Screening and prognostic value of potential biomarkers for ovarian cancer

Developments in Genetics: Better Management of Ovarian Cancer Patients

The purpose of this article is to highlight the new advancements in molecular and diagnostic genetic testing and to properly classify all ovarian cancers. In this article, we address statistics, histopathological classification, molecular pathways implicated in ovarian cancer, genetic screening panels, details about the genes, and also candidate genes. We hope to bring new information to the medical field so as to better prevent and diagnose ovarian cancer.

The UFMylated ribosome-recognition protein SAYSD1 is predominantly expressed in spermatids but is dispensable for fertility in mice

SAYSVFN domain-containing protein 1 (SAYSD1) is an evolutionarily conserved membrane protein that has recently been identified as a ubiquitin-fold modifier 1 (UFM1)-conjugated ribosome-recognition protein that plays a critical role in translocation-associated quality control (TAQC). However, its expression and roles in mammals in vivo remain largely unknown. We found that SAYSD1 is predominantly expressed in round and elongating spermatids and localizes in the endoplasmic reticulum (ER) of mouse testes, but not in differentiated spermatozoa. Mice deficient in Saysd1 developed normally post-partum. Furthermore, Saysd1-deficient mice were fertile, with no apparent differences in sperm morphology or motility compared with wild-type sperm, although the cauda epididymis contained slightly less sperm. Expression of the ER stress markers spliced X-box binding protein 1s (XBP1s) and CCAAT/enhancer binding protein (C/EBP)-homologous protein (CHOP) in the testes was comparable between Saysd1-deficient and wild-type mice. These results suggested that SAYSD1 is involved in sperm production in mice but is dispensable for their development and fertility.

A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma

Background

Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored.

Results

Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. BNIP3 and CXCL12 were significantly differentially expressed between both clusters (P<0.05) and were identified as prognosis-related genes. The risk model showed that the risk score and tumor metastasis were independent prognostic factors of patients with OS. A nomogram combining risk score and tumor metastasis effectively predicted the prognosis. In addition, patients in the high-risk group had low immune scores and high tumor purity. The levels of immune cell infiltration, expression of human leukocyte antigen (HLA) genes, immune response gene sets, and immune checkpoints were lower in the high-risk group than those in the low-risk group. The low-risk group was sensitive to the immune checkpoint PD-1 inhibitor, and the high-risk group exhibited lower inhibitory concentration values by 50% for 24 drugs, including AG.014699, AMG.706, and AZD6482.

Conclusion

The prognostic stratification framework of patients with OS based on ARGs, such as BNIP3 and CXCL12, may lead to more efficient clinical management.

Protein expression profiling identifies a prognostic model for ovarian cancer

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

Results

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

Conclusions

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

PTPN2 in the Immunity and Tumor Immunotherapy: A Concise Review

PTPN2 (protein tyrosine phosphatase non-receptor 2), also called TCPTP (T cell protein tyrosine phosphatase), is a member of the PTP family signaling proteins. Phosphotyrosine-based signaling of this non-transmembrane protein is essential for regulating cell growth, development, differentiation, survival, and migration. In particular, PTPN2 received researchers’ attention when Manguso et al. identified PTPN2 as a cancer immunotherapy target using in vivo CRISPR library screening. In this review, we attempt to summarize the important functions of PTPN2 in terms of its structural and functional properties, inflammatory reactions, immunomodulatory properties, and tumor immunity. PTPN2 exerts synergistic anti-inflammatory effects in various inflammatory cells and regulates the developmental differentiation of immune cells. The diversity of PTPN2 effects in different types of tumors makes it a potential target for tumor immunotherapy.

