Key regulators in prostate cancer identified by co-expression module analysis

Untousling the Role of Tousled-like Kinase 1 in DNA Damage Repair

DNA damage repair lies at the core of all cells' survival strategy, including the survival strategy of cancerous cells. Therefore, targeting such repair mechanisms forms the major goal of cancer therapeutics. The mechanism of DNA repair has been tousled with the discovery of multiple kinases. Recent studies on tousled-like kinases have brought significant clarity on the effectors of these kinases which stand to regulate DSB repair. In addition to their well-established role in DDR and cell cycle checkpoint mediation after DNA damage or inhibitors of replication, evidence of their suspected involvement in the actual DSB repair process has more recently been strengthened by the important finding that TLK1 phosphorylates RAD54 and regulates some of its activities in HRR and localization in the cell. Earlier findings of its regulation of RAD9 during checkpoint deactivation, as well as defined steps during NHEJ end processing, were earlier hints of its broadly important involvement in DSB repair. All this has opened up new avenues to target cancer cells in combination therapy with genotoxins and TLK inhibitors.

Targeting Prostate Cancer, the 'Tousled Way'

Androgen deprivation therapy (ADT) has been the mainstay of prostate cancer (PCa) treatment, with success in developing more effective inhibitors of androgen synthesis and antiandrogens in clinical practice. However, hormone deprivation and AR ablation have caused an increase in ADT-insensitive PCas associated with a poor prognosis. Resistance to ADT arises through various mechanisms, and most castration-resistant PCas still rely on the androgen axis, while others become truly androgen receptor (AR)-independent. Our research identified the human tousled-like kinase 1 (TLK1) as a crucial early mediator of PCa cell adaptation to ADT, promoting androgen-independent growth, inhibiting apoptosis, and facilitating cell motility and metastasis. Although explicit, the growing role of TLK1 biology in PCa has remained underrepresented and elusive. In this review, we aim to highlight the diverse functions of TLK1 in PCa, shed light on the molecular mechanisms underlying the transition from androgen-sensitive (AS) to an androgen-insensitive (AI) disease mediated by TLK1, and explore potential strategies to counteract this process. Targeting TLK1 and its associated signaling could prevent PCa progression to the incurable metastatic castration-resistant PCa (mCRPC) stage and provide a promising approach to treating PCa.

Tousled-like kinase 1: a novel factor with multifaceted role in mCRPC progression and development of therapy resistance

Standard treatment for advanced Prostate Cancer (PCa) consists of androgen deprivation therapy (ADT), but ultimately fails, resulting in the incurable phase of the disease: metastatic castration-resistant prostate cancer (mCRPC). Targeting PCa cells before their progression to mCRPC would greatly improve the outcome, if strategies could be devised selectively targeting androgen receptor (AR)-dependent and/or independent compensatory pathways which promote mCRPC development. Combination therapy by targeting the DNA damage response (DDR) along with ADT has been limited by general toxicity, and a goal of clinical trials is how to target the DDR more specifically. In recent years, our lab has identified a key role for the DDR kinase, TLK1, in mediating key aspects of adaptation to ADT, first by promoting a cell cycle arrest (through the TLK1>NEK1>ATR>Chk1 kinase cascade) under the unfavorable growth conditions (androgen deprivation), and then by reprogramming the PCa cells to adapt to androgen-independent growth via the NEK1>YAP/AR>CRPC conversion. In addition, TLK1 plays a key anti-apoptotic role via the NEK1>VDAC1 regulation on the intrinsic mitochondrial apoptotic pathway when the DDR is activated. Finally, TLK1 was recently identified as having an important role in motility and metastasis via regulation of the kinases MK5/PRAK and AKT (indirectly via AKTIP).

CRPMKB: a knowledge base of cancer risk prediction models for systematic comparison and personalized applications

MOTIVATION

In the era of big data and precision medicine, accurate risk assessment is a prerequisite for the implementation of risk screening and preventive treatment. A large number of studies have focused on the risk of cancer, and related risk prediction models have been constructed, but there is a lack of effective resource integration for systematic comparison and personalized applications. Therefore, the establishment and analysis of the cancer risk prediction model knowledge base (CRPMKB) is of great significance.

