miR-181a plays the tumor-suppressor role in chronic myeloid leukemia CD34 + cells partially via SERPINE1.

The formation of the BCR-ABL fusion gene drives human chronic myeloid leukemia (CML). The last 2 decades have witnessed that specific tyrosine kinase inhibitors (TKIs, e.g., imatinib mesylate, IM) against ABL1 improve disease treatment, although some patients still suffer from relapse and TKI resistance. Therefore, a better understanding of the molecular pathology of CML is still urgently needed. miR-181a-5p (miR-181a) acts as a tumor suppressor in CML; however, the molecular mechanism of miR-181a in CML stem/progenitor cells remains elusive. Herein, we showed that miR-181a inhibited the growth of CML CD34+ cells, including the quiescent subset, and sensitized them to IM treatment, while miR-181a inhibition by a sponge sequence collaborated with BCR-ABL to enhance the growth of normal CD34+ cells. Transcriptome data and biochemical analysis revealed that SERPINE1 was a bona fide and critical target of miR-181a, which deepened the understanding of the regulatory mechanism of SERPINE1. Genetic and pharmacological inhibition of SERPINE1 led to apoptosis mainly mediated by caspase-9 activation. The dual inhibition of SERPINE1 and BCR-ABL exhibited a significantly stronger inhibitory effect than a single agent. Taken together, this study demonstrates that a novel miR-181a/SERPINE1 axis modulates CML stem/progenitor cells, which likely provides an important approach to override TKI resistance.

A conserved ZFX/WNT3 axis modulates the growth and imatinib response of chronic myeloid leukemia stem/progenitor cells.

BACKGROUND: Zinc finger protein X-linked (ZFX) has been shown to promote the growth of tumor cells, including leukemic cells. However, the role of ZFX in the growth and drug response of chronic myeloid leukemia (CML) stem/progenitor cells remains unclear. METHODS: Real-time quantitative PCR (RT-qPCR) and immunofluorescence were used to analyze the expression of ZFX and WNT3 in CML CD34+ cells compared with normal control cells. Short hairpin RNAs (shRNAs) and clustered regularly interspaced short palindromic repeats/dead CRISPR-associated protein 9 (CRISPR/dCas9) technologies were used to study the role of ZFX in growth and drug response of CML cells. Microarray data were generated to compare ZFX-silenced CML CD34+ cells with their controls. Chromatin immunoprecipitation (ChIP) and luciferase reporter assays were performed to study the molecular mechanisms of ZFX to regulate WNT3 expression. RT-qPCR and western blotting were used to study the effect of ZFX on ß-catenin signaling. RESULTS: We showed that ZFX expression was significantly higher in CML CD34+ cells than in control cells. Overexpression and gene silencing experiments indicated that ZFX promoted the in vitro growth of CML cells, conferred imatinib mesylate (IM) resistance to these cells, and enhanced BCR/ABL-induced malignant transformation. Microarray data and subsequent validation revealed that WNT3 transcription was conservatively regulated by ZFX. WNT3 was highly expressed in CML CD34+ cells, and WNT3 regulated the growth and IM response of these cells similarly to ZFX. Moreover, WNT3 overexpression partially rescued ZFX silencing-induced growth inhibition and IM hypersensitivity. ZFX silencing decreased WNT3/ß-catenin signaling, including c-MYC and CCND1 expression. CONCLUSION: The present study identified a novel ZFX/WNT3 axis that modulates the growth and IM response of CML stem/progenitor cells.

DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer.

SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer.

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

Systematical Analysis of the Cancer Genome Atlas Database Reveals EMCN/MUC15 Combination as a Prognostic Signature for Gastric Cancer.

