEGs indicated that they were associated with CRC. The MEyellow stand out whereby showed the significant correlation with clinical feature (disease), and 4 hub genes, including ABCC13, AMPD1, SCNN1B and TMIGD1, were identified in yellow module. Nine datasets from Gene Expression Omnibus database confirmed these four genes were significantly down-regulated and the survival estimates for the low-expression group of these genes were lower than for the high-expression group in Kaplan-Meier survival analysis section. MEXPRESS suggested that down-regulation of some top hub genes may be caused by hypermethylation. Receiver operating characteristic curves indicated that these genes had certain diagnostic efficacy. Moreover, tumor-infiltrating immune cells and gene set enrichment analysis for hub genes suggested that there were some associations between these genes and the pathogenesis of CRC.

Conclusion

This study identified modules that were significantly associated with CRC, four novel hub genes, and further analysis of these genes. This may provide a little new insights and directions into the potential pathogenesis of CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00216-7.

Tissue, age, sex, and disease patterns of matrisome expression in GTEx transcriptome data

The extracellular matrix (ECM) has historically been explored through proteomic methods. Whether or not global transcriptomics can yield meaningful information on the human matrisome is unknown. Gene expression data from 17,382 samples across 52 tissues, were obtained from the Genotype-Tissue Expression (GTEx) project. Additional datasets were obtained from The Cancer Genome Atlas (TCGA) program and the Gene Expression Omnibus for comparisons. Gene expression levels generally matched proteome-derived matrisome expression patterns. Further, matrisome gene expression properly clustered tissue types, with some matrisome genes including SERPIN family members having tissue-restricted expression patterns. Deeper analyses revealed 382 gene transcripts varied by age and 315 varied by sex in at least one tissue, with expression correlating with digitally imaged histologic tissue features. A comparison of TCGA tumor, TCGA adjacent normal and GTEx normal tissues demonstrated robustness of the GTEx samples as a generalized matrix control, while also determining a common primary tumor matrisome. Additionally, GTEx tissues served as a useful non-diseased control in a separate study of idiopathic pulmonary fibrosis (IPF) matrix changes, while identifying 22 matrix genes upregulated in IPF. Altogether, these findings indicate that the transcriptome, in general, and GTEx in particular, has value in understanding the state of organ ECM.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer

Though transcriptomics technologies evolve rapidly in the past decades, integrative analysis of mixed data between microarray and RNA-seq remains challenging due to the inherent variability difference between them. Here, Rank-In was proposed to correct the nonbiological effects across the two technologies, enabling freely blended data for consolidated analysis. Rank-In was rigorously validated via the public cell and tissue samples tested by both technologies. On the two reference samples of the SEQC project, Rank-In not only perfectly classified the 44 profiles but also achieved the best accuracy of 0.9 on predicting TaqMan-validated DEGs. More importantly, on 327 Glioblastoma (GBM) profiles and 248, 523 heterogeneous colon cancer profiles respectively, only Rank-In can successfully discriminate every single cancer profile from normal controls, while the others cannot. Further on different sizes of mixed seq-array GBM profiles, Rank-In can robustly reproduce a median range of DEG overlapping from 0.74 to 0.83 among top genes, whereas the others never exceed 0.72. Being the first effective method enabling mixed data of cross-technology analysis, Rank-In welcomes hybrid of array and seq profiles for integrative study on large/small, paired/unpaired and balanced/imbalanced samples, opening possibility to reduce sampling space of clinical cancer patients. Rank-In can be accessed at http://www.badd-cao.net/rank-in/index.html.

METTL14-mediated N6-methyladenosine modification of SOX4 mRNA inhibits tumor metastasis in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the leading causes of tumor-related death worldwide, and its main cause of death is distant metastasis. Methyltransferase-like 14(METTL14), a major RNA N6-adenosine methyltransferase, is involved in tumor progression via regulating RNA function. The goal of the study is to uncover the biological function and molecular mechanism of METTL14 in CRC.

METHODS

Quantitative real-time PCR (qRT-PCR), western blot and immunohistochemical (IHC) assays were employed to detect METTL14 and SOX4 in CRC cell lines and tissues. The biological functions of METTL14 were demonstrated using in vitro and in vivo experiments. Chromatin immunoprecipitation (ChIP), Transcrptomic RNA sequencing (RNA-Seq), m6A-RNA immunoprecipitation sequencing (MeRIP-Seq), RNA immunoprecipitation and luciferase reporter assays were used to explore the mechanism of METTL14 action.