RESULTS

The current knowledge base contains 802 model data. The model comparison indicates that the accuracy of cancer risk prediction was greatly affected by regional differences, cancer types and model types. We divided the model variables into four categories: environment, behavioral lifestyle, biological genetics and clinical examination, and found that there are differences in the distribution of various variables among different cancer types. Taking 50 genes involved in the lung cancer risk prediction models as an example to perform pathway enrichment analyses and the results showed that these genes were significantly enriched in p53 Signaling and Aryl Hydrocarbon Receptor Signaling pathways which are associated with cancer and specific diseases. In addition, we verified the biological significance of overlapping lung cancer genes via STRING database. CRPMKB was established to provide researchers an online tool for the future personalized model application and developing. This study of CRPMKB suggests that developing more targeted models based on specific demographic characteristics and cancer types will further improve the accuracy of cancer risk model predictions.

AVAILABILITY AND IMPLEMENTATION

CRPMKB is freely available at http://www.sysbio.org.cn/CRPMKB/. The data underlying this article are available in the article and in its online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TLK1-mediated MK5-S354 phosphorylation drives prostate cancer cell motility and may signify distinct pathologies

Metastases account for the majority of prostate cancer (PCa) deaths, and targeting them is a major goal of systemic therapy. We identified a novel interaction between two kinases: tousled‐like kinase 1 (TLK1) and MAP kinase‐activated protein kinase 5 (MK5) that promotes PCa spread. In PCa progression, TLK1–MK5 signalling appears to increase following antiandrogen treatment and in metastatic castration‐resistant prostate cancer (mCRPC) patients. Determinations of motility rates (2D and 3D) of different TLK1‐ and MK5‐perturbed cells, including knockout (KO) and knockdown (KD), as well as the use of specific inhibitors, showed the importance of these two proteins for in vitro dissemination. We established that TLK1 phosphorylates MK5 on three residues (S160, S354 and S386), resulting in MK5 activation, and additionally, mobility shifts of MK5 also supported its phosphorylation by TLK1 in transfected HEK 293 cells. Expression of MK5‐S354A or kinase‐dead MK5 in MK5‐depleted mouse embryonic fibroblast (MEF) cells failed to restore their motility compared with that of wild‐type (WT) MK5‐rescued MK5^−/− MEF cells. A pMK5‐S354 antiserum was used to establish this site as an authentic TLK1 target in androgen‐sensitive human prostate adenocarcinoma (LNCaP) cells, and was used in immunohistochemistry (IHC) studies of age‐related PCa sections from TRAMP (transgenic adenocarcinoma of the mouse prostate) mice and to probe a human tissue microarray (TMA), which revealed pMK5‐S354 level is correlated with disease progression (Gleason score and nodal metastases). In addition, The Cancer Genome Atlas (TCGA) analyses of PCa expression and genome‐wide association study (GWAS) relations identify TLK1 and MK5 as potential drivers of advanced PCa and as markers of mCRPC. Our work suggests that TLK1–MK5 signalling is functionally involved in driving PCa cell motility and clinical features of aggressiveness; hence, disruption of this axis may inhibit the metastatic spread of PCa.

Natural Product-Based Studies for the Management of Castration-Resistant Prostate Cancer: Computational to Clinical Studies

