Shared MHC class II-dependent melanoma ribosomal protein L8 identified by phage display

Silencing RPL8 inhibits the progression of hepatocellular carcinoma by down-regulating the mTORC1 signalling pathway

This study aimed to explore the role of ribosomal protein L8 (RPL8) in controlling hepatocellular carcinoma (LIHC) development. We measured RPL8 expression, apoptosis, cell viability, proliferation, migration, invasion, glucose uptake, lactate production, and the ATP/ADP ratio of LIHC cells to investigate the effect of RPL8 on LIHC. Bioinformatic analysis was employed to analyse RPL8 expression and its potential mechanism in LIHC. RPL8 was upregulated in LIHC tissues and cells. RPL8 silencing accelerated apoptosis and suppressed viability, growth, and movement of LIHC cells. Additionally, RPL8 silencing inhibited glycolysis in LIHC cells. Bioinformatic analysis revealed that RPL8 is regulated by the upstream transcription factor upstream stimulating factor 1 (USF1) and activates the mTORC1 signalling pathway. USF1 overexpression eliminated the inhibitory effect of RPL8 silencing in LIHC cells. RPL8 overexpression increased cell growth, movement, and glycolysis in LIHC. However, inhibition of the mTORC1 signalling pathway eliminated the effect of RPL8 overexpression on LIHC cells. In conclusion, RPL8 may affect LIHC progression by regulating the mTORC1 signalling pathway.

Genome-Wide Association Study Identifies Genetic Loci Associated With Fat Cell Number and Overlap With Genetic Risk Loci for Type 2 Diabetes

Interindividual differences in generation of new fat cells determine body fat and type 2 diabetes risk. In the GENetics of Adipocyte Lipolysis (GENiAL) cohort, which consists of participants who have undergone abdominal adipose biopsy, we performed a genome-wide association study (GWAS) of fat cell number (n = 896). Candidate genes from the genetic study were knocked down by siRNA in human adipose-derived stem cells. We report 318 single nucleotide polymorphisms (SNPs) and 17 genetic loci displaying suggestive (P < 1 × 10-5) association with fat cell number. Two loci pass threshold for GWAS significance, on chromosomes 2 (lead SNP rs149660479-G) and 7 (rs147389390-deletion). We filtered for fat cell number-associated SNPs (P < 1.00 × 10-5) using evidence of genotype-specific expression. Where this was observed we selected genes for follow-up investigation and hereby identified SPATS2L and KCTD18 as regulators of cell proliferation consistent with the genetic data. Furthermore, 30 reported type 2 diabetes-associated SNPs displayed nominal and consistent associations with fat cell number. In functional follow-up of candidate genes, RPL8, HSD17B12, and PEPD were identified as displaying effects on cell proliferation consistent with genetic association and gene expression findings. In conclusion, findings presented herein identify SPATS2L, KCTD18, RPL8, HSD17B12, and PEPD of potential importance in controlling fat cell numbers (plasticity), the size of body fat, and diabetes risk.

Computational screening of potential glioma-related genes and drugs based on analysis of GEO dataset and text mining

BACKGROUND

Considering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs.

METHODS

Eight glioma samples and twelve control samples were analyzed on the GSE31095 datasets, and differentially expressed genes (DEGs) were obtained via the R software. The related glioma genes were further acquired from the text mining. Additionally, Venny program was used to screen out the common genes of the two gene sets and DAVID analysis was used to conduct the corresponding gene ontology analysis and cell signal pathway enrichment. We also constructed the protein interaction network of common genes through STRING, and selected the important modules for further drug-gene analysis. The existing antitumor drugs that targeted these module genes were screened to explore their efficacy in glioma treatment.

RESULTS

The gene set obtained from text mining was intersected with the previously obtained DEGs, and 128 common genes were obtained. Through the functional enrichment analysis of the identified 128 DEGs, a hub gene module containing 25 genes was obtained. Combined with the functional terms in GSE109857 dataset, some overlap of the enriched function terms are both in GSE31095 and GSE109857. Finally, 4 antitumor drugs were identified through drug-gene interaction analysis.

CONCLUSIONS

In this study, we identified that two potential genes and their corresponding four antitumor agents could be used as targets and drugs for glioma exploration.

Multi-omic Dissection of Oncogenically Active Epiproteomes Identifies Drivers of Proliferative and Invasive Breast Tumors

Proliferative and invasive breast tumors evolve heterogeneously in individual patients, posing significant challenges in identifying new druggable targets for precision, effective therapy. Here we present a functional multi-omics method, interaction-Correlated Multi-omic Aberration Patterning (iC-MAP), which dissects intra-tumor heterogeneity and identifies in situ the oncogenic consequences of multi-omics aberrations that drive proliferative and invasive tumors. First, we perform chromatin activity-based chemoproteomics (ChaC) experiments on breast cancer (BC) patient tissues to identify genetic/transcriptomic alterations that manifest as oncogenically active proteins. ChaC employs a biotinylated small molecule probe that specifically binds to the oncogenically active histone methyltransferase G9a, enabling sorting/enrichment of a G9a-interacting protein complex that represents the predominant BC subtype in a tissue. Second, using patient transcriptomic/genomic data, we retrospectively identified some G9a interactor-encoding genes that showed individualized iC-MAP. Our iC-MAP findings represent both new diagnostic/prognostic markers to identify patient subsets with incurable metastatic disease and targets to create individualized therapeutic strategies.

