Family with sequence similarity 13C (FAM13C) overexpression is an independent prognostic marker in prostate cancer

A genome-wide association study identifies genetic variants associated with hip pain in the UK Biobank cohort (N = 221,127)

Hip pain is a common musculoskeletal complaint that leads many people to seek medical attention. We conducted a primary genome-wide association study (GWAS) on the hip pain phenotype within the UK Biobank cohort. Sex-stratified GWAS analysis approach was also performed to explore sex specific variants associated with hip pain. We found seven different loci associated with hip pain at GWAS significance level, with the most significant single nucleotide polymorphism (SNP) being rs77641763 within the EXD3 (p value = 2.20 × 10-13). We utilized summary statistics from the FinnGen cohort and a previous GWAS meta-analysis on hip osteoarthritis as replication cohorts. Four loci (rs509345, rs73581564, rs9597759, rs2018384) were replicated with a p value less than 0.05. Sex-stratified GWAS analyses revealed a unique locus within the CUL1 gene (rs4726995, p = 2.56 × 10-9) in males, and three unique loci in females: rs1651359966 on chromosome 7 (p = 1.15 × 10-8), rs552965738 on chromosome 9 (p = 2.72 × 10-8), and rs1978969 on chromosome 13 (p = 2.87 × 10-9). This study has identified seven genetic loci associated with hip pain. Sex-stratified analysis also revealed sex specific variants associated with hip pain in males and females. This study has provided a foundation for advancing research of hip pain and hip osteoarthritis.

SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

Murine leukemia virus (MLV) P50 protein induces cell transformation via transcriptional regulatory function

BACKGROUND

The murine leukemia virus (MLV) has been a powerful model of pathogenesis for the discovery of genes involved in cancer. Its splice donor (SD')-associated retroelement (SDARE) is important for infectivity and tumorigenesis, but the mechanism remains poorly characterized. Here, we show for the first time that P50 protein, which is produced from SDARE, acts as an accessory protein that transregulates transcription and induces cell transformation.

RESULTS

By infecting cells with MLV particles containing SDARE transcript alone (lacking genomic RNA), we show that SDARE can spread to neighbouring cells as shown by the presence of P50 in infected cells. Furthermore, a role for P50 in cell transformation was demonstrated by CCK8, TUNEL and anchorage-independent growth assays. We identified the integrase domain of P50 as being responsible for transregulation of the MLV promoter using luciferase assay and RTqPCR with P50 deleted mutants. Transcriptomic analysis furthermore revealed that the expression of hundreds of cellular RNAs involved in cancerogenesis were deregulated in the presence of P50, suggesting that P50 induces carcinogenic processes via its transcriptional regulatory function.

CONCLUSION

We propose a novel SDARE-mediated mode of propagation of the P50 accessory protein in surrounding cells. Moreover, due to its transforming properties, P50 expression could lead to a cellular and tissue microenvironment that is conducive to cancer development.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Investigation of the effects of overexpression of jumping translocation breakpoint (JTB) protein in MCF7 cells for potential use as a biomarker in breast cancer

Jumping translocation breakpoint (JTB) gene acts as a tumor suppressor or an oncogene in different malignancies, including breast cancer (BC), where it was reported as overexpressed. However, the molecular functions, biological processes and underlying mechanisms through which JTB protein causes increased cell growth, proliferation and invasion is still not fully deciphered. Our goal is to identify the functions of JTB protein by cellular proteomics approaches. MCF7 breast cancer cells were transfected with sense orientation of hJTB cDNA in HA, His and FLAG tagged CMV expression vector to overexpress hJTB and the expression levels were confirmed by Western blotting (WB). Proteins extracted from transfected cells were separated by SDS-PAGE and the in-gel digested peptides were analyzed by nano-liquid chromatography tandem mass spectrometry (nanoLC-MS/MS). By comparing the proteome of cells with upregulated conditions of JTB vs control and identifying the protein dysregulation patterns, we aim to understand the function of this protein and its contribution to tumorigenesis. Gene Set Enrichment Analysis (GSEA) algorithm was performed to investigate the biological processes and pathways that are associated with the JTB protein upregulation. The results demonstrated four significantly enriched gene sets from the following significantly upregulated pathways: mitotic spindle assembly, estrogen response late, epithelial-to-mesenchymal transition (EMT) and estrogen response early. JTB protein itself is involved in mitotic spindle pathway by its role in cell division/cytokinesis, and within estrogen response early and late pathways, contributing to discrimination between luminal and mesenchymal breast cancer. Thus, the overexpressed JTB condition was significantly associated with an increased expression of ACTNs, FLNA, FLNB, EZR, MYOF, COL3A1, COL11A1, HSPA1A, HSP90A, WDR, EPPK1, FASN and FOXA1 proteins related to deregulation of cytoskeletal organization and biogenesis, mitotic spindle organization, ECM remodeling, cellular response to estrogen, proliferation, migration, metastasis, increased lipid biogenesis, endocrine therapy resistance, antiapoptosis and discrimination between different breast cancer subtypes. Other upregulated proteins for overexpressed JTB condition are involved in multiple cellular functions and pathways that become dysregulated, such as tumor microenvironment (TME) acidification, the transmembrane transport pathways, glycolytic flux, iron metabolism and oxidative stress, metabolic reprogramming, nucleocytosolic mRNA transport, transcriptional activation, chromatin remodeling, modulation of cell death pathways, stress responsive pathways, and cancer drug resistance. The downregulated proteins for overexpressed JTB condition are involved in adaptive communication between external and internal environment of cells and maintenance between pro-apoptotic and anti-apoptotic signaling pathways, vesicle trafficking and secretion, DNA lesions repair and suppression of genes involved in tumor progression, proteostasis, redox state regulation, biosynthesis of macromolecules, lipolytic pathway, carbohydrate metabolism, dysregulation of ubiquitin-mediated degradation system, cancer cell immune escape, cell-to-cell and cell-to-ECM interactions, and cytoskeletal behaviour. There were no significantly enriched downregulated pathways.