RESULTS

METTL14 expression was significantly downregulated in CRC and decreased METTL14 was associated with poor overall survival (OS). Both the univariate and multivariate Cox regression analysis indicated that METTL14 was an independent prognostic factor in CRC. Moreover, lysine-specific histone demethylase 5C(KDM5C)-mediated demethylation of histone H3 lysine 4 tri-methylation(H3K4me3) in the promoter of METTL14 inhibited METTL14 transcription. Functionally, we verified that METTL14 inhibited CRC cells migration, invasion and metastasis through in vitro and in vivo assays, respectively. Furthermore, we identified SRY-related high-mobility-group box 4(SOX4) as a target of METTL14-mediated m6A modification. Knockdown of METTL14 markedly abolished SOX4 mRNA m6A modification and elevated SOX4 mRNA expression. We also revealed that METTL14-mediated SOX4 mRNA degradation relied on the YTHDF2-dependent pathway. Lastly, we demonstrated that METTL14 might inhibit CRC malignant process partly through SOX4-mediated EMT process and PI3K/Akt signals.

CONCLUSIONS

Decreased METTL14 facilitates tumor metastasis in CRC, suggesting that METTL14 might be a potential prognostic biomarker and effective therapeutic target for CRC.

Diverse LEF/TCF Expression in Human Colorectal Cancer Correlates with Altered Wnt-Regulated Transcriptome in a Meta-Analysis of Patient Biopsies

Aberrantly activated Wnt signaling causes cellular transformation that can lead to human colorectal cancer. Wnt signaling is mediated by Lymphoid Enhancer Factor/T-Cell Factor (LEF/TCF) DNA-binding factors. Here we investigate whether altered LEF/TCF expression is conserved in human colorectal tumor sample and may potentially be correlated with indicators of cancer progression. We carried out a meta-analysis of carefully selected publicly available gene expression data sets with paired tumor biopsy and adjacent matched normal tissues from colorectal cancer patients. Our meta-analysis confirms that among the four human LEF/TCF genes, LEF1 and TCF7 are preferentially expressed in tumor biopsies, while TCF7L2 and TCF7L1 in normal control tissue. We also confirm positive correlation of LEF1 and TCF7 expression with hallmarks of active Wnt signaling (i.e., AXIN2 and LGR5). We are able to correlate differential LEF/TCF gene expression with distinct transcriptomes associated with cell adhesion, extracellular matrix organization, and Wnt receptor feedback regulation. We demonstrate here in human colorectal tumor sample correlation of altered LEF/TCF gene expression with quantitatively and qualitatively different transcriptomes, suggesting LEF/TCF-specific transcriptional regulation of Wnt target genes relevant for cancer progression and survival. This bioinformatics analysis provides a foundation for future more detailed, functional, and molecular analyses aimed at dissecting such functional differences.

Edgetic perturbation signatures represent known and novel cancer biomarkers

Isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RNA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer sub-type specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at EdgeExplorer: “http://webclu.bio.wzw.tum.de/EdgeExplorer”) are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.

Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein

Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions. This work demonstrates that statistically sound combination of gene expression data with prior knowledge about biology in the form of large protein interaction networks can yield quantitatively more reproducible observations from studies characterizing human disease. The novel concept of Well-Associated Proteins (WAPs) introduced herein-gene products significantly associated on protein interaction networks with the differences in transcript levels between control and disease-does not require choosing a differential expression threshold and can be computed efficiently enough to enable false discovery rate estimation via permutation. Reproducibility of WAPs is shown to be on average superior to that of DEGs under easily-quantifiable conditions suggesting that they can yield a significantly more robust description of disease. Enhanced reproducibility of WAPs versus DEGs is first demonstrated with four independent data sets focused on systemic sclerosis. This finding is then validated over thousands of pairs of data sets obtained by random partitions of large studies in several other diseases. Conditions that individual data sets must satisfy to yield robust WAP scores are examined. Reproducible identification of WAPs can potentially benefit drug target selection and precision medicine studies.

