Stoichioproteomics study of differentially expressed proteins and pathways in head and neck cancer

Hypoxia is a prominent feature of head and neck cancer. However, the oxygen element characteristics of proteins and how they adapt to hypoxia microenvironments of head and neck cancer are still unknown. Human genome sequences and proteins expressed data of head and neck cancer were retrieved from pathology atlas of Human Protein Atlas project. Then compared the oxygen and carbon element contents between proteomes of head and neck cancer and normal oral mucosa-squamous epithelial cells, genome locations, pathways, and functional dissection associated with head and neck cancer were also studied. A total of 902 differentially expressed proteins were observed where the average oxygen content is higher than that of the lowly expressed proteins in head and neck cancer proteins. Further, the average oxygen content of the up regulated proteins was 2.54% higher than other. None of their coding genes were distributed on the Y chromosome. The up regulated proteins were enriched in endocytosis, apoptosis and regulation of actin cytoskeleton. The increased oxygen contents of the highly expressed and the up regulated proteins might be caused by frequent activity of cytoskeleton and adapted to the rapid growth and fast division of the head and neck cancer cells. The oxygen usage bias and key proteins may help us to understand the mechanisms behind head and neck cancer in targeted therapy, which lays a foundation for the application of stoichioproteomics in targeted therapy and provides promise for potential treatments for head and neck cancer.

ProtFinder: finding subcellular locations of proteins using protein interaction networks

Protein subcellular localization prediction plays a crucial role in improving our understandings of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model gives an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations.

Integrated Bioinformatics Analysis for Identification of the Hub Genes Linked with Prognosis of Ovarian Cancer Patients

Background. One of the most usual gynecological state of tumor is ovarian cancer and is a major reason of gynecological tumor-related global mortality rate. There have been multiple risk elements related to ovarian cancer like the background of past cases associated with breast cancer or ovarian cancer, or excessive body weight issues, case history of smoking, and untimely menstruation or menopause. Because of unclear expressions, more than 70% of the ovarian cancer patient cases are determined during the early stage. Material and Methods. GSE38666, GSE40595, and GSE66957 were the three microarray datasets which were analyzed using GEO2R for screening the differentially expressed genes. GO, Kyoto Encyclopedia of Genes, and protein expression studies were performed for analysis of hub genes. Then, survival analysis was performed for all the hub genes. Results. From the dataset, a total of 199 differentially expressed genes (DEGs) were identified. Through the KEGG pathway study, it was noted that the DEGs are mainly linked with the AGE-RAGE signaling pathway, central carbon metabolism, and human papillomavirus infection. The survival analysis showed 4 highly expressed hub genes COL4A1, SDC1, CDKN2A, and TOP2A which correlated with overall survival in ovarian cancer patients. Moreover, the expression of the 4 hub genes was validated by the GEPIA database and the Human Protein Atlas. Conclusion. The results have shown that all 4 hub genes were found to be upregulated in ovarian cancer tissues which predict poor prognosis in patients with ovarian cancer.

Novel Molecular Targets for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of death from cancer globally. Indeed, only a few treatments are available, most of which are effective only for the early stages of the disease. Therefore, there is an urgent needing for potential markers for a specifically targeted therapy. Candidate proteins were selected from datasets of The Human Protein Atlas, in order to identify specific tumor-associated proteins overexpressed in HCC samples associated with poor prognosis. Potential epitopes were predicted from such proteins, and homology with peptides derived from viral proteins was assessed. A multiparametric validation was performed, including recognition by PBMCs from HCC-patients and healthy donors, showing a T-cell cross-reactivity with paired epitopes. These results provide novel HCC-specific tumor-associated antigens (TAAs) for immunotherapeutic anti-HCC strategies potentially able to expand pre-existing virus-specific CD8+ T cells with superior anticancer efficacy.

Expression, Regulation, and Functions of the Galectin-16 Gene in Human Cells and Tissues

Galectins comprise a family of soluble β-galactoside-binding proteins, which regulate a variety of key biological processes including cell growth, differentiation, survival, and death. This paper aims to address the current knowledge on the unique properties, regulation, and expression of the galectin-16 gene (LGALS16) in human cells and tissues. To date, there are limited studies on this galectin, with most focusing on its tissue specificity to the placenta. Here, we report the expression and 8-Br-cAMP-induced upregulation of LGALS16 in two placental cell lines (BeWo and JEG-3) in the context of trophoblastic differentiation. In addition, we provide the results of a bioinformatics search for LGALS16 using datasets available at GEO, Human Protein Atlas, and prediction tools for relevant transcription factors and miRNAs. Our findings indicate that LGALS16 is detected by microarrays in diverse human cells/tissues and alters expression in association with cancer, diabetes, and brain diseases. Molecular mechanisms of the transcriptional and post-transcriptional regulation of LGALS16 are also discussed based on the available bioinformatics resources.

