Expression genetics in cancer: shifting the focus from DNA to RNA

DDX3X dynamics, glioblastoma's genetic landscape, therapeutic advances, and autophagic interplay

Glioblastoma is one of the most aggressive and deadly forms of cancer, posing significant challenges for the medical community. This review focuses on key aspects of Glioblastoma, including its genetic differences between primary and secondary types. Temozolomide is a major first-line treatment for Glioblastoma, and this article explores its development, how it works, and the issue of resistance that limits its effectiveness, prompting the need for new treatment strategies. Gene expression profiling has greatly advanced cancer research by revealing the molecular mechanisms of tumors, which is essential for creating targeted therapies for Glioblastoma. One important protein in this context is DDX3X, which plays various roles in cancer, sometimes promoting it or otherwise suppressing it. Additionally, autophagy, a process that maintains cellular balance, has complex implications in cancer treatment. Understanding autophagy helps to identify resistance mechanisms and potential treatments, with Chloroquine showing promise in treating Glioblastoma. This review covers the interplay between Glioblastoma, DDX3X, and autophagy, highlighting the challenges and potential strategies in treating this severe disease.

Evolution of Bioelectric Membrane Potentials: Implications in Cancer Pathogenesis and Therapeutic Strategies

Electrophysiology typically deals with the electrical properties of excitable cells like neurons and muscles. However, all other cells (non-excitable) also possess bioelectric membrane potentials for intracellular and extracellular communications. These membrane potentials are generated by different ions present in fluids available in and outside the cell, playing a vital role in communication and coordination between the cell and its organelles. Bioelectric membrane potential variations disturb cellular ionic homeostasis and are characteristic of many diseases, including cancers. A rapidly increasing interest has emerged in sorting out the electrophysiology of cancer cells. Compared to healthy cells, the distinct electrical properties exhibited by cancer cells offer a unique way of understanding cancer development, migration, and progression. Decoding the altered bioelectric signals influenced by fluctuating electric fields benefits understanding cancer more closely. While cancer research has predominantly focussed on genetic and molecular traits, the delicate area of electrophysiological characteristics has increasingly gained prominence. This review explores the historical exploration of electrophysiology in the context of cancer cells, shedding light on how alterations in bioelectric membrane potentials, mediated by ion channels and gap junctions, contribute to the pathophysiology of cancer.

A Tutorial Review of Labeling Methods in Mass Spectrometry-Based Quantitative Proteomics

Recent advancements in mass spectrometry (MS) have revolutionized quantitative proteomics, with multiplex isotope labeling emerging as a key strategy for enhancing accuracy, precision, and throughput. This tutorial review offers a comprehensive overview of multiplex isotope labeling techniques, including precursor-based, mass defect-based, reporter ion-based, and hybrid labeling methods. It details their fundamental principles, advantages, and inherent limitations along with strategies to mitigate the limitation of ratio-distortion. This review will also cover the applications and latest progress in these labeling techniques across various domains, including cancer biomarker discovery, neuroproteomics, post-translational modification analysis, cross-linking MS, and single-cell proteomics. This Review aims to provide guidance for researchers on selecting appropriate methods for their specific goals while also highlighting the potential future directions in this rapidly evolving field.

scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

Collagen 1A1 (COL1A1) and Collagen11A1(COL11A1) as diagnostic biomarkers in Breast, colorectal and gastric cancers

PURPOSE

Collagen family genes (CFGs) play a significant role in the pathogenesis of cancers. This study aimed to evaluate changes in the expression levels (Els) of CFGs related to epithelial-mesenchymal transition (EMT) and metastasis in gastric (GC), breast (BC), and colorectal (CRC) cancers to introduce these genes as potential diagnostic biomarkers for these three types of cancer.

METHODS

The Cancer Genome Atlas (TCGA) examined ELS changes in CFGs associated with EMT and metastasis to determine their diagnostic value for GC, BC, and CRC. InteractiVenn was used to find genes shared by these three cancers. The biomarker role of CFGs was determined using the receiver operating characteristic (ROC) analysis. GC, BC, and CRC samples were analyzed using the RT-qPCR method to verify the bioinformatics results and evaluate the EL of the selected genes as biomarkers for these cancers.

RESULTS

The in-silico results showed a significant increase in the EL of several CFGs involved in EMT and metastasis in GC, BC, and CRC samples compared to healthy samples. Six common genes (COL11A1, COL12A1, COL1A1, COL1A2, COL5A1, and COL5A2) showed significantly increased in these three cancers, therebysupporting their oncogenic role. Furthermore, the biomarker-related analyses indicated that COL11A1 and COL1A1 were common diagnostic biomarkers for the three cancers. The RT-qPCR method confirmed that the ELs of COL11A1 and COL1A1 in the GC, BC, and CRC samples increased significantly compared to the adjacent normal samples.