Background: With prostate cancer being the fifth-greatest cause of cancer mortality in 2020, there is a dire need to expand the available treatment options. Castration-resistant prostate cancer (CRPC) progresses despite androgen depletion therapy. The mechanisms of resistance are yet to be fully discovered. However, it is hypothesized that androgens depletion enables androgen-independent cells to proliferate and recolonize the tumor. Objectives: Natural bioactive compounds from edible plants and herbal remedies might potentially address this need. This review compiles the available cheminformatics-based studies and the translational studies regarding the use of natural products to manage CRPC. Methods: PubMed and Google Scholar searches for preclinical studies were performed, while ClinicalTrials.gov and PubMed were searched for clinical updates. Studies that were not in English and not available as full text were excluded. The period of literature covered was from 1985 to the present. Results and Conclusion: Our analysis suggested that natural compounds exert beneficial effects due to their broad-spectrum molecular disease-associated targets. In vitro and in vivo studies revealed several bioactive compounds, including rutaecarpine, berberine, curcumin, other flavonoids, pentacyclic triterpenoids, and steroid-based phytochemicals. Molecular modeling tools, including machine and deep learning, have made the analysis more comprehensive. Preclinical and clinical studies on resveratrol, soy isoflavone, lycopene, quercetin, and gossypol have further validated the translational potential of the natural products in the management of prostate cancer.

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

A Convergent Study of Genetic Variants Associated With Crohn's Disease: Evidence From GWAS, Gene Expression, Methylation, eQTL and TWAS

Crohn’s Disease (CD) is one of the predominant forms of inflammatory bowel disease (IBD). A combination of genetic and non-genetic risk factors have been reported to contribute to the development of CD. Many high-throughput omics studies have been conducted to identify disease associated risk variants that might contribute to CD, such as genome-wide association studies (GWAS) and next generation sequencing studies. A pressing need remains to prioritize and characterize candidate genes that underlie the etiology of CD. In this study, we collected a comprehensive multi-dimensional data from GWAS, gene expression, and methylation studies and generated transcriptome-wide association study (TWAS) data to further interpret the GWAS association results. We applied our previously developed method called mega-analysis of Odds Ratio (MegaOR) to prioritize CD candidate genes (CDgenes). As a result, we identified consensus sets of CDgenes (62–235 genes) based on the evidence matrix. We demonstrated that these CDgenes were significantly more frequently interact with each other than randomly expected. Functional annotation of these genes highlighted critical immune-related processes such as immune response, MHC class II receptor activity, and immunological disorders. In particular, the constitutive photomorphogenesis 9 (COP9) signalosome related genes were found to be significantly enriched in CDgenes, implying a potential role of COP9 signalosome involved in the pathogenesis of CD. Finally, we found some of the CDgenes shared biological functions with known drug targets of CD, such as the regulation of inflammatory response and the leukocyte adhesion to vascular endothelial cell. In summary, we identified highly confident CDgenes from multi-dimensional evidence, providing insights for the understanding of CD etiology.

The TLK1-Nek1 axis promotes prostate cancer progression

We recently uncovered the critical TLK1>NEK1>ATR > Chk1 axis in mediating the DDR and cell cycle checkpoint while transiting from Androgen Sensitive to Insensitive growth for LNCaP and TRAMP-C2 cells. However, we did not know the generality of this pathway in PCa progression since there are few cell lines where the transition has been studied. Furthermore, the identification of Nek1, and more importantly the TLK-mediated phosphorylation of T141, has never been studied in PCa biopsies. We now report the first study of a PCa TMA of p-Nek1-T141 and correlation to the Gleason score. In addition we found that TRAMP mice treated with the TLK inhibitor, thioridazine (THD), following castration did not recover cancerous growth of their prostates. Moreover, we recapitulated the process of translational increase in TLK1B expression in a naïve PDX model that was established from an AR + adenocarcinoma. Therefore, we believe that this TLK1-Nek1 mediated DDR axis is likely to be a common adaptive response during the transition of PCa cells toward androgen-insensitive growth, and hence CRPC progression, which has the potential to be targeted with THD and other TLK or Nek1 inhibitors.

