Differentially Expressed Genes and Molecular Pathways in an Autochthonous Mouse Prostate Cancer Model

Abstract

Prostate cancer remains a major public health problem and the second leading cause of cancer-related deaths in men in the United States. The present study aims to understand the molecular pathway(s) of prostate cancer which is essential for early detection and treatment. Dorsolateral prostate from 20 week transgenic adenocarcinoma of the mouse prostate (TRAMP) mice, which spontaneously develops prostate cancer and recapitulates human disease and age-matched non-transgenic littermates were utilized for microarray analysis. Mouse genome network and pathway analyses were mapped to the human genome using the Ingenuity Pathway Analysis (IPA) database for annotation, visualization, and integrated discovery. In total, 136 differentially expressed genes, including 32 downregulated genes and 104 upregulated genes were identified in the dorsolateral prostate of TRAMP, compared to non-transgenic mice. A subset of differentially expressed genes were validated by qRT-PCR. Alignment with human genome database identified 18 different classes of proteins, among these, 36% were connected to the nucleic acid binding, including ribosomal proteins, which play important role in protein synthesis—the most enriched pathway in the development of prostate cancer. Furthermore, the results suggest deregulation of signaling molecules (9%) and enzyme modulators (8%) affect various pathways. An imbalance in other protein classes, including transporter proteins (7%), hydrolases (6%), oxidoreductases, and cytoskeleton proteins (5%), contribute to cancer progression. Our study evaluated the underlying pathways and its connection to human prostate cancer, which may further help assess the risk of disease development and progression and identify potential targets for therapeutic intervention.

Prostate cancer resistance leads to a global deregulation of translation factors and unconventional translation of long non-coding RNAs

“…This is consistent with previous data reporting clinical therapeutics targeting mitochondrial processes such as Gossypol, the G3139 anti-sense oligonucleotide and 2-deoxy-d-glucose (Hsu et al 2016;Yang et al 2016;Kim et al 2017;D'Souza and Minczuk 2018;Fulda et al 2010;Zhang et al 2011). Our model highlights the dysregulation of certain ribosomal genes, for example RPL9 and RPS13, and translation regulators such as EIF4B and EIF4EBP1, which have already been found to be implicated in PCa through the use of varied models (Hsieh et al 2015;Verma et al 2019;Liu et al 2019;Guo et al 2011;Ren et al 2013). Furthermore, the downregulation of genes implicated in translation may suggest that development of drug resistance in PCa could benefit from this reprogramming of translation.…”

Quantitative SWATH-Based Proteomic Profiling for Identification of Mechanism-Driven Diagnostic Biomarkers Conferring in the Progression of Metastatic Prostate Cancer

“…Another study has reported that UBE2N promotes melanoma growth via MEK/FRA1/SOX10 signaling hence proving the role of UBE2N as a potential biomarker for PCa (25). Microarray studies have shown upregulated expression of UBE2N in TRAMP mice when compared to their age-matched non-transgenic littermate (26). Another group has also reported the involvement of UBE2N at protein level in rat spermatogenesis in response to in vivo androgen manipulation (27).…”

Androgen Deprivation Induces Transcriptional Reprogramming in Prostate Cancer Cells to Develop Stem Cell-Like Characteristics

“…The reaction for qRT was setup accordingly; 2.5 µl of SYBR green (Radiant™ SYBR Green low-ROX qPCR, Alkali Scientific, Fort Lauderdale, FL, USA) of 5× sample were added for a total 10 μL volume with thermal cycler program used started at 50 °C for 2 min then proceeded with 95 °C for 10 min for initial denaturing, followed by 40 cycles of 95 °C for 15 s, 60 °C for 40 s, and 72 °C for 35 s to collect cycle threshold ( C t) values, along with dissociation curve cycle. The 2-ΔΔ C t method was used to calculate relative expression of each gene as previously described [ 62 ].…”