A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition

A cellular regulator of the niche: telocyte

Interstitial cells are present in the environment of stem cells in order to increase stem cell proliferation and differentiation and they are important to increase the efficiency of their transplantation. Telocytes (TCs) play an important role both in the preservation of tissue organ integrity and in the pathophysiology of many diseases, especially cancer. They make homo- or heterocellular contacts to form the structure of 3D network through their telopodes and deliver signaling molecules via a juxtacrine and/or paracrine association by budding shed vesicles into the vascular, nervous and endocrine systems. During this interaction, along with organelles, mRNA, microRNA, long non-coding RNA, and genomic DNA are transferred. This review article not only specifies the properties of TCs and their roles in the tissue organ microenvironment but also gives information about the factors that play a role in the transport of epigenetic information by TCs.

Comprehensive information-based differential gene regulatory networks analysis (CIdrgn): Application to gastric cancer and chemotherapy-responsive gene network identification

Biological condition-responsive gene network analysis has attracted considerable research attention because of its ability to identify pathways or gene modules involved in the underlying mechanisms of diseases. Although many condition-specific gene network identification methods have been developed, they are based on partial or incomplete gene regulatory network information, with most studies only considering the differential expression levels or correlations among genes. However, a single gene-based analysis cannot effectively identify the molecular interactions involved in the mechanisms underlying diseases, which reflect perturbations in specific molecular network functions rather than disorders of a single gene. To comprehensively identify differentially regulated gene networks, we propose a novel computational strategy called comprehensive analysis of differential gene regulatory networks (CIdrgn). Our strategy incorporates comprehensive information on the networks between genes, including the expression levels, edge structures and regulatory effects, to measure the dissimilarity among networks. We extended the proposed CIdrgn to cell line characteristic-specific gene network analysis. Monte Carlo simulations showed the effectiveness of CIdrgn for identifying differentially regulated gene networks with different network structures and scales. Moreover, condition-responsive network identification in cell line characteristic-specific gene network analyses was verified. We applied CIdrgn to identify gastric cancer and itsf chemotherapy (capecitabine and oxaliplatin) -responsive network based on the Cancer Dependency Map. The CXC family of chemokines and cadherin gene family networks were identified as gastric cancer-specific gene regulatory networks, which was verified through a literature survey. The networks of the olfactory receptor family with the ASCL1/FOS family were identified as capecitabine- and oxaliplatin sensitive -specific gene networks. We expect that the proposed CIdrgn method will be a useful tool for identifying crucial molecular interactions involved in the specific biological conditions of cancer cell lines, such as the cancer stage or acquired anticancer drug resistance.

Unraveling the Molecular Puzzle: Exploring Gene Networks across Diverse EMT Status of Cell Lines

Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial-mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues.

New insights into KLFs and SOXs in cancer pathogenesis, stemness, and therapy

Despite the development of cancer therapies, the success of most treatments has been impeded by drug resistance. The crucial role of tumor cell plasticity has emerged recently in cancer progression, cancer stemness and eventually drug resistance. Cell plasticity drives tumor cells to reversibly convert their cell identity, analogous to differentiation and dedifferentiation, to adapt to drug treatment. This phenotypical switch is driven by alteration of the transcriptome. Several pluripotent factors from the KLF and SOX families are closely associated with cancer pathogenesis and have been revealed to regulate tumor cell plasticity. In this review, we particularly summarize recent studies about KLF4, KLF5 and SOX factors in cancer development and evolution, focusing on their roles in cancer initiation, invasion, tumor hierarchy and heterogeneity, and lineage plasticity. In addition, we discuss the various regulation of these transcription factors and related cutting-edge drug development approaches that could be used to drug "undruggable" transcription factors, such as PROTAC and PPI targeting, for targeted cancer therapy. Advanced knowledge could pave the way for the development of novel drugs that target transcriptional regulation and could improve the outcome of cancer therapy.