CONCLUSION

CFGs in EMT and metastasis of GC, BC, and CRC are strong common diagnostic biomarkers for these cancers.

Study of Drug Targets Associated With Oncogenesis and Cancer Cell Survival and the Therapeutic Activity of Engineered Ashwagandha Extract Having Differential Withanolide Constitutions

Ashwagandha (Withania somnifera) has gained worldwide popularity for a multitude of health benefits inclusive of cancer-preventive and curative effects. Despite numerous research data supporting the benefits of this wonder herb, the actual use of ashwagandha for cancer treatment in clinics is limited. The primary reason for this is the inconsistent therapeutic outcome due to highly variable composition and constitution of active ingredients in the plant extract impacting ashwagandha's pharmacology. We investigate here an engineered yield: an ashwagandha extract (Oncowithanib) that has a unique and fixed portion of active ingredients to achieve consistent and effective therapeutic activity. Using the MCF7 cell line, Oncowithanib was studied for its anti-neoplastic efficacy and drug targets associated with cell cycle regulation, translation machinery, and cell survival and apoptosis. Results demonstrate a dose-dependent decline in Oncowithanib-treated MCF7 cell viability and reduced colony-forming ability. Treated cells showed increased cell death as evidenced by enhancement of Caspase 3 enzyme activity and decreased expressions of cell proliferation markers such as Ki67 and Aurora Kinase A. Oncowithanib treatment was also found to be associated with expressional suppression of key cellular kinases such as RSK1, Akt1, and mTOR in MCF7 cells. Our findings indicate that Oncowithanib decreases MCF7 cell survival and propagation, and sheds light on common drug targets that might be good candidates for the development of cancer therapeutics. Further in-depth investigations are required to fully explore the potency and pharmacology of this novel extract. This study also highlights the importance of the standardization of herbal extracts to get consistent therapeutic activity for the disease indication.

Variant Analysis of miRNA Regulatory Genes in 35 Sporadic Lung Carcinoma Tumors

Lung cancer is one of the cancer types with the highest mortality worldwide. The most frequently mutated genes known to be clinically important in lung cancers are EGFR, BRAF, and KRAS genes. Therefore, the therapeutic agents developed are directed against variants that cause over-activation of the EGFR-KRAS-BRAF-BRAF-MEK/ERK signalling pathway. However, different responses of patients to Tyrosine Kinase Inhibitors (TKIs) suggest that new prognostic biomarkers should be defined and epigenetic mechanisms may be related to this situation.

METHODS

In this study, sequence analyses of AGO2, DICER, and DROSHA genes involved in miRNA biogenesis and EGFR, KRAS, and BRAF genes were performed in 35 patients with sporadic lung cancer.

RESULTS

We found variations in genes involved in miRNA biogenesis that have not been previously reported in the literature. In addition, we found 4 different variants in the EGFR gene that have been described in the literature. In addition, a statistically significant association was found between the presence of mutations in at least one of the genes involved in miRNA biogenesis and metastasis (p:0.02).

CONCLUSIONS

In conclusion, genomic dysregulation of key miRNA biogenesis genes may be one of the possible reasons for the differential response of patients to therapeutic agents and the development of metastasis in EGFR wild type tumours.

TK1 expression influences pathogenicity by cell cycle progression, cellular migration, and cellular survival in HCC 1806 breast cancer cells

Breast cancer is the most common cancer diagnosis worldwide accounting for 1 out of every 8 cancer diagnoses. The elevated expression of Thymidine Kinase 1 (TK1) is associated with more aggressive tumor grades, including breast cancer. Recent studies indicate that TK1 may be involved in cancer pathogenesis; however, its direct involvement in breast cancer has not been identified. Here, we evaluate potential pathogenic effects of elevated TK1 expression by comparing HCC 1806 to HCC 1806 TK1-knockdown cancer cells (L133). Transcriptomic profiles of HCC 1806 and L133 cells showed cell cycle progression, apoptosis, and invasion as potential pathogenic pathways affected by TK1 expression. Subsequent in-vitro studies confirmed differences between HCC 1806 and L133 cells in cell cycle phase progression, cell survival, and cell migration. Expression comparison of several factors involved in these pathogenic pathways between HCC 1806 and L133 cells identified p21 and AKT3 transcripts were significantly affected by TK1 expression. Creation of a protein-protein interaction map of TK1 and the pathogenic factors we evaluated predict that the majority of factors evaluated either directly or indirectly interact with TK1. Our findings argue that TK1 elevation directly increases HCC 1806 cell pathogenicity and is likely occurring by p21- and AKT3-mediated mechanisms to promote cell cycle arrest, cellular migration, and cellular survival.

