Gene expression changes during the development of estrogen-independent and antiestrogen-resistant growth in breast cancer cell culture models

Long noncoding RNA MALAT-1: A versatile regulator in cancer progression, metastasis, immunity, and therapeutic resistance

Long noncoding RNAs (lncRNAs) are RNA transcripts longer than 200 nucleotides that do not code for proteins but have been linked to cancer development and metastasis. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1) influences crucial cancer hallmarks through intricate molecular mechanisms, including proliferation, invasion, angiogenesis, apoptosis, and the epithelial-mesenchymal transition (EMT). The current article highlights the involvement of MALAT-1 in drug resistance, making it a potential target to overcome chemotherapy refractoriness. It discusses the impact of MALAT-1 on immunomodulatory molecules, such as major histocompatibility complex (MHC) proteins and PD-L1, leading to immune evasion and hindering anti-tumor immune responses. MALAT-1 also plays a significant role in cancer immunology by regulating diverse immune cell populations. In summary, MALAT-1 is a versatile cancer regulator, influencing tumorigenesis, chemoresistance, and immunotherapy responses. Understanding its precise molecular mechanisms is crucial for developing targeted therapies, and therapeutic strategies targeting MALAT-1 show promise for improving cancer treatment outcomes. However, further research is needed to fully uncover the role of MALAT-1 in cancer biology and translate these findings into clinical applications.

LncRNA-MALAT1 Regulates Cancer Glucose Metabolism in Prostate Cancer via MYBL2/mTOR Axis

It is known that the long noncoding RNAs (lncRNA) MALAT1 is associated with tumorigenesis and progression in various cancers; however, its functions and mechanisms in prostate cancer (PCa) initiation and progression are still unknown. In the present study, our findings revealed that MALAT1 plays a critical part in regulating PCa proliferation and glucose metabolism. Knockdown of MALAT1 affects the protein and mRNA levels of MYBL2. In addition, MALAT1 enhances the phosphorylation level of mTOR pathway by upregulating MYBL2. Knockdown of MALAT1 or MYBL2 in PCa cell lines significantly inhibits their proliferation capacity. Silencing MALAT1/MYBL2/mTOR axis in PCa cell lines affects their glycolysis and lactate levels, and we verified these findings in mice. Furthermore, we explored the underlying tumorigenesis functions of MYBL2 in PCa and found that high expression of MYBL2 was positively associated with TNM stage, Gleason score, PSA level, and poor survival rate in PCa patients. Taken together, our research suggests that MALAT1 controls cancer glucose metabolism and progression by upregulating MYBL2-mTOR axis.

Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), a high mortality malignancy, has become a worldwide public health concern. Acquired resistance to the multikinase inhibitor sorafenib challenges its clinical efficacy and the survival benefits it provides to patients with advanced HCC. This study aimed to identify critical genes and pathways associated with sorafenib resistance in HCC using integrated bioinformatics analysis. Differentially expressed genes (DEGs) were identified using four HCC gene expression profiles (including 34 sorafenib-resistant and 29 sorafenib-sensitive samples) based on the robust rank aggregation method and R software. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and small molecules reversing sorafenib resistance were searched for using the connectivity map (CMAP) database. Pearson correlation and survival analyses of hub genes were performed using cBioPortal and Gene Expression Profiling and Interactive Analysis (GEPIA). Finally, the expression levels of hub genes in sorafenib-resistant HCC cells were verified using quantitative polymerase chain reaction (q-PCR). A total of 165 integrated DEGs (66 upregulated and 99 downregulated in sorafenib resistant samples compared sorafenib sensitive ones) primarily enriched in negative regulation of endopeptidase activity, extracellular exosome, and protease binding were identified. Some pathways were commonly shared between the integrated DEGs. Seven promising therapeutic agents and 13 hub genes were identified. These findings provide a strategy and theoretical basis for overcoming sorafenib resistance in HCC patients.