Tumor suppressor genes and allele-specific expression: mechanisms and significance

Recent findings indicate that allele-specific expression (ASE) at specific cancer driver gene loci may be of importance in onset/progression of the disease. Of particular interest are loss-of-function (LOF) of tumor suppressor gene (TSGs) alleles. While LOF tumor suppressor mutations are typically considered to be recessive, if these mutant alleles can be significantly differentially expressed relative to wild-type alleles in heterozygotes, the clinical consequences could be significant. LOF TSG alleles are shown to be segregating at high frequencies in world-wide populations of normal/healthy individuals. Matched sets of normal and tumor tissues isolated from 233 cancer patients representing four diverse tumor types demonstrate functionally important changes in patterns of ASE in individuals heterozygous for LOF TSG alleles associated with cancer onset/progression. While a variety of molecular mechanisms were identified as potentially contributing to changes in ASE patterns in cancer, changes in DNA copy number and allele-specific alternative splicing possibly mediated by antisense RNA emerged as predominant factors. In conclusion, LOF TSGs are segregating in human populations at significant frequencies indicating that many otherwise healthy individuals are at elevated risk of developing cancer. Changes in ASE between normal and cancer tissues indicates that LOF TSG alleles may contribute to cancer onset/progression even when heterozygous with wild-type functional alleles.

Transcriptome and Network Dissection of Microsatellite Stable and Highly Instable Colorectal Cancer

Background:

Colorectal cancer (CRC) is one of the most common cancers worldwide with high number of mortality every year. Microsatellite instability (MSI) is a considerable feature of CRC which affects prognosis and treatment. High level of MSI or MSI-high (MSI-H) colorectal cancer has better prognosis and immunotherapy response, while microsatellite stable (MSS) CRC has better response to 5-fluorouracil (5-FU)-based chemotherapy. More studies are needed, specifically on MSS CRC which has worse prognosis, to further reveal biological differences and similarities between MSS and MSI colorectal cancer, which may equip us with the knowledge to develop more promising therapeutic approaches to target both types or be more effective for each type.

Methods:

We aimed to find affected biological processes and their regulators in both type, MSS and MSI-H, of CRC; as well as reveal specific ones in each type. We applied meta- and network analysis on freely available transcriptome data in MSS and MSI-H colorectal cancer from gene expression omnibus (GEO) database to detect common differentially expressed genes (DEGs) and critical biological processes and predict their most significant regulators.

Results:

Our results demonstrate considerable up and downregulation in cell cycle and lipid catabolism processes, respectively; and introduced MYC and FOXM1 as two central and up-stream regulators of DEGs in both type of CRC. Chemokine-mediated processes displayed up-regulation in MSI-H type, while metastasis-related processes showed more activation in MSS CRC. Additionally, DACH1 and TP53 were detected as two important transcription factors that differentially expressed just in MSS and MSI-H, respectively.

Conclusion:

Our results can explain why MSI and MSS CRC display different immunotherapy response, prognosis, and metastasis feature. Moreover, our predicted up-stream regulators in the regulatory networks may be promising therapeutic targets.

Integrated Analysis of Oncogenic Networks in Colorectal Cancer Identifies GUCA2A as a Molecular Marker

Colorectal cancer (CRC) is one of the most common and deadly malignancies in the world. In China, the morbidity rate of CRC has increased during the period 2000 to 2011. Biomarker detection for early CRC diagnosis can effectively reduce the mortality of patients with CRC. To explore the underlying mechanisms of effective biomarkers and identify more of them, we performed weighted correlation network analysis (WGCNA) on a GSE68468 dataset generated from 378 CRC tissue samples. We screened the gene set (module), which was significantly associated with CRC histology, and analyzed the hub genes. The key genes were identified by obtaining six colorectal raw data (i.e., GSE25070, GSE44076, GSE44861, GSE21510, GSE9348, and GSE21815) from the GEO database (https://www.ncbi.nlm.nih.gov/geo). The robust differentially expressed genes (DEGs) in all six datasets were calculated and obtained using the library “RobustRankAggreg” package in R 3.5.1. An integrated analysis of CRC based on the top 50 downregulated DEGs and hub genes in the red module from WGCNA was conducted, and the intersecting genes were screened. The Kaplan–Meier plot was further analyzed, and the genes associated with CRC prognosis based on patients from the TCGA database were determined. Finally, we validated the candidate gene in our clinical CRC specimens. We postulated that the candidate genes screened from the database and verified by our clinical pathological data may contribute to understanding the molecular mechanisms of tumorigenesis and may serve as potential biomarkers for CRC diagnosis and treatment.