Targeting the TLK1/NEK1 DDR axis with Thioridazine suppresses outgrowth of androgen independent prostate tumors

Standard therapy for advanced Prostate Cancer (PCa) consists of antiandrogens, which provide respite from disease progression, but ultimately fail resulting in the incurable phase of the disease: mCRPC. Targeting PCa cells before their progression to mCRPC would greatly improve the outcome. Combination therapy targeting the DNA Damage Response (DDR) has been limited by general toxicity, and a goal of clinical trials is how to target the DDR more specifically. We now show that androgen deprivation therapy (ADT) of LNCaP cells results in increased expression of TLK1B, a key kinase upstream of NEK1 and ATR and mediating the DDR that typically results in a temporary cell cycle arrest of androgen responsive PCa cells. Following DNA damage, addition of the TLK specific inhibitor, thioridazine (THD), impairs ATR and Chk1 activation, establishing the existence of a ADT > TLK1 > NEK1 > ATR > Chk1, DDR pathway, while its abrogation leads to apoptosis. Treatment with THD suppressed the outgrowth of androgen‐independent (AI) colonies of LNCaP and TRAMP‐C2 cells cultured with bicalutamide. Moreover, THD significantly inhibited the growth of several PCa cells in vitro (including AI lines). Administration of THD or bicalutamide was not effective at inhibiting long‐term tumor growth of LNCaP xenografts. In contrast, combination therapy remarkably inhibited tumor growth via bypass of the DDR. Moreover, xenografts of LNCaP cells overexpressing a NEK1‐T141A mutant were durably suppressed with bicalutamide. Collectively, these results suggest that targeting the TLK1/NEK1 axis might be a novel therapy for PCa in combination with standard of care (ADT).

What's new?

Standard therapy for advanced Prostate Cancer (PCa) consists of anti‐androgens, which only provide temporary respite from disease progression to metastatic castrate‐resistant prostate cancer (mCRPC). Here, the authors show in the LNCaP cell model that the increased expression with ADT of TLK1B, a prosurvival checkpoint pathway that is enacted before conversion to androgen‐independent growth, offers a unique target for attacking more specifically PCa cells before their conversion to CRPC. Moreover, they suggest to re‐purpose thioridazine or other phenothiazine antipsychotic drugs as inhibitors of the TLK1 > Nek1 > ATR > Chk1 DNA Damage Response (DDR) axis for the early treatment of advanced PCa still responsive to ADT.

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma

The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.

Prediction of microRNA-disease associations based on distance correlation set

BACKGROUND

Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited.

RESULTS

In this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies.

CONCLUSIONS

According to the simulation results, DCSMDA can be a great addition to the biomedical research field.

Pan-organ transcriptome variation across 21 cancer types

It is widely accepted that some messenger RNAs are evolutionarily conserved across species, both in sequence and tissue-expression specificity. To date, however, little effort has been made to exploit the transcriptome divergence between cancer and adjacent normal tissue at the pan-organ level. In this work, a transcriptome sequencing dataset from 675 normal-tumor pairs, representing 21 solid organs in The Cancer Genome Atlas, is used to evaluate expression evolution. The results show that in most cancer types, gene expression divergence and organ-specificity are reduced in cancer tissue compared to adjacent normal tissue. Furthermore, we observe that all cancers share cell cycle dysregulation through interrogating differentially expressed protein coding genes. Meanwhile, weighted correlation network analysis is used to detect of the gene module structure variation between cancer and adjacent normal tissue. And modules consisting of tightly co-regulated genes in cancer change substantially compared with those in adjacent normal tissue. We thus assume that the destruction of a coordinated regulatory network might result in tumorigenesis and tumor progression. Our results provide new insights into the complex cancer biology and shed light on the mysterious regulation mode for cancer.