Computational Tactics for Precision Cancer Network Biology

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

KLF5 regulates epithelial-mesenchymal transition of liver cancer cells in the context of p53 loss through miR-192 targeting of ZEB2

Krüppel-like factor 5 (KLF5) can both promote and suppress cell migration, but the underlying mechanisms have not been elucidated. In this study, we show that the function of KLF5 in epithelial-mesenchymal transition (EMT) and migration of liver cancer cells depends on the status of the cellular tumor antigen p53 (p53). Furthermore, zinc finger E-box-binding homeobox 2 (ZEB2) is the main regulator of KLF5 in EMT in liver cancer cells in the context of p53 loss. Most importantly, the regulation of ZEB2 by p53 and KLF5 is indirect and that miR-192 mediates this regulation. Finally, we find that in invasive liver cancer, KLF5 is absent in the context of p53 loss or mutation.

The roles and regulation of the KLF5 transcription factor in cancers

Krüppel‐like factor 5 (KLF5) is a member of the KLF family. Recent studies have suggested that KLF5 regulates the expression of a large number of new target genes and participates in diverse cellular functions, such as stemness, proliferation, apoptosis, autophagy, and migration. In response to multiple signaling pathways, various transcriptional modulation and posttranslational modifications affect the expression level and activity of KLF5. Several transgenic mouse models have revealed the physiological and pathological functions of KLF5 in different cancers. Studies of KLF5 will provide prognostic biomarkers, therapeutic targets, and potential drugs for cancers.

Global gene network exploration based on explainable artificial intelligence approach

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.

A New Switch for TGFβ in Cancer

The TGFβ cytokine plays dichotomous roles during tumor progression. In normal and premalignant cancer cells, the TGFβ signaling pathway inhibits proliferation and promotes cell-cycle arrest and apoptosis. However, the activation of this pathway in late-stage cancer cells could facilitate the epithelial-to-mesenchymal transition, stemness, and mobile features to enhance tumorigenesis and metastasis. The opposite functions of TGFβ signaling during tumor progression make it a challenging target to develop anticancer interventions. Nevertheless, the recent discovery of cellular contextual determinants, especially the binding partners of the transcription modulators Smads, is critical to switch TGFβ responses from proapoptosis to prometastasis. In this review, we summarize the recently identified contextual determinants (such as PSPC1, KLF5, 14-3-3ζ, C/EBPβ, and others) and the mechanisms of how tumor cells manage the context-dependent autonomous TGFβ responses to potentiate tumor progression. With the altered expression of some contextual determinants and their effectors during tumor progression, the aberrant molecular prometastatic switch might serve as a new class of theranostic targets for developing anticancer strategies.

Robust Sample-Specific Stability Selection with Effective Error Control

Identifying individual characteristics is a crucial issue in personalized genome research. To effectively identify sample-specific characteristics, we propose a novel strategy called robust sample-specific stability selection. Although stability selection shows effective feature selection results and has attractive theoretical property (i.e., per-family error rate control), the method's results are sensitive to the value of the regularization parameter because the method performs feature selection based only on the particular parameter value that maximizes the selection probability. To resolve this issue, we propose robust stability selection and show that our method provides an effective theoretical property (i.e., effective per-family error rate control). We also propose a sample-specific random lasso based on the kernel-based L₁-type regularization and weighted random sampling. The proposed robust sample-specific stability selection estimates the selection probabilities of variables using the sample-specific random lasso and then selects variables based on robust stability selection. Our method controls the effect of samples on sample-specific analysis by the two-stage strategy (i.e., the weighted random sampling and the kernel-based L₁-type approach in the random lasso), and thus we can effectively perform sample-specific analysis without disturbances of samples having characteristics different from those of the target sample. We observe from the numerical studies that our strategies can effectively perform sample-specific analysis and provide biologically reliable results in gene selection.

