Identification of androgen response-related lncRNAs in prostate cancer.

BACKGROUND: Long noncoding RNAs (lncRNAs) are RNA molecules with over 200 nucleotides that do not code for proteins, but are known to be widely expressed and have key roles in gene regulation and cellular functions. They are also found to be involved in the onset and development of various cancers, including prostate cancer (PCa). Since PCa are commonly driven by androgen regulated signaling, mainly stimulated pathways, identification and determining the influence of lncRNAs in androgen response is useful and necessary. LncRNAs regulated by the androgen receptor (AR) can serve as potential biomarkers for PCa. In the present study, gene expression data analysis were performed to distinguish lncRNAs related to the androgen response pathway. METHODS AND RESULTS: We used publicly available RNA-sequencing and ChIP-seq data to identify lncRNAs that are associated with the androgen response pathway. Using Universal Correlation Coefficient (UCC) and Pearson Correlation Coefficient (PCC) analyses, we found 15 lncRNAs that have (a) highly correlated expression with androgen response genes in PCa and are (b) differentially expressed in the setting of treatment with an androgen agonist as well as antagonist compared to controls. Using publicly available ChIP-seq data, we investigated the role of androgen/AR axis in regulating expression of these lncRNAs. We observed AR binding in the promoter regions of 5 lncRNAs (MIR99AHG, DUBR, DRAIC, PVT1, and COLCA1), showing the direct influence of AR on their expression and highlighting their association with the androgen response pathway. CONCLUSION: By utilizing publicly available multiomics data and by employing in silico methods, we identified five candidate lncRNAs that are involved in the androgen response pathway. These lncRNAs should be investigated as potential biomarkers for PCa.

A hybrid breast cancer/mesenchymal stem cell population enhances chemoresistance and metastasis.

Patients with triple-negative breast cancer remain at risk for metastatic disease despite treatment. The acquisition of chemoresistance is a major cause of tumor relapse and death, but the mechanisms are far from understood. We have demonstrated that breast cancer cells (BCCs) can engulf mesenchymal stem/stromal cells (MSCs), leading to enhanced dissemination. Here, we show that clinical samples of primary invasive carcinoma and chemoresistant breast cancer metastasis contain a unique hybrid cancer cell population coexpressing pancytokeratin and the MSC marker fibroblast activation protein-α. We show that hybrid cells form in primary tumors and that they promote breast cancer metastasis and chemoresistance. Using single-cell microfluidics and in vivo models, we found that there are polyploid senescent cells within the hybrid cell population that contribute to metastatic dissemination. Our data reveal that Wnt Family Member 5A (WNT5A) plays a crucial role in supporting the chemoresistance properties of hybrid cells. Furthermore, we identified that WNT5A mediates hybrid cell formation through a phagocytosis-like mechanism that requires BCC-derived IL-6 and MSC-derived C-C Motif Chemokine Ligand 2. These findings reveal hybrid cell formation as a mechanism of chemoresistance and suggest that interrupting this mechanism may be a strategy in overcoming breast cancer drug resistance.

UALCAN: An update to the integrated cancer data analysis platform.

Cancer genomic, transcriptomic, and proteomic profiling has generated extensive data that necessitate the development of tools for its analysis and dissemination. We developed UALCAN to provide a portal for easy exploring, analyzing, and visualizing these data, allowing users to integrate the data to better understand the gene, proteins, and pathways perturbed in cancer and make discoveries. UALCAN web portal enables analyzing and delivering cancer transcriptome, proteomics, and patient survival data to the cancer research community. With data obtained from The Cancer Genome Atlas (TCGA) project, UALCAN has enabled users to evaluate protein-coding gene expression and its impact on patient survival across 33 types of cancers. The web portal has been used extensively since its release and received immense popularity, underlined by its usage from cancer researchers in more than 100 countries. The present manuscript highlights the task we have undertaken and updates that we have made to UALCAN since its release in 2017. Extensive user feedback motivated us to expand the resource by including data on a) microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and promoter DNA methylation from TCGA and b) mass spectrometry-based proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). UALCAN provides easy access to pre-computed, tumor subgroup-based gene/protein expression, promoter DNA methylation status, and Kaplan-Meier survival analyses. It also provides new visualization features to comprehend and integrate observations and aids in generating hypotheses for testing. UALCAN is accessible at http://ualcan.path.uab.edu.

Loss of cellular identity in common pre-clinical models of serine­threonine kinase 11 (Liver kinase B1) loss.

Nearly 1/3 of lung adenocarcinomas have loss of STK11 (LKB1) function. Herein, a bioinformatics approach was used to determine how accurately preclinical model systems reflect the in vivo biology of STK11 loss in human patients. Hierarchical and K-mean clustering, principle component, and gene set enrichment analyses were employed to model gene expression due to STK11 loss in patient cohorts representing nearly 1000 lung adenocarcinoma patients. K-means clustering classified STK11 loss patient tumors into three distinct sub-groups; positive (54%), neuroendocrine (NE) (35%) and negative (11%). The positive and NE groups are both defined by the expression of NKX2-1. In addition to NKX2-1, NE patients express neuroendocrine markers such as ASCL1 and CALCA. In contrast, the negative group does not express NKX2-1 (or neuroendocrine markers) and is characterized by significantly reduced survival relative to the two other groups. Two gene expression signatures were derived to explain both neuroendocrine features and differentiation (NKX2-1 loss) and were validated through two public datasets involving chemical differentiation (DCI) and NKX2-1 reconstitution. Patients results were then compared with established cell lines, transgenic mice, and patient-derived xenograft models of STK11 loss. Interestingly, all cell line and PDX models cluster and show expression patterns similar with the NKX2-1 negative subset of STK11-loss human tumors. Surprisingly, even mouse models of STK11 loss do not resemble patient tumors based on gene expression patterns. Results suggest pre-clinical models of STK11 loss are pronounced by marked elimination of type II pneumocyte identity, opposite of most in vivo human tumors.