Diagnostic MicroRNA Biomarker Discovery for Non-Small-Cell Lung Cancer Adenocarcinoma by Integrative Bioinformatics Analysis

Lung cancer is the leading cause of cancer death and its incidence is ranked high in men and women worldwide. Non-small-cell lung cancer (NSCLC) adenocarcinoma is one of the most frequent histological subtypes of lung cancer. The aberration profile and the molecular mechanism driving its progression are the key for precision therapy of lung cancer, while the screening of biomarkers is essential to the precision early diagnosis and treatment of the cancer. In this work, we applied a bioinformatics method to analyze the dysregulated interaction network of microRNA-mRNA in NSCLC, based on both the gene expression data and the microRNA-gene regulation network. Considering the properties of the substructure and their biological functions, we identified the putative diagnostic biomarker microRNAs, some of which have been reported on the PubMed citations while the rest, that is, miR-204-5p, miR-567, miR-454-3p, miR-338-3p, and miR-139-5p, were predicted as the putative novel microRNA biomarker for the diagnosis of NSCLC adenocarcinoma. They were further validated by functional enrichment analysis of their target genes. These findings deserve further experimental validations for future clinical application.

Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

Mining kidney toxicogenomic data by using gene co-expression modules

Background

Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.

Results

We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.

Conclusions

Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.

Screening key microRNAs for castration-resistant prostate cancer based on miRNA/mRNA functional synergistic network

High-throughput methods have been used to explore the mechanisms by which androgen-sensitive prostate cancer (ASPC) develops into castration-resistant prostate cancer (CRPC). However, it is difficult to interpret cryptic results by routine experimental methods. In this study, we performed systematic and integrative analysis to detect key miRNAs that contribute to CRPC development. From three DNA microarray datasets, we retrieved 11 outlier microRNAs (miRNAs) that had expression discrepancies between ASPC and CRPC using a specific algorithm. Two of the miRNAs (miR-125b and miR-124) have previously been shown to be related to CRPC. Seven out of the other nine miRNAs were confirmed by quantitative PCR (Q-PCR) analysis. MiR-210, miR-218, miR-346, miR-197, and miR-149 were found to be over-expressed, while miR-122, miR-145, and let-7b were under-expressed in CRPC cell lines. GO and KEGG pathway analyses revealed that miR-218, miR-197, miR-145, miR-122, and let-7b, along with their target genes, were found to be involved in the PI3K and AKT3 signaling network, which is known to contribute to CRPC development. We then chose five miRNAs to verify the accuracy of the analysis. The target genes of each miRNA were altered significantly upon transfection of specific miRNA mimics in the C4–2 CRPC cell line, which was consistent with our pathway analysis results. Finally, we hypothesized that miR-218, miR-145, miR-197, miR-149, miR-122, and let-7b may contribute to the development of CRPC through the influence of Ras, Rho proteins, and the SCF complex. Further investigation is needed to verify the functions of the identified novel pathways in CRPC development.

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

Network analysis of an in vitro model of androgen-resistance in prostate cancer

BACKGROUND

The development of androgen resistance is a major limitation to androgen deprivation treatment in prostate cancer. We have developed an in vitro model of androgen-resistance to characterise molecular changes occurring as androgen resistance evolves over time. Our aim is to understand biological network profiles of transcriptomic changes occurring during the transition to androgen-resistance and to validate these changes between our in vitro model and clinical datasets (paired samples before and after androgen-deprivation therapy of patients with advanced prostate cancer).

METHODS

We established an androgen-independent subline from LNCaP cells by prolonged exposure to androgen-deprivation. We examined phenotypic profiles and performed RNA-sequencing. The reads generated were compared to human clinical samples and were analysed using differential expression, pathway analysis and protein-protein interaction networks.

RESULTS

After 24 weeks of androgen-deprivation, LNCaP cells had increased proliferative and invasive behaviour compared to parental LNCaP, and its growth was no longer responsive to androgen. We identified key genes and pathways that overlap between our cell line and clinical RNA sequencing datasets and analysed the overlapping protein-protein interaction network that shared the same pattern of behaviour in both datasets. Mechanisms bypassing androgen receptor signalling pathways are significantly enriched. Several steroid hormone receptors are differentially expressed in both datasets. In particular, the progesterone receptor is significantly differentially expressed and is part of the interaction network disrupted in both datasets. Other signalling pathways commonly altered in prostate cancer, MAPK and PI3K-Akt pathways, are significantly enriched in both datasets.

