ToP: a trend-of-disease-progression procedure works well for identifying cancer genes from multi-state cohort gene expression data for human colorectal cancer

Overexpressed SNRPB/D1/D3/E/F/G correlate with poor survival and immune infiltration in hepatocellular carcinoma

BACKGROUND

Prior reports have indicated that the abnormal expression of small nuclear ribonucleoproteins (snRNPs) genes is related to malignant tumors. However, in hepatocellular carcinoma (HCC), the precise role of snRNPs is not well understood. Therefore, the purpose of this study was to evaluate the prognostic roles of SNRPB/D1/D2/D3/E/F/G and their correlation to immune infiltration in HCC.

METHODS

The study was carried out via the following databases, software, and experimental validation: ONCOMINE, GEPIA2, UALCAN, The Cancer Genome Atlas, Gene Expression Omnibus, ArrayExpress, Kaplan-Meier plotter, cBioPortal, STRING, DAVID 6.8, TIMER, Cytoscape software, and immunohistochemistry experiments.

RESULTS

Overexpressed SNRPB/D1/D2/D3/E/F/G proteins were found in HCC tissues. The transcription levels of 7 snRNPs genes were related to the TP53 mutation and tumor grades. SNRPB/D1/D2/D3/F/G expression was significantly correlated with cancer staging, whereas SNRPE was not. Moreover, Kaplan-Meier survival analysis showed that upregulation of SNRPB/D1/D2/E/G was relevant to worse OS in HCC patients, especially in patients with alcohol consumption and those without viral hepatitis. Multivariate Cox regression analysis indicated that expression of SNRPB/D1/D3/E/F/G were independent prognostic factors for unfavorable OS in HCC. In addition, a high mutation rate of snRNPs genes (44%) was also found in HCC. The mRNA expression levels of snRNPs were meaningfully and positively related to six types of infiltrating immune cells (B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophil, macrophage, and dendritic cells). Also, SNRPB/D1/G genes were significantly associated with molecular markers of various immune cells in HCC.

CONCLUSIONS

SNRPB/D1/D3/E/F/G are potential prognostic biomarkers for a short OS in HCC, and SNRPB/D1/G were novel immune therapy targets in HCC patients.

Perspectives on Drug Repurposing

Complex common diseases are a significant burden for our societies and demand not only preventive measures but also more effective, safer, and more affordable treatments. The whole process of the current model of drug discovery and development implies a high investment by the pharmaceutical industry, which ultimately impact in high drug prices. In this sense, drug repurposing would help meet the needs of patients to access useful and novel treatments. Unlike the traditional approach, drug repurposing enters both the preclinical evaluation and clinical trials of the compound of interest faster, budgeting research and development costs, and limiting potential biosafety risks. The participation of government, society, and private investors is needed to secure the funds for experimental design and clinical development of repurposing candidates to have affordable, effective, and safe repurposed drugs. Moreover, extensive advertising of repurposing as a concept in the health community, could reduce prescribing bias when enough clinical evidence exists, which will support the employment of cheaper and more accessible repurposed compounds for common conditions.

Drug repurposing for cancer therapy, easier said than done

Drug repurposing for cancer therapy is currently a hot topic of research. Theoretically, in contrast to the known hurdles of developing new molecular entities, the approach of repurposing has several advantages. Mostly, it is said that it is faster, safer, easier, and cheaper. In the real world, however, there are only three repurposed drugs so far, that are listed in widely recognized cancer guidelines, but a large number of them are being studied. Among the many barriers to repurposing cancer drugs, economical-driven are the most important that difficult the clinical development of them. In this review, we provide an overview of the current status of drug repurposing for cancer therapy and the barriers that need to be overcome to realize the benefit of this approach. It means to have repositioned drugs for cancer therapy accepted as standard therapy for cancer indications at low cost.

Differential network analysis and protein-protein interaction study reveals active protein modules in glucocorticoid resistance for infant acute lymphoblastic leukemia

BACKGROUND

Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis.

METHODS

This study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed into two sub-networks, specific to each phenotype. Finally, both sub-networks were clustered into modules using weighted gene co-expression network analysis (WGCNA) and further analyzed with functional enrichment analysis.

RESULTS

Through the integration of DC analysis and PPI network, four protein modules were found active under the GC-resistance phenotype but not under the GC-sensitive. Functional enrichment analysis revealed that these modules are related to proteasome, electron transport chain, tRNA-aminoacyl biosynthesis, and peroxisome signaling pathways. These findings are in accordance with previous findings related to GC-resistance in other hematological malignancies such as pediatric ALL.

CONCLUSIONS

Differential co-expression analysis is a promising approach to incorporate the dynamic context of gene expression profiles into the well-documented protein interaction networks. The approach allows the detection of relevant protein modules that are highly enriched with DC gene pairs. Functional enrichment analysis of detected protein modules generates new biological hypotheses and may help in explaining the GC-resistance in MLL-rearranged infant ALL patients.

ILF2 promotes anchorage independence through direct regulation of PTEN

Anoikis is a specific form of programmed cell death induced by loss of contact between cells and extracellular matrices or other cells. Only tumor cells that are resistant to anoikis can survive in the state of detachment from the primary tissue during the early stages of metastasis. The ability to resist anoikis is crucial for cancer cell metastasis. ILF2 is a proto-oncogene previously studied in glioma, NSCLC, esophageal cancer and pancreatic ductal carcinoma. The results from the present study revealed that the transcription factor interleukin enhancer-binding factor 2 (ILF2) was highly expressed in non-small cell lung cancer (NSCLC) cell lines compared with in normal cell lines. ChIP and luciferase reporter gene assays demonstrated that ILF2 inhibited the expression level of the tumor suppressor gene phosphatase and tensin homolog (PTEN) by directly binding to its upstream regulatory region. Furthermore, the results from the detection of cell adhesion and apoptosis in cell suspension culture demonstrated that this mechanism enabled NSCLC cells to reduce adherence to the matrix and to survive in this abnormal state. These results suggested that ILF2 may promote the anchorage-independence of NSCLC cells through the suppression of PTEN.