Comprehensive Analysis of Aldehyde Dehydrogenases (ALDHs) and Its Significant Role in Hepatocellular Carcinoma

Oxidative DNA damage is closely related to the occurrence and progression of cancer. Oxidative stress plays an important role in alcohol-induced hepatocellular carcinoma (HCC). Aldehyde dehydrogenase (ALDH) is a family of enzymes that plays an essential role in the reducing oxidative damage. However, how ALDHs family affects alcohol-related HCC remains obscure. We aimed to explore the correlation between the differential expression of ALDHs in patients with HCC and pathological features, as well as the relationship between ALDHs and prognosis, and finally analyze the possible mechanism of ALDHs in targeted therapy of HCC. The data of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. This research explored the expression and prognostic values of ALDHs in HCC using Oncomine, UALCAN, Human Protein Atlas, cBioPortal, Kaplan–Meier plotter, GeneMANIA, Tumor Immune Estimation Resource, GEPIA databases, and WebGestalt. Low mRNA and protein expressions of ALDHs were found to be significantly associated with tumor grade and clinical cancer stages in HCC patients. In particular, the loss of ALDH expression is more obvious in Asians, and its effect on prognosis is far more significant than that in the White race. Our findings play an important role in the study of prognostic markers and anti-liver cancer therapeutic targets for the members of the ALDHs family, especially in patients with liver cancer in Asia.

Identification and Mechanism of the PD-1/PD-L1 Genomic Signature SORL1 as Protective Factor in Bladder Cancer

Background: Immunotherapy has recently shown remarkable efficacy for advanced bladder cancer patients. Accordingly, identifying a biomarker associated with the programmed cell death protein 1 (PD-1)/its ligand (PD-L1) genomic signature to predict patient prognosis is necessary.Methods: In this study, we used mutation data and RNA-seq data of bladder cancer samples acquired from The Cancer Genome Atlas (TCGA) database to combine PD-1/PD-L1-associated mutational signatures with PD-1/PD-L1-associated differentially expressed genes (DEGs). Then, we performed a Kaplan-Meier analysis on the corresponding clinical data of the TCGA bladder urothelial carcinoma (BLCA) cohort to identify prognostic genes, and the results were validated using the GSE48075 cohort. The online platform UCSC Xena was used to analyze the relationship between the candidate genes and clinical parameters. We utilized the Human Protein Atlas (HPA) database to validate the protein expression levels. Then, correlation analysis, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, and gene set enrichment analysis (GSEA) were used to clarify the mechanism.Results: We identified one prognostic gene, sortilin related receptor 1 (SORL1), whose downregulation was associated with a comparatively advanced BLCA stage. While further exploring this finding, we found that SORL1 expression was negatively correlated with PD-1/PD-L1 expression and M2 macrophage levels. Furthermore, we found that the downregulation of SORL1 expression was significantly associated with a higher epithelial-mesenchymal transition (EMT) score.Conclusion: We described a novel PD-1/PD-L1-associated signature, SORL1, that predicts favorable outcomes in bladder cancer. SORL1 might reduce immune suppression and inhibit the M2 macrophage-induced EMT phenotype of tumor cells.

Extracellular vesicle secretion is tissue-dependent ex vivo and skeletal muscle myofiber extracellular vesicles reach the circulation in vivo.

Extracellular vesicles (EVs) are biomarkers and modifiers of human disease. EVs secreted by insulin-responsive tissues like skeletal muscle (SkM) and white adipose (WAT) contribute to metabolic health and disease but the relative abundance of EVs from these tissues has not been directly examined. Human Protein Atlas data and directly measuring EV secretion in mouse SkM and WAT using an ex vivo tissue explant model confirmed that SkM tissue secretes more EVs than WAT. Differences in EV secretion between SkM and WAT were not due to SkM contraction but may be explained by differences in tissue metabolic capacity. We next examined how many EVs secreted from SkM tissue ex vivo and in vivo are myofiber-derived. To do this, a SkM myofiber-specific dual fluorescent reporter mouse was created. Spectral flow cytometry revealed that SkM myofibers are a major source of SkM tissue-derived EVs ex vivo and EV immunocapture indicate that ~5% of circulating tetraspanin-positive EVs are derived from SkM myofibers in vivo. Our findings demonstrate that 1) SkM secretes more EVs than WAT, 2) many SkM tissue EVs are derived from SkM myofibers and 3) SkM myofiber-derived EVs reach the circulation in vivo. These findings advance our understanding of EV secretion between metabolically active tissues and provide direct evidence that SkM myofibers secrete EVs that can reach the circulation in vivo.