CONCLUSIONS

The overlap between the human and cell-line differential expression profiles and protein networks was statistically significant showing that the cell-line model reproduces molecular patterns observed in clinical castrate resistant prostate cancer samples, making this cell line a useful tool in understanding castrate resistant prostate cancer. Pathway analysis revealed similar patterns of enriched pathways from differentially expressed genes of both human clinical and cell line datasets. Our analysis revealed several potential mechanisms and network interactions, including cooperative behaviours of other nuclear receptors, in particular the subfamily of steroid hormone receptors such as PGR and alteration to gene expression in both the MAPK and PI3K-Akt signalling pathways.

NFATc1 promotes prostate tumorigenesis and overcomes PTEN loss-induced senescence

Despite recent insights into prostate cancer (PCa)-associated genetic changes, full understanding of prostate tumorigenesis remains elusive due to complexity of interactions among various cell types and soluble factors present in prostate tissue. We found upregulation of Nuclear Factor of Activated T Cells c1 (NFATc1) in human PCa and cultured PCa cells, but not in normal prostates and non-tumorigenic prostate cells. To understand the role of NFATc1 in prostate tumorigenesis in situ, we temporally and spatially controlled the activation of NFATc1 in mouse prostate and showed that such activation resulted in prostatic adenocarcinoma with features similar to those seen in human PCa. Our results indicate that the activation of a single transcription factor, NFATc1 in prostatic luminal epithelium to PCa can affect expression of diverse factors in both cells harboring the genetic changes and in neighboring cells through microenvironmental alterations. In addition to the activation of oncogenes c-MYC and STAT3 in tumor cells, a number of cytokines and growth factors, such as IL1β, IL6, and SPP1 (Osteopontin, a key biomarker for PCa), were upregulated in NFATc1-induced PCa, establishing a tumorigenic microenvironment involving both NFATc1 positive and negative cells for prostate tumorigenesis. To further characterize interactions between genes involved in prostate tumorigenesis, we generated mice with both NFATc1 activation and Pten inactivation in prostate. We showed that NFATc1 activation led to acceleration of Pten-null–driven prostate tumorigenesis by overcoming the PTEN loss–induced cellular senescence through inhibition of p21 activation. This study provides direct in vivo evidence of an oncogenic role of NFATc1 in prostate tumorigenesis and reveals multiple functions of NFATc1 in activating oncogenes, in inducing proinflammatory cytokines, in oncogene addiction, and in overcoming cellular senescence, which suggests calcineurin-NFAT signaling as a potential target in preventing PCa.

Identification of novel therapeutic target genes in acquired lapatinib-resistant breast cancer by integrative meta-analysis

Acquired resistance to lapatinib is a highly problematic clinical barrier that has to be overcome for a successful cancer treatment. Despite efforts to determine the mechanisms underlying acquired lapatinib resistance (ALR), no definitive genetic factors have been reported to be solely responsible for the acquired resistance in breast cancer. Therefore, we performed a cross-platform meta-analysis of three publically available microarray datasets related to breast cancer with ALR, using the R-based RankProd package. From the meta-analysis, we were able to identify a total of 990 differentially expressed genes (DEGs, 406 upregulated, 584 downregulated) that are potentially associated with ALR. Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs showed that "response to organic substance" and "p53 signaling pathway" may be largely involved in ALR process. Of these, many of the top 50 upregulated and downregulated DEGs were found in oncogenesis of various tumors and cancers. For the top 50 DEGs, we constructed the gene coexpression and protein-protein interaction networks from a huge database of well-known molecular interactions. By integrative analysis of two systemic networks, we condensed the total number of DEGs to six common genes (LGALS1, PRSS23, PTRF, FHL2, TOB1, and SOCS2). Furthermore, these genes were confirmed in functional module eigens obtained from the weighted gene correlation network analysis of total DEGs in the microarray datasets ("GSE16179" and "GSE52707"). Our integrative meta-analysis could provide a comprehensive perspective into complex mechanisms underlying ALR in breast cancer and a theoretical support for further chemotherapeutic studies.