Exploring tumor-normal cross-talk with TranNet: Role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights into the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations.

A multi-platform normalization method for meta-analysis of gene expression data

Transcriptomic profiling is a mainstay of translational cancer research and is often used to identify cancer subtypes, stratify responders vs. non-responders patients, predict survival, and identify potential targets for therapeutic intervention. Analysis of gene expression data gathered by RNA sequencing (RNA-seq) and microarray is generally the first step in identifying and characterizing cancer-associated molecular determinants. The methodological advancements and reduced costs associated with transcriptomic profiling have increased the number of publicly available gene expression profiles for cancer subtypes. Data integration from multiple datasets is routinely done to increase the number of samples, improve statistical power, and provide better insight into the heterogeneity of the biological determinant. However, utilizing raw data from multiple platforms, species, and sources introduces systematic variations due to noise, batch effects, and biases. As such, the integrated data is mathematically adjusted through normalization, which allows direct comparison of expression measures among studies while minimizing technical and systemic variations. This study applied meta-analysis to multiple independent Affymetrix microarray and Illumina RNA-seq datasets available through the Gene Expression Omnibus (GEO) and The Cancer Gene Atlas (TCGA). We have previously identified a tripartite motif containing 37 (TRIM37), a breast cancer oncogene, that drives tumorigenesis and metastasis in triple-negative breast cancer. In this article, we adapted and assessed the validity of Stouffer's z-score normalization method to interrogate TRIM37 expression across different cancer types using multiple large-scale datasets.

Dysregulation of SWI/SNF Chromatin Remodelers in NSCLC: Its Influence on Cancer Therapies including Immunotherapy

Lung cancer is the leading cause of cancer death worldwide. Molecularly targeted therapeutics and immunotherapy revolutionized the clinical care of NSCLC patients. However, not all NSCLC patients harbor molecular targets (e.g., mutated EGFR), and only a subset benefits from immunotherapy. Moreover, we are lacking reliable biomarkers for immunotherapy, although PD-L1 expression has been mainly used for guiding front-line therapeutic options. Alterations of the SWI/SNF chromatin remodeler occur commonly in patients with NSCLC. This subset of NSCLC tumors tends to be undifferentiated and presents high heterogeneity in histology, and it shows a dismal prognosis because of poor response to the current standard therapies. Catalytic subunits SMARCA4/A2 and DNA binding subunits ARID1A/ARID1B/ARID2 as well as PBRM1 were identified to be the most commonly mutated subunits of SWI/SNF complexes in NSCLC. Mechanistically, alteration of these SWI/SNF subunits contributes to the tumorigenesis of NSCLC through compromising the function of critical tumor suppressor genes, enhancing oncogenic activity as well as impaired DNA repair capacity related to genomic instability. Several vulnerabilities of NSCLCS with altered SWI/SNF subunits were detected and evaluated clinically using EZH2 inhibitors, PROTACs of mutual synthetic lethal paralogs of the SWI/SNF subunits as well as PARP inhibitors. The response of NSCLC tumors with an alteration of SWI/SNF to ICIs might be confounded by the coexistence of mutations in genes capable of influencing patients' response to ICIs. High heterogenicity in the tumor with SWI/SNF deficiency might also be responsible for the seemingly conflicting results of ICI treatment of NSCLC patients with alterations of SWI/SNF. In addition, an alteration of each different SWI/SNF subunit might have a unique impact on the response of NSCLC with deficient SWI/SNF subunits. Prospective studies are required to evaluate how the alterations of the SWI/SNF in the subset of NSCLC patients impact the response to ICI treatment. Finally, it is worthwhile to point out that combining inhibitors of other chromatin modulators with ICIs has been proven to be effective for the treatment of NSCLC with deficient SWI/SNF chromatin remodelers.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations. The TranNet method was implemented in python, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/TranNet .