MYBL2 disrupts the Hippo-YAP pathway and confers castration resistance and metastatic potential in prostate cancer

Rationale: Resistance to androgen-deprivation therapy (ADT) associated with metastatic progression remains a challenging clinical task in prostate cancer (PCa) treatment. Current targeted therapies for castration-resistant prostate cancer (CRPC) are not durable. The exact molecular mechanisms mediating resistance to castration therapy that lead to CRPC progression remain obscure. Methods: The expression of MYB proto-oncogene like 2 (MYBL2) was evaluated in PCa samples. The effect of MYBL2 on the response to ADT was determined by in vitro and in vivo experiments. The survival of patients with PCa was analyzed using clinical specimens (n = 132) and data from The Cancer Genome Atlas (n = 450). The mechanistic model of MYBL2 in regulating gene expression was further detected by subcellular fractionation, western blotting, quantitative real-time PCR, chromatin immunoprecipitation, and luciferase reporter assays. Results: MYBL2 expression was significantly upregulated in CRPC tissues and cell lines. Overexpression of MYBL2 could facilitate castration-resistant growth and metastatic capacity in androgen-dependent PCa cells by promoting YAP1 transcriptional activity via modulating the activity of the Rho GTPases RhoA and LATS1 kinase. Importantly, targeting MYBL2, or treatment with either the YAP/TAZ inhibitor Verteporfin or the RhoA inhibitor Simvastatin, reversed the resistance to ADT and blocked bone metastasis in CRPC cells. Finally, high MYBL2 levels were positively associated with TNM stage, total PSA level, and Gleason score and predicted a higher risk of metastatic relapse and poor prognosis in patients with PCa. Conclusions: Our results reveal a novel molecular mechanism conferring resistance to ADT and provide a strong rationale for potential therapeutic strategies against CRPC.

Whole transcriptome analysis of the ERα synthetic fragment P295-T311 (ERα17p) identifies specific ERα-isoform (ERα, ERα36)-dependent and -independent actions in breast cancer cells

ERα17p is a peptide corresponding to the sequence P295LMIKRSKKNSLALSLT311 of the estrogen receptor alpha (ERα) and initially found to interfere with ERα-related calmodulin binding. ERα17p was subsequently found to elicit estrogenic responses in E2-deprived ERα-positive breast cancer cells, increasing proliferation and ERE-dependent gene transcription. Surprisingly, in E2-supplemented media, ERα17p-induced apoptosis and modified the actin network, influencing cell motility. Here, we report that ERα17p internalizes in breast cancer cells (T47D, MDA-MB-231, SKBR3) and induces a massive early (3 h) transcriptional activity. Remarkably, about 75% of significantly modified transcripts were also modified by E2, confirming the pro-estrogenic profile of ERα17p. The different ER spectra of the used cell lines allowed us to identify a specific ERα17p signature related to ERα as well as its variant ERα36. With respect to ERα, the peptide activates nuclear (cell cycle, cell proliferation, nucleic acid and protein synthesis) and extranuclear signaling pathways. In contrast, through ERα36, it mainly triggers inhibitory actions on inflammation. This is the first work reporting a detailed ERα36-specific transcriptional signature. In addition, we report that ERα17p-induced transcripts related to apoptosis and actin modifying effects of the peptide are independent from its estrogen receptor(s)-related actions. We discuss our findings in view of the potential use of ERα17p as a selective peptidomimetic estrogen receptor modulator (PERM).

Inhibition of FOSL1 overexpression in antiestrogen-resistant MCF-7 cells decreases cell growth and increases vacuolization and cell death

Elevated activator protein-1 (AP-1) activity in breast cancer cells has been linked to Tamoxifen (TAM) resistance. Fos-like antigen-1 (FOSL1) is a member of the AP-1 transcription factor and is overexpressed in a variety of human cancers including breast tumors. We have previously established an estrogen-independent and antiestrogen Toremifene (TOR)-resistant subline of MCF-7 breast cancer cells. In these cells, the expression of FOSL1 is upregulated when compared to the parental cells. In the present study, partial inhibition of FOSL1 expression in these cells by small interfering RNA resulted in a marked decrease of cell growth. The inhibition of cell growth paralleled with changes in cell morphology such as increased formation of vacuoles followed by an increase in the number of dead cells. The inhibition of FOSL1 expression in these cells also restored sensitivity to TOR. Our results suggest that chemotherapy targeting overexpression of FOSL1 could be a potent strategy for treating endocrine resistant breast cancers.

Conserved and differential gene interactions in dynamical biological systems

MOTIVATION

While biological systems operated from a common genome can be conserved in various ways, they can also manifest highly diverse dynamics and functions. This is because the same set of genes can interact differentially across specific molecular contexts. For example, differential gene interactions give rise to various stages of morphogenesis during cerebellar development. However, after over a decade of efforts toward reverse engineering biological networks from high-throughput omic data, gene networks of most organisms remain sketchy. This hindrance has motivated us to develop comparative modeling to highlight conserved and differential gene interactions across experimental conditions, without reconstructing complete gene networks first.