Transcriptome analyses of reprogrammed feather / scale chimeric explants revealed co-expressed epithelial gene networks during organ specification

BACKGROUND

The molecular mechanism controlling regional specific skin appendage phenotypes is a fundamental question that remains unresolved. We recently identified feather and scale primordium associated genes and with functional studies, proposed five major modules are involved in scale-to-feather conversion and their integration is essential to form today's feathers. Yet, how the molecular networks are wired and integrated at the genomic level is still unknown.

RESULTS

Here, we combine classical recombination experiments and systems biology technology to explore the molecular mechanism controlling cell fate specification. In the chimeric explant, dermal fate is more stable, while epidermal fate is reprogrammed to be similar to the original appendage type of the mesenchyme. We analyze transcriptome changes in both scale-to-feather and feather-to-scale transition in the epidermis. We found a highly interconnected regulatory gene network controlling skin appendage types. These gene networks are organized around two molecular hubs, β-catenin and retinoic acid (RA), which can bind to regulatory elements controlling downstream gene expression, leading to scale or feather fates. ATAC sequencing analyses revealed about 1000 altered widely distributed chromatin open sites. We find that perturbation of a key gene alters the expression of many other co-expressed genes in the same module.

CONCLUSIONS

Our findings suggest that these feather / scale fate specification genes form an interconnected network and rewiring of the gene network can lead to changes of appendage phenotypes, acting similarly to endogenous reprogramming at the tissue level. This work shows that key hub molecules, β-catenin and retinoic acid, regulate scale / feather fate specification gene networks, opening up new possibilities to understand the switches controlling organ phenotypes in a two component (epithelial and mesenchyme) system.

Hybrid epithelial/mesenchymal phenotypes promote metastasis and therapy resistance across carcinomas

Cancer metastasis and therapy resistance are the major unsolved clinical challenges, and account for nearly all cancer-related deaths. Both metastasis and therapy resistance are fueled by epithelial plasticity, the reversible phenotypic transitions between epithelial and mesenchymal phenotypes, including epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET). EMT and MET have been largely considered as binary processes, where cells detach from the primary tumor as individual units with many, if not all, traits of a mesenchymal cell (EMT) and then convert back to being epithelial (MET). However, recent studies have demonstrated that cells can metastasize in ways alternative to traditional EMT paradigm; for example, they can detach as clusters, and/or occupy one or more stable hybrid epithelial/mesenchymal (E/M) phenotypes that can be the end point of a transition. Such hybrid E/M cells can integrate various epithelial and mesenchymal traits and markers, facilitating collective cell migration. Furthermore, these hybrid E/M cells may possess higher tumor-initiation and metastatic potential as compared to cells on either end of the EMT spectrum. Here, we review in silico, in vitro, in vivo and clinical evidence for the existence of one or more hybrid E/M phenotype(s) in multiple carcinomas, and discuss their implications in tumor-initiation, tumor relapse, therapy resistance, and metastasis. Together, these studies drive the emerging notion that cells in a hybrid E/M phenotype may occupy 'metastatic sweet spot' in multiple subtypes of carcinomas, and pathways linked to this (these) hybrid E/M state(s) may be relevant as prognostic biomarkers as well as a promising therapeutic targets.

The Many-Faced Program of Epithelial-Mesenchymal Transition: A System Biology-Based View

System biology uses a range of experimental and statistical methods to dissect complex processes that results from alterations in biological models. Given the complexity of the epithelial–mesenchymal transition (EMT) program, system biology represents a promising approach to understanding its fine molecular regulation by the interpretation of high-throughput datasets. Herein, we review recent contributions of system biology applied to the field of EMT physiology and illustrate the importance of these approaches to model biological networks that are perturbed during the transition. Together, these results allowed the definition of an EMT signature across different tumor types, the identification of dysregulated processes and new modules of regulation, making possible to reveal the EMT molecular visage underneath.

Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as an L₁-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-based L₁-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-based L₁-type regularization method based on a modified k-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample-specific generalized cross-validation for choosing the sample-specific tuning parameters in the kernel-based L₁-type regularization method. Numerical studies demonstrate that the proposed adaptive NetworkProfiler effectively performs sample-specific gene network construction. We apply the proposed statistical strategy to the publicly available Sanger Genomic data analysis, and extract anti-cancer drug sensitivity-specific gene regulatory networks.

Inflammatory breast cancer: a model for investigating cluster-based dissemination

Metastases claim more than 90% of cancer-related patient deaths and are usually seeded by a subset of circulating tumor cells shed off from the primary tumor. In circulation, circulating tumor cells are found both as single cells and as clusters of cells. The clusters of circulating tumor cells, although many fewer in number, possess much higher metastatic potential as compared to that of individual circulating tumor cells. In this review, we highlight recent insights into molecular mechanisms that can enable the formation of these clusters—(a) hybrid epithelial/mesenchymal phenotype of cells that couples their ability to migrate and adhere, and (b) intercellular communication that can spatially coordinate the cluster formation and provide survival signals to cancer cells. Building upon these molecular mechanisms, we also offer a possible mechanistic understanding of why clusters are endowed with a higher metastatic potential. Finally, we discuss the highly aggressive Inflammatory Breast Cancer as an example of a carcinoma that can metastasize via clusters and corroborates the proposed molecular mechanisms.

Grhl2 reduces invasion and migration through inhibition of TGFβ-induced EMT in gastric cancer

Metastasis is one of the typical features of malignancy that significantly increases cancer-related mortality. Recent studies have shown that epithelial-mesenchymal transition (EMT) is closely related to the invasion and migration of cancer cells. Grainyhead-like 2 (Grhl2), a transcription factor, has been reported to be associated with several tumor processes including EMT. In the previous study, we have reported that Grhl2 functioned as a tumor suppressor in proliferation and apoptosis of gastric cancer. Here we aim to explore the effects of Grhl2 on invasion and migration of gastric cancer and further clarify its possible underlying mechanisms. As a result, in both SGC7901 and MKN45 cells, Grhl2 overexpression significantly inhibited the ability of invasion and migration. In addition, preliminary experiments showed that Grhl2 reduces the protein expression of matrix metalloproteinase-2, -7 and -9 (MMP-2, MMP-7 and MMP-9). Most importantly, Grhl2 antagonizes transforming growth factor-β (TGFβ)-induced EMT, and inhibition of TGFβ signaling pathways can restore Grhl2 expression. Finally, the results of subcutaneous xenograft model indicated that Grhl2 suppresses the growth of gastric cancer and reverses EMT process in vivo. Meanwhile, the metastatic tumor model further confirmed the inhibition of Grhl2 on metastasis of gastric cancer. Taken together, our findings proved that Grhl2, functioned as a tumor suppressor, reduces the invasion and migration through inhibition of TGFβ-induced EMT in gastric cancer.

PDLIM1 Stabilizes the E-Cadherin/β-Catenin Complex to Prevent Epithelial-Mesenchymal Transition and Metastatic Potential of Colorectal Cancer Cells

Metastasis is a major cause of death in patients with colorectal cancer, and increasing evidence supports the contribution of the epithelial-mesenchymal transition (EMT) to cancer progression. The dissociation of the E-cadherin/β-catenin adhesion complex represents a key step in EMT and promotes cancer invasion and metastasis, but the upstream signaling pathways regulating this interaction are poorly understood. Here, we show that PDLIM1, a member of the PDZ and LIM protein family, was downregulated in highly metastatic colorectal cancer cells and liver metastases from colorectal cancer patients. We found that loss of PDLIM1 promoted the expression of EMT markers and increased the invasive and migratory properties of multiple colorectal cancer cell lines. Furthermore, PDLIM1 knockdown increased colon-derived liver metastasis in an orthotopic colorectal cancer model and promoted distant metastatic colonization in an experimental lung metastasis model. Mechanistic investigations revealed that PDLIM1 interacted with and stabilized the E-cadherin/β-catenin complex, thereby inhibiting the transcriptional activity of β-catenin and preventing EMT. Accordingly, PDLIM1 overexpression attenuated EMT of colorectal cancer cells. Moreover, the downregulation of PDLIM1 in colorectal cancer samples correlated with reduced E-cadherin and membrane β-catenin levels, and was associated with shorter overall survival. In conclusion, our study demonstrates that PDLIM1 suppresses EMT and metastatic potential of colorectal cancer cells by stabilizing β-catenin at cell-cell junctions, and its loss in metastatic tissues may represent a potential prognostic marker of aggressive disease.

Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data

BACKGROUND

Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival.

RESULTS

This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies.

CONCLUSIONS

The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples.

Krüppel-like factors in cancer progression: three fingers on the steering wheel

Krüppel-like factors (KLFs) comprise a highly conserved family of zinc finger transcription factors, that are involved in a plethora of cellular processes, ranging from proliferation and apoptosis to differentiation, migration and pluripotency. During the last few years, evidence on their role and deregulation in different human cancers has been emerging. This review will discuss current knowledge on Krüppel-like transcription in the epithelial-mesenchymal transition (EMT), invasion and metastasis, with a focus on epithelial cancer biology and the extensive interface with pluripotency. Furthermore, as KLFs are able to mediate different outcomes, important influences of the cellular and microenvironmental context will be highlighted. Finally, we attempt to integrate diverse findings on KLF functions in EMT and stem cell biology to ft in the current model of cellular plasticity as a tool for successful metastatic dissemination.

Robust prediction of anti-cancer drug sensitivity and sensitivity-specific biomarker

The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc.) and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

A core invasiveness gene signature reflects epithelial-to-mesenchymal transition but not metastatic potential in breast cancer cell lines and tissue samples

Introduction

Metastases remain the primary cause of cancer-related death. The acquisition of invasive tumour cell behaviour is thought to be a cornerstone of the metastatic cascade. Therefore, gene signatures related to invasiveness could aid in stratifying patients according to their prognostic profile. In the present study we aimed at identifying an invasiveness gene signature and investigated its biological relevance in breast cancer.

Methods & Results

We collected a set of published gene signatures related to cell motility and invasion. Using this collection, we identified 16 genes that were represented at a higher frequency than observed by coincidence, hereafter named the core invasiveness gene signature. Principal component analysis showed that these overrepresented genes were able to segregate invasive and non-invasive breast cancer cell lines, outperforming sets of 16 randomly selected genes (all P<0.001). When applied onto additional data sets, the expression of the core invasiveness gene signature was significantly elevated in cell lines forced to undergo epithelial-mesenchymal transition. The link between core invasiveness gene expression and epithelial-mesenchymal transition was also confirmed in a dataset consisting of 2420 human breast cancer samples. Univariate and multivariate Cox regression analysis demonstrated that CIG expression is not associated with a shorter distant metastasis free survival interval (HR = 0.956, 95%C.I. = 0.896–1.019, P = 0.186).

Discussion

These data demonstrate that we have identified a set of core invasiveness genes, the expression of which is associated with epithelial-mesenchymal transition in breast cancer cell lines and in human tissue samples. Despite the connection between epithelial-mesenchymal transition and invasive tumour cell behaviour, we were unable to demonstrate a link between the core invasiveness gene signature and enhanced metastatic potential.

KLF5 activates microRNA 200 transcription to maintain epithelial characteristics and prevent induced epithelial-mesenchymal transition in epithelial cells