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ChaC dissects tumor heterogeneity for identifying oncogenic-active proteins

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An oncogenic-active G9a-interactome represents the invasive tumor in a tissue

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iC-MAP identifies multi-omics aberrations that drive invasive tumors

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Patient-specific iC-MAP of select interactor genes are of prognostic value

Identification of Key Candidate Proteins and Pathways Associated with Temozolomide Resistance in Glioblastoma Based on Subcellular Proteomics and Bioinformatical Analysis

TMZ resistance remains one of the main reasons why treatment of glioblastoma (GBM) fails. In order to investigate the underlying proteins and pathways associated with TMZ resistance, we conducted a cytoplasmic proteome research of U87 cells treated with TMZ for 1 week, followed by differentially expressed proteins (DEPs) screening, KEGG pathway analysis, protein–protein interaction (PPI) network construction, and validation of key candidate proteins in TCGA dataset. A total of 161 DEPs including 65 upregulated proteins and 96 downregulated proteins were identified. Upregulated DEPs were mainly related to regulation in actin cytoskeleton, focal adhesion, and phagosome and PI3K-AKT signaling pathways which were consistent with our previous studies. Further, the most significant module consisted of 28 downregulated proteins that were filtered from the PPI network, and 9 proteins (DHX9, HNRNPR, RPL3, HNRNPA3, SF1, DDX5, EIF5B, BTF3, and RPL8) among them were identified as the key candidate proteins, which were significantly associated with prognosis of GBM patients and mainly involved in ribosome and spliceosome pathway. Taking the above into consideration, we firstly identified candidate proteins and pathways associated with TMZ resistance in GBM using proteomics and bioinformatic analysis, and these proteins could be potential biomarkers for prevention or prediction of TMZ resistance in the future.

Dysregulated COL3A1 and RPL8, RPS16, and RPS23 in Disc Degeneration Revealed by Bioinformatics Methods

STUDY DESIGN

Bioinformatics analysis of published microarray data.

OBJECTIVE

This study aimed to reveal the possible genes and pathways related to the pathogenesis of disc degeneration (DD) by analyzing the microarray data.

SUMMARY OF BACKGROUND DATA

DD is one of the main causes of low back pain, which has become an enormous economic burden for society.

METHODS

Gene expression data of annulus cells and nucleus pulposus cells from patients with DD and controls subjects were downloaded from Gene Expression Omnibus. T test and enrichment analysis were used to identify differentially expressed genes (DEGs) and DEGs-associated functions and pathways in DD, respectively. Protein-protein interaction network and module were constructed to analyze the key nodes associated with this disease.

RESULTS

A total of 326 DEGs and 35 DEGs were obtained from the annulus cells and nucleus pulposus cells, respectively. The DEGs of DD in annulus cells were mainly involved in translation, cell adhesion, cell death regulation, and skeletal system development whereas the DEGs in nucleus pulposus cells were mainly related to the biological processes of vascular system development, skeletal system development, and enzyme-linked receptor protein signaling pathway. COL3A1 was the common DEG in both annulus cells and nucleus pulposus cells. The genes encode ribosomal proteins (RPL8, RPS16, and RPS23) in module were enriched in biological processes of translation, translation elongation, and RNA processing.

CONCLUSION

The results revealed the involvement of COL3A1 in skeletal system process and RPL8, RPS16, and RPS23 in the protein synthesis processes in the progression of DD, suggesting their potential use in the diagnosis and therapy of DD.

LEVEL OF EVIDENCE

N/A.

Unique genomic profile of fibrolamellar hepatocellular carcinoma

BACKGROUND & AIMS

Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer that develops in children and young adults without cirrhosis. Little is known about its pathogenesis, and it can be treated only with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors.

METHODS

By using 78 clinically annotated FLC samples, we performed whole-transcriptome (n = 58), single-nucleotide polymorphism array (n = 41), and next-generation sequencing (n = 48) analyses; we also assessed the prevalence of the DNAJB1-PRKACA fusion transcript associated with this cancer (n = 73). We performed class discovery using non-negative matrix factorization, and functional annotation using gene-set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry. The genomic identification of significant targets in a cancer algorithm was used to identify chromosomal aberrations, MuTect and VarScan2 were used to identify somatic mutations, and the random survival forest was used to determine patient prognoses. Findings were validated in an independent cohort.