RESULTS

We established a comparative dynamical system modeling (CDSM) approach to identify conserved and differential interactions across molecular contexts. In CDSM, interactions are represented by ordinary differential equations and compared across conditions through statistical heterogeneity and homogeneity tests. CDSM demonstrated a consistent superiority over differential correlation and reconstruct-then-compare in simulation studies. We exploited CDSM to elucidate gene interactions important for cellular processes poorly understood during mouse cerebellar development. We generated hypotheses on 66 differential genetic interactions involved in expansion of the external granule layer. These interactions are implicated in cell cycle, differentiation, apoptosis and morphogenesis. Additional 1639 differential interactions among gene clusters were also identified when we compared gene interactions during the presence of Rhombic lip versus the presence of distinct internal granule layer. Moreover, compared with differential correlation and reconstruct-then-compare, CDSM makes fewer assumptions on data and thus is applicable to a wider range of biological assays.

AVAILABILITY

Source code in C++ and R is available for non-commercial organizations upon request from the corresponding author. The cerebellum gene expression dataset used in this article is available upon request from the Goldowitz lab (dang@cmmt.ubc.ca, http://grits.dglab.org/).

CONTACT

joemsong@cs.nmsu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The role of the basal stem cell of the human breast in normal development and cancer

MCF-10F, an ERα negative human breast epithelial cell line derived from normal breast tissue, is able to form ductal structures in a tridimensional collagen matrix system. MCF-10F cells that are estrogen transformed (trMCF cells) progressively express phenotypes of in vitro cell transformation, including colony formation in agar methocel and loss of the ductulogenic capacity. Selection of these trMCF cells for invasiveness identified cells (bcMCF) that formed tumors in severe combined immunodeficient mice. The cell lines derived from those tumors (caMCF) were poorly differentiated ER, PR, and ERBB2 negative adenocarcinomas. These characteristics are similar to the human basal cell-like carcinomas. This in vitro-in vivo model demonstrates the importance of the basal cell type as a stem cell that reconstitutes the branching pattern of the breast and that is also target of a carcinogenic insult leading to transformation and cancer.

Soluble L1CAM promotes breast cancer cell adhesion and migration in vitro, but not invasion

Background

Neural recognition molecule L1CAM, which is a key protein involved in early nervous system development, is known to be abnormally expressed and shed in several types of cancers where it participates in metastasis and progression. The distinction of L1CAM presence in cancerous vs. normal tissues has suggested it to be a new target for cancer treatment. Our current study focused on the potential role of soluble L1CAM in breast cancer cell adhesion to extracellular matrix proteins, migration, and invasion.

Results

We found L1 expression levels were correlated with breast cancer stage of progression in established data sets of clinical samples, and also were high in more metastatic breast cancer cell lines MDA-MB-231 and MDA-MB-435, but low in less migratory MDA-MB-468 cells. Proteolysis of L1 into its soluble form (sL1) was detected in cell culture medium from all three above cell lines, and can be induced by PMA activation. Over-expression of the L1 ectodomain in MDA-MB-468 cells by using a lentiviral vector greatly increased the amount of sL1 released by those cells. Concomitantly, cell adhesion to extracellular matrix and cell transmigration ability were significantly promoted, while cell invasion ability through Matrigel™ remained unaffected. On the other hand, attenuating L1 expression in MDA-MB-231 cells by using a shRNA lentiviral vector resulted in reduced cell-matrix adhesion and transmigration. Similar effects were also shown by monoclonal antibody blocking of the L1 extracellular region. Moreover, sL1 in conditioned cell culture medium induced a directional migration of MDA-MB-468 cells, which could be neutralized by antibody treatment.

Conclusions

Our data provides new evidence for the function of L1CAM and its soluble form in promoting cancer cell adhesion to ECM and cell migration. Thus, L1CAM is validated further to be a potential early diagnostic marker in breast cancer progression and a target for breast cancer therapy.

Multilevel support vector regression analysis to identify condition-specific regulatory networks

MOTIVATION

The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification.

RESULTS

We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-specific regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. To evaluate the effectiveness of the proposed strategy, we first compared the ml-SVR approach with other existing methods on simulation data and yeast cell cycle data. The resulting performance shows that the ml-SVR approach outperforms other methods in the identification of both regulators and their target genes. We then applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.

AVAILABILITY AND IMPLEMENTATION

The ml-SVR MATLAB package can be downloaded at http://www.cbil.ece.vt.edu/software.htm.