KLF5 is an essential basic transcriptional factor that regulates a number of physiopathological processes. In this study, we tested whether and how KLF5 modulates the epithelial-mesenchymal transition (EMT). Using transforming growth factor β (TGF-β)- and epidermal growth factor (EGF)-treated epithelial cells as an established model of EMT, we found that KLF5 was downregulated during EMT and that knockdown of KLF5 induced EMT even in the absence of TGF-β and EGF treatment, as indicated by phenotypic and molecular EMT properties. Array-based screening suggested and biochemical analyses confirmed that the microRNA 200 (miR-200) microRNAs, a group of well-established EMT repressors, were transcriptionally activated by KLF5 via its direct binding to the GC boxes in miR-200 gene promoters. Functionally, overexpression of miR-200 prevented the EMT induced by KLF5 knockdown or by TGF-β and EGF treatment, and ectopic expression of KLF5 attenuated TGF-β- and EGF-induced EMT by rescuing the expression of miR-200. In mouse prostates, knockout of Klf5 downregulated the miR-200 family and induced molecular changes indicative of EMT. These findings indicate that KLF5 maintains epithelial characteristics and prevents EMT by transcriptionally activating the miR-200 family in epithelial cells.

Krüppel-like factors in cancer

Krüppel-like factors (KLFs) are a family of DNA-binding transcriptional regulators with diverse and essential functions in a multitude of cellular processes, including proliferation, differentiation, migration, inflammation and pluripotency. In this Review, we discuss the roles and regulation of the 17 known KLFs in various cancer-relevant processes. Importantly, the functions of KLFs are context dependent, with some KLFs having different roles in normal cells and cancer, during cancer development and progression and in different cancer types. We also identify key questions for the field that are likely to lead to important new translational research and discoveries in cancer biology.

KLF6-SV1 drives breast cancer metastasis and is associated with poor survival

Metastasis is the major cause of cancer mortality. A more thorough understanding of the mechanisms driving this complex multistep process will aid in the identification and characterization of therapeutically targetable genetic drivers of disease progression. We demonstrate that KLF6-SV1, an oncogenic splice variant of the KLF6 tumor suppressor gene, is associated with increased metastatic potential and poor survival in a cohort of 671 lymph node-negative breast cancer patients. KLF6-SV1 overexpression in mammary epithelial cell lines resulted in an epithelial-to-mesenchymal-like transition and drove aggressive multiorgan metastatic disease in multiple in vivo models. Additionally, KLF6-SV1 loss-of-function studies demonstrated reversion to an epithelial and less invasive phenotype. Combined, these findings implicate KLF6-SV1 as a key driver of breast cancer metastasis that distinguishes between indolent and lethal early-stage disease and provides a potential therapeutic target for invasive breast cancer.

Gene regulation, modulation, and their applications in gene expression data analysis

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.

The transcription factors Grainyhead-like 2 and NK2-homeobox 1 form a regulatory loop that coordinates lung epithelial cell morphogenesis and differentiation

The Grainyhead family of transcription factors controls morphogenesis and differentiation of epithelial cell layers in multicellular organisms by regulating cell junction- and proliferation-related genes. Grainyhead-like 2 (Grhl2) is expressed in developing mouse lung epithelium and is required for normal lung organogenesis. The specific epithelial cells expressing Grhl2 and the genes regulated by Grhl2 in normal lungs are mostly unknown. In these studies we identified the NK2-homeobox 1 transcription factor (Nkx2-1) as a direct transcriptional target of Grhl2. By binding and transcriptional assays and by confocal microscopy we showed that these two transcription factors form a positive feedback loop in vivo and in cell lines and are co-expressed in lung bronchiolar and alveolar type II cells. The morphological changes observed in flattening lung alveolar type II cells in culture are associated with down-regulation of Grhl2 and Nkx2-1. Reduction of Grhl2 in lung epithelial cell lines results in lower expression levels of Nkx2-1 and of known Grhl2 target genes. By microarray analysis we identified that in addition to Cadherin1 and Claudin4, Grhl2 regulates other cell interaction genes such as semaphorins and their receptors, which also play a functional role in developing lung epithelium. Impaired collective cell migration observed in Grhl2 knockdown cell monolayers is associated with reduced expression of these genes and may contribute to the altered epithelial phenotype reported in Grhl2 mutant mice. Thus, Grhl2 functions at the nexus of a novel regulatory network, connecting lung epithelial cell identity, migration, and cell-cell interactions.