A Ten-N6-Methyladenosine (m6A)-Modified Gene Signature Based on a Risk Score System Predicts Patient Prognosis in Rectum Adenocarcinoma

OBJECTIVES

The study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.

MATERIALS AND METHODS

RNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N6-methyladenosine (m6A)-modified genes in READ were downloaded from the "m6Avar" database. Differentially expressed m6A-modified genes in READ stratified by different clinicopathological characteristics were identified using the "limma" package in R. Protein-protein interaction (PPI) network and co-expression analysis of differentially expressed genes (DEGs) were performed using "STRING" and Cytoscape, respectively. Principal component analysis (PCA) was done using R. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to functionally annotate the differentially expressed genes in different subgroups. Univariate Cox regression analyses were conducted to identify the powerful independent prognostic factors in READ associated with overall survival (OS). A robust likelihood-based survival model was built using the "rbsurv" package to screen for survival-associated signature genes. The Support Vector Machine (SVM) was used to predict the prognosis of READ through the risk score of survival-associated signature genes. Correlation analysis were carried out using GraphPad prism 8.

RESULTS

We screened 974 differentially expressed m6A-modified genes among four types of READ samples. Two READ subgroups (group 1 and group 2) were identified by K means clustering according to the expression of DEGs. The two subgroups were significantly different in overall survival and pathological stages. Next, 118 differentially expressed genes between the two subgroups were screened and the expression of 112 genes was found to be related to the prognosis of READ. Next, a panel of 10 survival-associated signature genes including adamtsl1, csmd2, fam13c, fam184a, klhl4, olfml2b, pdzd4, sec14l5, setbp1, tmem132b was constructed. The signature performed very well for prognosis prediction, time-dependent receiver-operating characteristic (ROC) analysis displaying an area under the curve (AUC) of 0.863, 0.8721, and 0.8752 for 3-year survival rate, prognostic status, and pathological stage prediction, respectively. Correlation analysis showed that the expression levels of the 10 m6A-modified genes were positively correlated with that of m6A demethylase FTO and ALKBH5.

CONCLUSION

This study identified potential m6A-modified genes that may be involved in the pathophysiology of READ and constructed a novel gene expression panel for READ risk stratification and prognosis prediction.

Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline

PURPOSE

This guideline provides recommendations for available tissue-based prostate cancer biomarkers geared toward patient selection for active surveillance, identification of clinically significant disease, choice of postprostatectomy adjuvant versus salvage radiotherapy, and to address emerging questions such as the relative value of tissue biomarkers compared with magnetic resonance imaging.

METHODS

An ASCO multidisciplinary Expert Panel, with representatives from the European Association of Urology, American Urological Association, and the College of American Pathologists, conducted a systematic literature review of localized prostate cancer biomarker studies between January 2013 and January 2019. Numerous tissue-based molecular biomarkers were evaluated for their prognostic capabilities and potential for improving management decisions. Here, the Panel makes recommendations regarding the clinical use and indications of these biomarkers.

RESULTS

Of 555 studies identified, 77 were selected for inclusion plus 32 additional references selected by the Expert Panel. Few biomarkers had rigorous testing involving multiple cohorts and only 5 of these tests are commercially available currently: Oncotype Dx Prostate, Prolaris, Decipher, Decipher PORTOS, and ProMark. With various degrees of value and validation, multiple biomarkers have been shown to refine risk stratification and can be considered for select men to improve management decisions. There is a paucity of prospective studies assessing short- and long-term outcomes of patients when these markers are integrated into clinical decision making.

RECOMMENDATIONS

Tissue-based molecular biomarkers (evaluating the sample with the highest volume of the highest Gleason pattern) may improve risk stratification when added to standard clinical parameters, but the Expert Panel endorses their use only in situations in which the assay results, when considered as a whole with routine clinical factors, are likely to affect a clinical decision. These assays are not recommended for routine use as they have not been prospectively tested or shown to improve long-term outcomes-for example, quality of life, need for treatment, or survival. Additional information is available at www.asco.org/genitourinary-cancer-guidelines.