Wnt-11 as a Potential Prognostic Biomarker and Therapeutic Target in Colorectal Cancer

The expression of the secreted factor Wnt-11 is elevated in several types of cancer, including colorectal cancer, where it promotes cancer cell migration and invasion. Analysis of colorectal cancer gene expression databases associated WNT11 mRNA expression with increased likelihood of metastasis in a subset of patients. WNT11 expression was correlated with the expression of the Wnt receptors FZD6, RYK, and PTK7, and the combined expression of WNT11, FZD6 and RYK or PTK7 was associated with an increased risk of 5-year mortality rates. Immunohistochemical analysis of Wnt-11 in a cohort of 357 colorectal cancer patients found significantly higher Wnt-11 levels in tumors, compared with benign tissue. Elevated Wnt-11 levels occurred more frequently in rectal tumors than in colonic tumors and in tumors from women than men. In univariate analysis, increased Wnt-11 expression was also associated with tumor invasion and increased 5-year mortality. High Wnt-11 levels were not associated with high levels of nuclear β-catenin, suggesting Wnt-11 is not simply an indicator for activation of β-catenin-dependent signaling. Expression of Wnt-11 in colorectal cancer cell lines expressing low endogenous Wnt-11 inhibited β-catenin/Tcf activity and increased ATF2-dependent transcriptional activity. WNT11 gene silencing and antibody-mediated inhibition of Wnt-11 in colorectal cancer cell lines expressing high Wnt-11 reduced their capacity for invasion. Together, these observations suggest that Wnt-11 could be a potential target for the treatment of patients with invasive colorectal cancer.

Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis

Background

Colorectal cancer (CRC) is one of the most common malignant tumors. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma, and adenocarcinoma samples was downloaded to identify critical genes and potential drugs in CRC.

Methods

Expression profiles, GSE33113 and GSE44076, were integrated using bioinformatics methods. Differentially expressed genes (DEGs) were analyzed by R language. Functional enrichment analyses of the DEGs were performed using the Database for Annotation, visualization, and integrated discovery (DAVID) database. Then, the search tool for the retrieval of interacting genes (STRING) database and Cytoscape were used to construct a protein–protein interaction (PPI) network and identify hub genes. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis (GEPIA). Connectivity Map (CMap) was used to query potential drugs for CRC.

Results

A total of 428 upregulated genes and 751 downregulated genes in CRC were identified. The functional changes of these DEGs were mainly associated with cell cycle, oocyte meiosis, DNA replication, p53 signaling pathway, and progesterone‐mediated oocyte maturation. A PPI network was identified by STRING with 482 nodes and 2,368 edges. Survival analysis revealed that high mRNA expression of AURKA, CCNB1, CCNF, and EXO1 was significantly associated with longer overall survival. Moreover, CMap predicted a panel of small molecules as possible adjuvant drugs to treat CRC.

Conclusion

Our study found key dysregulated genes involved in CRC and potential drugs to combat it, which may provide novel insights and potential biomarkers for prognosis, as well as providing new CRC treatments.

Expression of the human antimicrobial peptide β-defensin-1 is repressed by the EGFR-ERK-MYC axis in colonic epithelial cells

The human β-defensin-1 (HBD1) is an antimicrobial peptide constitutively expressed by epithelial cells at mucosal surfaces. In addition to its microbicidal properties, the loss of HBD1 expression in several cancers suggests that it may also have an anti-tumor activity. Here, we investigated the link between HBD1 expression and cancer signaling pathways in the human colon cancer cell lines TC7 and HT-29, and in normal human colonic primary cells, using a mini-gut organoid model. Using available datasets from patient cohorts, we found that HBD1 transcription is decreased in colorectal cancer. We demonstrated that inhibiting the Epidermal Growth Factor Receptor (EGFR) increased HBD1 expression, whereas activating EGFR repressed HBD1 expression, through the MEKK1/2-ERK1/2 pathway that ultimately regulates MYC. We finally present evidences supporting a role of MYC, together with the MIZ1 coregulator, in HBD1 regulation. Our work uncovers the role and deciphers the function of the EGFR-ERK-MYC axis as a repressor of HBD1 expression and contributes to the understanding of HBD1 suppression observed in colorectal cancer.

Colon-specific eQTL analysis to inform on functional SNPs

Background

Genome-wide association studies on colorectal cancer have identified more than 60 susceptibility loci, but for most of them there is no clear knowledge of functionality or the underlying gene responsible for the risk modification. Expression quantitative trail loci (eQTL) may provide functional information for such single nucleotide polymorphisms (SNPs).

