GLIPR1 and SPARC expression profile reveals a signature associated with prostate Cancer Brain metastasis

Multi-omic profiling reveals an RNA processing rheostat that predisposes to prostate cancer

Prostate cancer is the most commonly diagnosed malignancy and the third leading cause of cancer deaths. GWAS have identified variants associated with prostate cancer susceptibility; however, mechanistic and functional validation of these mutations is lacking. We used CRISPR-Cas9 genome editing to introduce a missense variant identified in the ELAC2 gene, which encodes a dually localised nuclear and mitochondrial RNA processing enzyme, into the mouse Elac2 gene as well as to generate a prostate-specific knockout of Elac2. These mutations caused enlargement and inflammation of the prostate and nodule formation. The Elac2 variant or knockout mice on the background of the transgenic adenocarcinoma of the mouse prostate (TRAMP) model show that Elac2 mutation with a secondary genetic insult exacerbated the onset and progression of prostate cancer. Multiomic profiling revealed defects in energy metabolism that activated proinflammatory and tumorigenic pathways as a consequence of impaired noncoding RNA processing and reduced protein synthesis. Our physiologically relevant models show that the ELAC2 variant is a predisposing factor for prostate cancer and identify changes that underlie the pathogenesis of this cancer.

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942.

GLIPR1 promotes proliferation, metastasis and 5-fluorouracil resistance in hepatocellular carcinoma by activating the PI3K/PDK1/ROCK1 pathway

Hepatocellular carcinoma (HCC) contributes to a heavy disease burden for its high prevalence and poor prognosis, with limited effective systemic therapies available. In the era of precision medicine, treatment efficacy might be improved by combining personalized systemic therapies. Since oncogenic activation is one of the primary driving forces in HCC, characterization of these oncogenes can provide insights for developing new targeted therapies. Based on RNA sequencing of epithelial-mesenchymal transition (EMT)-induced HCC cells, this study discovers and characterizes glioma pathogenesis-related protein 1 (GLIPR1) that robustly drives HCC progression and can potentially serve as a prognostic biomarker and therapeutic target with clinical utility. GLIPR1 serves opposing roles and involves distinct mechanisms in different cancers. However, based on integrated in-silico analysis, in vitro and in vivo functional investigations, we demonstrate that GLIPR1 plays a multi-faceted oncogenic role in HCC development via enhancing tumor proliferation, metastasis, and 5FU resistance. We also found that GLIPR1 induces EMT and is actively involved in the PI3K/PDK1/ROCK1 singling axis to exert its oncogenic effects. Thus, pre-clinical evaluation of GLIPR1 and its downstream factors in HCC patients might facilitate further discovery of therapeutic targets, as well as improve HCC chemotherapeutic outcomes and prognosis.

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Simple Summary

It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options.

Abstract

Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.