Digestive cancers-including gastric cancer (GC), colorectal cancer, hepatocellular carcinoma, esophageal cancer, and pancreatic cancer-accounted for 26% of cancer cases and 35% of cancer deaths worldwide in 2018. It is crucial and urgent to develop biomarkers for the diagnosis, prognosis, and therapeutic benefits of digestive cancers, especially for GC, since the incidence of GC is lower only than lung cancer in China, is hard to detect at an early stage, and is associated with poor prognosis. Mucins, glycoproteins encoded by MUC family genes, act as a part of a physical barrier in the digestive tract and participate in various signaling pathways. Some mucins have been used or proposed as biomarkers for carcinomas, such as MUC16 (CA125) and MUC4. However, there are no systematic investigations on the association of MUC family members with diagnoses and clinical outcomes even though relevant data have been largely accumulated in the past decade. By analyzing transcriptomic and clinical data of digestive cancer samples from TCGA involving colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), and pancreatic adenocarcinoma (PAAD), it was found that expressions levels of MUC15, MUC13, and MUC21 were individually associated with survival for digestive cancers, and high expressions of EMCN (MUC14) and MUC15 were correlated with poor survival for STAD. Cox regression analysis indicated the predictive power of an EMCN/MUC15 combination for overall survival (OS) of GC patients, which was validated on an independent dataset from GEO. EMCN/MUC15 correlated genes were identified to be enriched in cancer-related processes, such as vasculature development, mitosis, and immunity. Therefore, we propose that an EMCN/MUC15 combination could be a potential prognostic signature for gastric cancer.

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models.

BACKGROUND: One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform. RESULTS: In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated. CONCLUSION: DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.

A comparison of next-generation sequencing analysis methods for cancer xenograft samples.

The application of next-generation sequencing (NGS) technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft (PDX) models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.

Differential networking meta-analysis of gastric cancer across Asian and American racial groups.

BACKGROUND: Gastric Carcinoma is one of the most lethal cancer around the world, and is also the most common cancers in Eastern Asia. A lot of differentially expressed genes have been detected as being associated with Gastric Carcinoma (GC) progression, however, little is known about the underlying dysfunctional regulation mechanisms. To address this problem, we previously developed a differential networking approach that is characterized by involving differential coexpression analysis (DCEA), stage-specific gene regulatory network (GRN) modelling and differential regulation networking (DRN) analysis. RESULT: In order to implement differential networking meta-analysis, we developed a novel framework which integrated the following steps. Considering the complexity and diversity of gastric carcinogenesis, we first collected three datasets (GSE54129, GSE24375 and TCGA-STAD) for Chinese, Korean and American, and aimed to investigate the common dysregulation mechanisms of gastric carcinogenesis across racial groups. Then, we constructed conditional GRNs for gastric cancer corresponding to normal and carcinoma, and prioritized differentially regulated genes (DRGs) and gene links (DRLs) from three datasets separately by using our previously developed differential networking method. Based on our integrated differential regulation information from three datasets and prior knowledge (e.g., transcription factor (TF)-target regulatory relationships and known signaling pathways), we eventually generated testable hypotheses on the regulation mechanisms of two genes, XBP1 and GIF, out of 16 common cross-racial DRGs in gastric carcinogenesis. CONCLUSION: The current cross-racial integrative study from the viewpoint of differential regulation networking provided useful clues for understanding the common dysfunctional regulation mechanisms of gastric cancer progression and discovering new universal drug targets or biomarkers for gastric cancer.

Differential regulation analysis reveals dysfunctional regulatory mechanism involving transcription factors and microRNAs in gastric carcinogenesis.

Gastric cancer (GC) is one of the most incident malignancies in the world. Although lots of featured genes and microRNAs (miRNAs) have been identified to be associated with gastric carcinogenesis, underlying regulatory mechanisms still remain unclear. In order to explore the dysfunctional mechanisms of GC, we developed a novel approach to identify carcinogenesis relevant regulatory relationships, which is characterized by quantifying the difference of regulatory relationships between stages. Firstly, we applied the strategy of differential coexpression analysis (DCEA) to transcriptomic datasets including paired mRNA and miRNA of gastric samples to identify a set of genes/miRNAs related to gastric cancer progression. Based on these genes/miRNAs, we constructed conditional combinatorial gene regulatory networks (cGRNs) involving both transcription factors (TFs) and miRNAs. Enrichment of known cancer genes/miRNAs and predicted prognostic genes/miRNAs was observed in each cGRN. Then we designed a quantitative method to measure differential regulation level of every regulatory relationship between normal and cancer, and the known cancer genes/miRNAs proved to be ranked significantly higher. Meanwhile, we defined differentially regulated link (DRL) by combining differential regulation, differential expression and the regulation contribution of the regulator to the target. By integrating survival analysis and DRL identification, three master regulators TCF7L1, TCF4, and MEIS1 were identified and testable hypotheses of dysfunctional mechanisms underlying gastric carcinogenesis related to them were generated. The fine-tuning effects of miRNAs were also observed. We propose that this differential regulation network analysis framework is feasible to gain insights into dysregulated mechanisms underlying tumorigenesis and other phenotypic changes.