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notably including cancers, can make repurposing time consuming and inefficient. The key question we address is how to efficiently repurpose an existing drug to treat a given indication. As drug efficacy remains the main bottleneck for overall success, we discuss the need for machine-learning computational methods in combination with specific phenotypic studies along with mechanistic studies, chemical genetics and omics assays to successfully predict disease-drug pairs. Such a pipeline could be particularly important to cancer patients who face heterogeneous, recurrent and metastatic disease and need fast and personalized treatments. Here we focus on drug repurposing for colorectal cancer and describe selected therapeutics already repositioned for its prevention and/or treatment as well as potential candidates. We consider this review as a selective compilation of approaches and methodologies, and argue how, taken together, they could bring drug repurposing to the next level.

Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis

Gastric Carcinoma is one of the most common cancers in the world. A large number of differentially expressed genes have been identified as being associated with gastric cancer progression, however, little is known about the underlying regulatory mechanisms. To address this problem, we developed a differential networking approach that is characterized by including a nascent methodology, differential coexpression analysis (DCEA), and two novel quantitative methods for differential regulation analysis. We first applied DCEA to a gene expression dataset of gastric normal mucosa, adenoma and carcinoma samples to identify gene interconnection changes during cancer progression, based on which we inferred normal, adenoma, and carcinoma-specific gene regulation networks by using linear regression model. It was observed that cancer genes and drug targets were enriched in each network. To investigate the dynamic changes of gene regulation during carcinogenesis, we then designed two quantitative methods to prioritize differentially regulated genes (DRGs) and gene pairs or links (DRLs) between adjacent stages. It was found that known cancer genes and drug targets are significantly higher ranked. The top 4% normal vs. adenoma DRGs (36 genes) and top 6% adenoma vs. carcinoma DRGs (56 genes) proved to be worthy of further investigation to explore their association with gastric cancer. Out of the 16 DRGs involved in two top-10 DRG lists of normal vs. adenoma and adenoma vs. carcinoma comparisons, 15 have been reported to be gastric cancer or cancer related. Based on our inferred differential networking information and known signaling pathways, we generated testable hypotheses on the roles of GATA6, ESRRG and their signaling pathways in gastric carcinogenesis. Compared with established approaches which build genome-scale GRNs, or sub-networks around differentially expressed genes, the present one proved to be better at enriching cancer genes and drug targets, and prioritizing disease-related genes on the dataset we considered. We propose this extendable differential networking framework as a promising way to gain insights into gene regulatory mechanisms underlying cancer progression and other phenotypic changes.

The role of IRES trans-acting factors in carcinogenesis

Regulation of protein expression through RNA metabolism is a key aspect of cellular homeostasis. Upon specific cellular stresses, distinct transcripts are selectively controlled to modify protein output in order to quickly and appropriately respond to stress. Reprogramming of the translation machinery is one node of this strict control that typically consists of an attenuation of the global, cap-dependent translation and accompanying switch to alternative mechanisms of translation initiation, such as internal ribosome entry site (IRES)-mediated initiation. In cancer, many aspects of the RNA metabolism are frequently misregulated to provide cancer cells with a growth and survival advantage. This includes changes in the expression and function of RNA binding proteins termed IRES trans-acting factors (ITAFs) that are central to IRES translation. In this review, we will examine select emerging, as well as established, ITAFs with important roles in cancer initiation and progression, and in particular their role in IRES-mediated translation. This article is part of a Special Issue entitled: Translation and Cancer.

Functional Module Connectivity Map (FMCM): a framework for searching repurposed drug compounds for systems treatment of cancer and an application to colorectal adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.

Dynamic protein interaction modules in human hepatocellular carcinoma progression

Background

Gene expression profiles have been frequently integrated with the human protein interactome to uncover functional modules under specific conditions like disease state. Beyond traditional differential expression analysis, differential co-expression analysis has emerged as a robust approach to reveal condition-specific network modules, with successful applications in a few human disease studies. Hepatocellular carcinoma (HCC), which is often interrelated with the Hepatitis C virus, typically develops through multiple stages. A comprehensive investigation of HCC progression-specific differential co-expression modules may advance our understanding of HCC's pathophysiological mechanisms.

Results

Compared with differentially expressed genes, differentially co-expressed genes were found more likely enriched with Hepatitis C virus binding proteins and cancer-mutated genes, and they were clustered more densely in the human reference protein interaction network. These observations indicated that a differential co-expression approach could outperform the standard differential expression network analysis in searching for disease-related modules. We then proposed a differential co-expression network approach to uncover network modules involved in HCC development. Specifically, we discovered subnetworks that enriched differentially co-expressed gene pairs in each HCC transition stage, and further resolved modules with coherent co-expression change patterns over all HCC developmental stages. Our identified network modules were enriched with HCC-related genes and implicated in cancer-related biological functions. In particular, APC and YWHAZ were highlighted as two most remarkable genes in the network modules, and their dynamic interaction partnership was resolved in HCC development.

Conclusions

We demonstrated that integration of differential co-expression with the protein interactome could outperform the traditional differential expression approach in discovering network modules of human diseases. In our application of this approach to HCC's gene expression data, we successfully identified subnetworks with marked differential co-expression in individual HCC stage transitions and network modules with coherent co-expression change patterns over all HCC developmental stages. Our results shed light on subtle HCC mechanisms, including temporal activation and dismissal of pivotal functions and dynamic interaction partnerships of key genes.