A Novel Prognostic Biomarker Panel for Early-Stage Colon Carcinoma

Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors

BACKGROUND

Although differentiation therapy can cure some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs).

METHODS

A transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (~5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC lines in vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs).

RESULTS

The drug shifts the network predictably, induces CDX2 and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards all CDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (~1700 CRCs), revealing the impact of CDX2-reinstatement therapy could translate into a ~50% reduction in the risk of mortality/recurrence.

CONCLUSIONS

Findings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine.

ZEB1 hypermethylation is associated with better prognosis in patients with colon cancer

BACKGROUND

Colon cancer (CC) is a heterogeneous disease that is categorized into four Consensus Molecular Subtypes (CMS) according to gene expression. Patients with loco-regional CC (stages II/III) lack prognostic factors, making it essential to analyze new molecular markers that can delineate more aggressive tumors. Aberrant methylation of genes that are essential in crucial mechanisms such as epithelial mesenchymal transition (EMT) contributes to tumor progression in CC. We evaluate the presence of hyper- and hypomethylation in subrogate IHC markers used for CMS classification (CDX2, FRMD6, HTR2B, ZEB1) of 144 stage II/III patients and CC cell lines by pyrosequencing. ZEB1 expression was also studied in control and shRNA-silenced CC cell lines and in paired normal tissue/tumors by quantitative PCR. The pattern of ZEB1 staining was also analyzed in methylated/unmethylated tumors by immunohistochemistry.

RESULTS

We describe for the first time the hypermethylation of ZEB1 gene and the hypomethylation of the FRMD6 gene in 32.6% and 50.9% of tumors, respectively. Additionally, we confirm the ZEB1 re-expression by epigenetic drugs in methylated cell lines. ZEB1 hypermethylation was more frequent in CMS1 patients and, more importantly, was a good prognostic factor related to disease-free survival (p = 0.015) and overall survival (p = 0.006) in our patient series, independently of other significant clinical parameters such as patient age, stage, lymph node involvement, and blood vessel and perineural invasion.

CONCLUSIONS

Aberrant methylation is present in the subrogate genes used for CMS classification. Our results are the first evidence that ZEB1 is hypermethylated in CC and that this alteration is an independent factor of good prognosis.

Orchestration of MUC2 - The key regulatory target of gut barrier and homeostasis: A review

The gut mucosa of human is covered by mucus, functioning as a crucial defense line for the intestine against external stimuli and pathogens. Mucin2 (MUC2) is a subtype of secretory mucins generated by goblet cells and is the major macromolecular component of mucus. Currently, there is an increasing interest on the investigations of MUC2, noting that its function is far beyond a maintainer of the mucus barrier. Moreover, numerous gut diseases are associated with dysregulated MUC2 production. Appropriate production level of MUC2 and mucus contributes to gut barrier function and homeostasis. The production of MUC2 is regulated by a series of physiological processes, which are orchestrated by various bioactive molecules, signaling pathways and gut microbiota, etc., forming a complex regulatory network. Incorporating the latest findings, this review provided a comprehensive summary of MUC2, including its structure, significance and secretory process. Furthermore, we also summarized the molecular mechanisms of the regulation of MUC2 production aiming to provide developmental directions for future researches on MUC2, which can act as a potential prognostic indicator and targeted therapeutic manipulation for diseases. Collectively, we elucidated the micro-level mechanisms underlying MUC2-related phenotypes, hoping to offer some constructive guidance for intestinal and overall health of mankind.

The expression of SERPINE1 in colon cancer and its regulatory network and prognostic value

BACKGROUND

Serpin Peptidase Inhibitor 1 (SERPINE1) promotes cancer progression by making it easier for cancer cells to spread to surrounding normal tissue. We expect to understand the prognostic value and regulatory network of SERPINE1 in colon cancer using bioinformatics methods.

METHODS

The expression of target gene SERPINE1 in varying cancers was analyzed by the Tumor Immune Estimation Resource (TIMER) database. SERPINE1 expression in Colon Adenocarcinoma and normal tissue samples was assessed by starBase and UALCAN databases. SERPINE1 expression in clinical tissues was assayed using quantitative reverse transcription Polymerase Chain Reaction (qRT-PCR). SERPINE1 expression was detected in colon cancer patients with various clinical features (age, gender, nodal metastasis status, race, stages, and subtype) using analysis of variance. Survival curve was used to analyze the effect of high and low expression of SERPINE1 on the survival time of patients with different clinical phenotypes. Gene Set Enrichment Analysis (GSEA) was conducted on the results of LinkFinder calculation using LinkInterpreter module, which was combined with Pearson correlation analysis to obtain the kinase targets and miRNA targets, transcription factor targets, and corresponding signaling pathways associated with SERPINE1. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on GSEA result. Finally, Gene Multiple Association Network Integration Algorithm (GeneMANIA) was utilized to establish a network of genes related to the kinases MAPK1, miR-18a, and SRF_Q, and biological functions were analyzed.

RESULTS

Based on TIMER, starBase, and UALCAN databases, SERPINE1 was found to be remarkably highly expressed in colon cancer patients, which was further verified by clinical tissue. It was also associated with different clinical features (nodal metastasis status, stages, subtypes). Additionally, survival analysis showed that patients with low expression of SERPINE1 had a longer survival time, suggesting that SERPINE1 was a prognostic risk factor for colon cancer. Pearson correlation analysis revealed that the expression of Integrin Alpha 5 (ITGA5), Matrix Metallopeptidase 19 (MMP19), and ADAM Metallopeptidase with Thrombospondin Type 1 Motif, 4 (ADAMTS4) had the highest correlation with that of SERPINE1. The GSEA results indicated that these genes were mainly enriched in the pathways of RNA expression and kinases. Finally, GeneMANIA analysis was introduced to construct the molecular network of SERPINE1.

CONCLUSION

Overall, our bioinformatics analyses comprehensively described the networks involved SERPINE1 in colon cancer and the potentially associated molecular mechanisms.