Methods

We have performed detailed eQTL analysis specific for colon tissue on a series of 97 colon tumours, their paired adjacent normal mucosa and 47 colon mucosa samples donated by healthy individuals. R package MatrixEQTL was used to search for genome-wide cis-eQTL and trans-eQTL fitting linear models adjusted for age, gender and tissue type to rank transformed expression data.

Results

The cis-eQTL analyses has revealed 29,073 SNP-gene associations with permutation-adjusted P-values < 0.01. These correspond to 363 unique genes. The trans-eQTL analysis identified 10,665 significant SNP-gene associations, most of them in the same chromosome, further than 1 Mb of the gene. We provide a web tool to search for specific SNPs or genes. The tool calculates Pearson or Spearman correlation, and allows to select tissue type for analysis. Data and plots can be exported.

Conclusions

This resource should be useful to prioritise SNPs for further functional studies and to identify relevant genes behind identified loci.

LncRNA DLEU1 contributes to colorectal cancer progression via activation of KPNA3

Background

Accumulating evidences show that long noncoding RNAs (lncRNA) play essential roles in the development and progression of various malignancies. However, their functions remains poorly understood and many lncRNAs have not been defined in colorectal cancer (CRC). In this study, we investigated the role of DLEU1 in CRC.

Methods

Quantitative real-time PCR was used to detect the expression of DLEU1 and survival analysis was adopted to explore the association between DLEU1 expression and the prognosis of CRC patients. CRC cells were stably transfected with lentivirus approach and cell proliferation, migration, invasion and cell apoptosis, as well as tumorigenesis in nude mice were performed to assess the effects of DLEU1 in BCa. Biotin-coupled probe pull down assay, RNA immunoprecipitation and Fluorescence in situ hybridization assays were conducted to confirm the relationship between DLEU1 and SMARCA1.

Results

Here we revealed that DLEU1 was crucial for activation of KPNA3 by recruiting SMARCA1, an essential subunit of the NURF chromatin remodeling complex, in CRC. DLEU1 was indispensible for the deposition of SMARCA1 at the promoter of KPNA3 gene. Increased expression of DLEU1 and KPNA3 was observed in human CRC tissues. And higher expression of DLEU1 or KPNA3 in patients indicates lower survival rate and poorer prognosis. DLEU1 knockdown remarkably inhibited CRC cell proliferation, migration and invasion in vitro and in vivo while overexpressing KPNA3 in the meantime reversed it.

Conclusions

Our results identify DLEU1 as a key regulator by a novel DLEU1/SMARCA1/KPNA3 axis in CRC development and progression, which may provide a potential biomarker and therapeutic target for the management of CRC.

Electronic supplementary material

The online version of this article (10.1186/s12943-018-0873-2) contains supplementary material, which is available to authorized users.

Integrated analysis of long non-coding RNAs in human colorectal cancer

Accumulating evidence highlights the role of long non-coding RNAs (lncRNAs) in tumors. However, the genome-wide expression and roles of lncRNAs in colorectal cancer (CRC) remain unknown. Here, we systematically examined the global gene expressions in primary and synchronous liver metastases CRC tissue, in which thousands of aberrantly expressed lncRNAs were characterized. Co-expression analysis revealed that some lncRNAs correlated to their neighboring mRNAs in expression levels, whereas others formed networks with protein-coding genes in trans. We observed H3K4me3 was enriched at expressed lncRNA transcription start sites (TSSs) and correlated to dysregulated lncRNAs. Furthermore, we identified primary and metastasis tumor linked lncRNA signatures positively correlated with poor-prognosis gene set. Finally, functional experiments demonstrated two candidate lncRNAs were required for proliferation and migration of CRC cells. In summary, we provided a new framework for lncRNA associated clinical prognosis evaluation and target selection of gene therapy in CRC.

Classification of gene signatures for their information value and functional redundancy

Gene signatures are more and more used to interpret results of omics data analyses but suffer from compositional (large overlap) and functional (correlated read-outs) redundancy. Moreover, many gene signatures rarely come out as significant in statistical tests. Based on pan-cancer data analysis, we construct a restricted set of 962 signatures defined as informative and demonstrate that they have a higher probability to appear enriched in comparative cancer studies. We show that the majority of informative signatures conserve their weights for the genes composing the signature (eigengenes) from one cancer type to another. We finally construct InfoSigMap, an interactive online map of these signatures and their cross-correlations. This map highlights the structure of compositional and functional redundancies between informative signatures, and it charts the territories of biological functions. InfoSigMap can be used to visualize the results of omics data analyses and suggests a rearrangement of existing gene sets.