Genome-wide evaluation of copy gain and loss variations in three Afghan sheep breeds

Copy number variation (CNV) is one of the main sources of variation between different individuals that has recently attracted much researcher interest as a major source for heritable variation in complex traits. The aim of this study was to identify CNVs in Afghan indigenous sheep consisting of three Arab, Baluchi, and Gadik breeds using genomic arrays containing 53,862 single nucleotide polymorphism (SNP) markers. Data were analyzed using the Hidden Markov Model (HMM) of PennCNV software. In this study, out of 45 sheep studied, 97.8% (44 animals) have shown CNVs. In total, 411 CNVs were observed for autosomal chromosomes and the entire sequence length of around 144 Mb was identified across the genome. The average number of CNVs per each sheep was 9.13. The identified CNVs for Arab, Baluchi, and Gadik breeds were 306, 62, and 43, respectively. After merging overlapped regions, a total of 376 copy number variation regions (CNVR) were identified, which are 286, 50, and 40 for Arab, Baluchi, and Gadik breeds, respectively. Bioinformatics analysis was performed to identify the genes and QTLs reported in these regions and the biochemical pathways involved by these genes. The results showed that many of these CNVRs overlapped with the genes or QTLs that are associated with various pathways such as immune system development, growth, reproduction, and environmental adaptions. Furthermore, to determine a genome-wide pattern of selection signatures in Afghan sheep breeds, the unbiased estimates of FST was calculated and the results indicated that 37 of the 376 CNVRs (~ 10%) have been also under selection signature, most of those overlapped with the genes influencing production, reproduction and immune system. Finally, the statistical methods used in this study was applied in an external dataset including 96 individuals of the Iranian sheep breed. The results indicated that 20 of the 114 CNVRs (18%) identified in Iranian sheep breed were also identified in our study, most of those overlapped with the genes influencing production, reproduction and immune system. Overall, this is the first attempts to develop the genomic map of loss and gain variation in the genome of Afghan indigenous sheep breeds, and may be important to shed some light on the genomic regions associated with some economically important traits in these breeds.

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning

Background

Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC.

Purpose

This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients.

Methods

The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels.

Results

A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts.

Conclusion

The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.

Role of adaptin protein complexes in intracellular trafficking and their impact on diseases

Adaptin proteins (APs) play a crucial role in intracellular cell trafficking. The 'classical' role of APs is carried out by AP1‒3, which bind to clathrin, cargo, and accessory proteins. Accordingly, AP1-3 are crucial for both vesicle formation and sorting. All APs consist of four subunits that are indispensable for their functions. In fact, based on studies using cells, model organism knockdown/knock-out, and human variants, each subunit plays crucial roles and contributes to the specificity of each AP. These studies also revealed that the sorting and intracellular trafficking function of AP can exert varying effects on pathology by controlling features such as cell development, signal transduction related to the apoptosis and proliferation pathways in cancer cells, organelle integrity, receptor presentation, and viral infection. Although the roles and functions of AP1‒3 are relatively well studied, the functions of the less abundant and more recently identified APs, AP4 and AP5, are still to be investigated. Further studies on these APs may enable a better understanding and targeting of specific diseases.APs known or suggested locations and functions.

Comprehensive analysis of prognostic genes in gastric cancer

Background: Gastric cancer is associated with high mortality, and effective methods for predicting prognosis are lacking. We aimed to identify potential prognostic markers associated with the development of gastric cancer through bioinformatic analyses.

Methods: Gastric cancer-associated gene expression profiles were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The key genes involved in the development of gastric cancer were obtained by differential expression analysis, coexpression analysis, and short time-series expression miner (STEM) analysis. The potential prognostic value of differentially expressed genes was further evaluated using a Cox regression model and risk scores. Hierarchical clustering was applied to validate the impact of key genes on the overall survival of gastric cancer patients.

Results: A total of 1381 genes were consistently dysregulated in the development of gastric cancer. Among them, 186 genes affected the overall survival of gastric cancer patients. The following genes had areas under the receiver operating characteristic curve greater than 0.9 in both datasets and were therefore considered key genes: ADAM12, CEP55, LRFN4, INHBA, ADH1B, DPT, FAM107A, and LOC100506388. LRFN4, DPT, and LOC100506388 were identified as potential prognostic genes for gastric cancer through a nomogram. Overexpression of LRFN4 and LOC100506388 was associated with a higher risk of gastric cancer. Finally, we found that tumors were infiltrated with high levels of Th2 cells and mast cells, and the infiltration levels were associated with overall survival in gastric cancer patients.

Conclusions: We found that key dysregulated genes may have a prognostic value for the development of gastric cancer.