RESULTS

Unsupervised gene expression clustering showed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mammalian target of rapamycin signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine enriched production; and the unannotated class (23% of samples) had a gene expression signature that was not associated previously with liver tumors. Expression of genes that regulate neuroendocrine function, as well as histologic markers of cholangiocytes and hepatocytes, were detected in all 3 classes. FLCs had few copy number variations; the most frequent were focal amplification at 8q24.3 (in 12.5% of samples), and deletions at 19p13 (in 28% of samples) and 22q13.32 (in 25% of samples). The DNAJB1-PRKACA fusion transcript was detected in 79% of samples. FLC samples also contained mutations in cancer-related genes such as BRCA2 (in 4.2% of samples), which are uncommon in liver neoplasms. However, FLCs did not contain mutations most commonly detected in liver cancers. We identified an 8-gene signature that predicted survival of patients with FLC.

CONCLUSIONS

In a genomic analysis of 78 FLC samples, we identified 3 classes based on gene expression profiles. FLCs contain mutations and chromosomal aberrations not previously associated with liver cancer, and almost 80% contain the DNAJB1-PRKACA fusion transcript. By using this information, we identified a gene signature that is associated with patient survival time.

Ribosomal proteins and human diseases: pathogenesis, molecular mechanisms, and therapeutic implications

Ribosomes are essential components of the protein synthesis machinery. The process of ribosome biogenesis is well organized and tightly regulated. Recent studies have shown that ribosomal proteins (RPs) have extraribosomal functions that are involved in cell proliferation, differentiation, apoptosis, DNA repair, and other cellular processes. The dysfunction of RPs has been linked to the development and progression of hematological, metabolic, and cardiovascular diseases and cancer. Perturbation of ribosome biogenesis results in ribosomal stress, which triggers activation of the p53 signaling pathway through RPs-MDM2 interactions, resulting in p53-dependent cell cycle arrest and apoptosis. RPs also regulate cellular functions through p53-independent mechanisms. We herein review the recent advances in several forefronts of RP research, including the understanding of their biological features and roles in regulating cellular functions, maintaining cell homeostasis, and their involvement in the pathogenesis of human diseases. We also highlight the translational potential of this research for the identification of molecular biomarkers, and in the discovery and development of novel treatments for human diseases.

Biomarker discovery using statistically significant gene sets

Analysis of large gene expression data sets in the presence and absence of a phenotype can lead to the selection of a group of genes serving as biomarkers jointly predicting the phenotype. Among gene selection methods, filter methods derived from ranked individual genes have been widely used in existing products for diagnosis and prognosis. Univariate filter approaches selecting genes individually, although computationally efficient, often ignore gene interactions inherent in the biological data. On the other hand, multivariate approaches selecting gene subsets are known to have a higher risk of selecting spurious gene subsets due to the overfitting of the vast number of gene subsets evaluated. Here we propose a framework of statistical significance tests for multivariate feature selection that can reduce the risk of selecting spurious gene subsets. Using three existing data sets, we show that our proposed approach is an essential step to identify such a gene set that is generated by a significant interaction of its members, even improving classification performance when compared to established approaches. This technique can be applied for the discovery of robust biomarkers for medical diagnosis.

Developing dendritic cell-based therapies to condition immune responses to novel oncogenic proteins and stem cells

Cancer vaccines have been disappointing when utilized as stand-alone therapy, especially in late disease settings. However, recent clinical studies in prostate cancer have suggested that dendritic cellular (DC) vaccines may impact patient survival, reviving the notion that cancer vaccines can impact established cancer. In this review we will highlight the advances that have been made in the development of DC-based therapies activated by Toll-like receptor agonists with the capacity to condition toward strong Th1 cellular responses, through the production of cytokines and chemokines, and a capacity to induce apoptosis of tumor cells. Used in early cancer settings, these DCs induce clinically effective immune responses, thus shifting the emphasis toward using these cells earlier in the disease process. We will also discuss targeting novel molecules and cancer stem cells that can eliminate cells with high metastatic potential, moving DC-based therapies into mainstream cancer therapy.

Molecular characterization of the response to chemotherapy in conventional osteosarcomas: predictive value of HSD17B10 and IFITM2

The therapy regimen of high-grade osteosarcoma includes chemotherapy followed by surgical resection and postoperative chemotherapy. The degree of necrosis following definitive surgery remains the only reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. The aim of this study was to find molecular markers able to classify patients with an osteosarcoma as good or poor responders to chemotherapy before beginning treatment. Gene expression screening of 20 nonmetastatic high-grade osteosarcoma patients was performed using cDNA microarray. Expression of selected relevant genes was validated using QRT-PCR. Immunohistochemistry on tissue microarrays sections of 73 biopsies was performed to investigate protein expression. Fluorescent in situ hybridization was performed for RPL8 gene. We have found that HSD17B10 gene expression was up-regulated in poor responders and that immunohistochemistry expression of HSD17B10 on biopsy before treatment was correlated to response to chemotherapy. Other results include correlation of IFITM2, IFITM3, and RPL8 gene expression to chemotherapy response. A statistical correlation was found between polysomy 8 or gain of RPL8 and good response to chemotherapy. These data suggest that HSD17B10, RPL8, IFITM2, and IFITM3 genes are involved in the response to the chemotherapy and that HSD17B10 may be a therapeutic target. RPL8 and IFITM2 may be useful in the assessment at diagnosis and for stratifying patients taking part in randomized trials.