An informative collection of gene signatures for transcriptomic data analysis is constructed. The number of transcriptomic signatures grows fast and their collections are highly redundant that hampers omics data analyses interpretation. A computational biology team from Institut Curie led by Andrei Zinovyev selected a collection of 962 gene signatures shown to be informative for cancer studies and reflecting mechanisms of cancer progression. The signatures were filtered from a large compendium without requiring any manual curation by experts through a large-scale unbiased analysis of pancancer data. They have much higher chance to obtain significant enrichment scores in a comparative trancriptomic study. The authors integrated the 962 signatures into InfoSigMap, a new data visualization resource for the interpretation of the results of omics data analyses, which facilitates getting an insight into the mechanisms driving cancer.

Mutanome and expression of immune response genes in microsatellite stable colon cancer

The aim of this study was to analyze the impact of the mutanome in the prognosis of microsatellite stable stage II CRC tumors. The exome of 42 stage II, microsatellite stable, colon tumors (21 of them relapse) and their paired mucosa were sequenced and analyzed. Although some pathways accumulated more mutations in patients exhibiting good or poor prognosis, no single somatic mutation was associated with prognosis. Exome sequencing data is also valuable to infer tumor neoantigens able to elicit a host immune response. Hence, putative neoantigens were identified by combining information about missense mutations in each tumor and HLAs genotypes of the patients. Under the hypothesis that neoantigens should be correctly presented in order to activate the immune response, expression levels of genes involved in the antigen presentation machinery were also assessed. In addition, CD8A level (as a marker of T-cell infiltration) was measured. We found that tumors with better prognosis showed a tendency to generate a higher number of immunogenic epitopes, and up-regulated genes involved in the antigen processing machinery. Moreover, tumors with higher T-cell infiltration also showed better prognosis. Stratifying by consensus molecular subtype, CMS4 tumors showed the highest association of expression levels of genes involved in the antigen presentation machinery with prognosis. Thus, we hypothesize that a subset of stage II microsatellite stable CRC tumors are able to generate an immune response in the host via MHC class I antigen presentation, directly related with a better prognosis.

VGLL4 targets a TCF4-TEAD4 complex to coregulate Wnt and Hippo signalling in colorectal cancer

Concerted co-regulation of multiple signalling pathways is crucial for tissue homoeostasis and tumorigenesis. Here we report that VGLL4, a previously identified YAP antagonist, also functions as a regulator of Wnt/β-catenin signalling. The expression of VGLL4 is significantly downregulated in clinical colorectal carcinoma (CRC) specimens, positively associated with patient survival rate, and inversely correlated with the expression of Wnt target genes in CRCs. Knockdown of VGLL4 enhances proliferation and tumour formation of CRC cells. A designed peptide mimicking the function of VGLL4 effectively inhibits CRC progression in a de novo mouse model. Mechanistically, TEAD4 associates with TCF4 to form a complex and cobind target genes. VGLL4 targets this TEAD4–TCF4 complex to interfere the functional interplay between TEAD4 and TCF4, suppressing the transactivation of TCF4. Collectively, our study indicates that Wnt/β-catenin and Hippo-YAP signalling are directly linked at transcription factor-level, and VGLL4 can target a TEAD4–TCF4 complex to co-regulate both pathways.

The Wnt/β-catenin and YAP signaling pathways have fundamental roles in cancer. Here, the authors show that VGLL4, a known YAP antagonist, also negatively regulates Wnt/β-catenin signaling by targeting TEAD-DNA-TCF4 complex, thereby inhibiting colorectal cancer growth.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer.

Detection of gene communities in multi-networks reveals cancer drivers

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Altered pathways and colorectal cancer prognosis

The identification of molecular markers with prognostic value in colorectal cancer is a challenging task that is needed to define therapeutic guidelines. Clinical factors are insufficient to identify those patients with stage II at risk of relapse or those patients with stage III at low risk. There is a current effort to define a consensus in molecular subtypes based on expression profiles, which are characterized by a distinctive prognostic outcome. Also several gene expression signatures based on individual genes have been proposed to predict prognosis, but they show low consistency and reproducibility. Slattery et al. describe a pathway-based approach to analyze gene expression differences between normal and colon cancer tissues. The most interesting finding is that having more deregulated pathways is associated with good prognosis. If these findings are properly validated, new insights into the mechanisms of colon carcinogenesis may be revealed. Please see related article: http://dx.doi.org/10.1186/s12916-015-0292-9 .