Identification and Validation of Potentially Clinically Relevant CpG Regions within the Class 2 Tumor Suppressor Gene SFRP1 in Pancreatic Cancer

In pancreatic cancer treatment, tumor stage-dependent chemotherapies are used to prolong overall survival. By measuring DNA promoter hypermethylation in the plasma of patients with stage IV pancreatic cancer, it was recently shown that promoter DNA methylation of the tumor suppressor gene SFRP1 has a high value for predicting failure of drug treatment with gemcitabine. In this study, we therefore aimed to identify as precisely as possible the region in the SFRP1 promoter that is frequently hypermethylated in pancreatic cancer tissue. First, we used the TCGA data set to define CpG-rich regions flanking the SFRP1 transcription start site that were significantly more methylated in pancreatic cancer compared to normal pancreatic acinar tissue. A core CpG island was identified that exhibited abundant tumor DNA methylation and anti-correlation of SFRP1 mRNA expression. To validate our in silico results, we performed bisulfide conversion followed by DNA pyrosequencing of 28 matched formalin-fixed, paraffin-embedded (FFPE) pancreatic cancer cases and six pancreatic cancer cell lines. A defined block of seven CpG sites within the core CpG island was identified, which confirmed our in silico results by showing significantly higher SFRP1 methylation in pancreatic cancer specimens than in normal pancreatic tissue. By selecting this core CpG island, we were able to determine a median overall survival benefit for the low SFRP1 methylation group compared to the high SFRP1 methylation group (702 versus 517 days, p = 0.01) in the TCGA pancreatic cancer cohort. We propose a compact pyrosequencing assay that can be used in the future to further investigate the prognostic value of SFRP1 promoter hypermethylation in predicting pancreatic cancer chemoresistance. Therefore, instead of DNA analysis from blood (liquid biopsy), DNA easily extractable from cancer tissue blocks (FFPE material) could be used.

Silencing effects of mutant RAS signalling on transcriptomes

Mutated genes of the RAS family encoding small GTP-binding proteins drive numerous cancers, including pancreatic, colon and lung tumors. Besides the numerous effects of mutant RAS gene expression on aberrant proliferation, transformed phenotypes, metabolism, and therapy resistance, the most striking consequences of chronic RAS activation are changes of the genetic program. By performing systematic gene expression studies in cellular models that allow comparisons of pre-neoplastic with RAS-transformed cells, we and others have estimated that 7 percent or more of all transcripts are altered in conjunction with the expression of the oncogene. In this context, the number of up-regulated transcripts approximates that of down-regulated transcripts. While up-regulated transcription factors such as MYC, FOSL1, and HMGA2 have been identified and characterized as RAS-responsive drivers of the altered transcriptome, the suppressed factors have been less well studied as potential regulators of the genetic program and transformed phenotype in the breadth of their occurrence. We therefore have collected information on downregulated RAS-responsive factors and discuss their potential role as tumor suppressors that are likely to antagonize active cancer drivers. To better understand the active mechanisms that entail anti-RAS function and those that lead to loss of tumor suppressor activity, we focus on the tumor suppressor HREV107 (alias PLAAT3 [Phospholipase A and acyltransferase 3], PLA2G16 [Phospholipase A2, group XVI] and HRASLS3 [HRAS-like suppressor 3]). Inactivating HREV107 mutations in tumors are extremely rare, hence epigenetic causes modulated by the RAS pathway are likely to lead to down-regulation and loss of function.

Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction

MOTIVATION

Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system.

METHODS

In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC's convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses.

RESULTS

We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models' capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Pharmacophore-Model-Based Virtual-Screening Approaches Identified Novel Natural Molecular Candidates for Treating Human Neuroblastoma

The mortality of cancer patients with neuroblastoma is increasing due to the limited availability of specific treatment options. Few drug candidates for combating neuroblastoma have been developed, and identifying novel therapeutic candidates against the disease is an urgent issue. It has been found that muc-N protein is amplified in one-third of human neuroblastomas and expressed as an attractive drug target against the disease. The myc-N protein interferes with the bromodomain and extraterminal (BET) family proteins. Pharmacologically inhibition of the protein potently depletes MYCN in neuroblastoma cells. BET inhibitors target MYCN transcription and show therapeutic efficacy against neuroblastoma. Therefore, the study aimed to identify potential inhibitors against the BET family protein, specifically Brd4 (brodamine-containing protein 4), to hinder the activity of neuroblastoma cells. To identify effective molecular candidates against the disease, a structure-based pharmacophore model was created for the binding site of the Brd4 protein. The pharmacophore model generated from the protein Brd4 was validated to screen potential natural active compounds. The compounds identified through the pharmacophore-model-based virtual-screening process were further screened through molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, and molecular dynamics (MD) simulation approach. The pharmacophore-model-based screening process initially identified 136 compounds, further evaluated based on molecular docking, ADME analysis, and toxicity approaches, identifying four compounds with good binding affinity and lower side effects. The stability of the selected compounds was also confirmed by dynamic simulation and molecular mechanics with generalized Born and surface area solvation (MM-GBSA) methods. Finally, the study identified four natural lead compounds, ZINC2509501, ZINC2566088, ZINC1615112, and ZINC4104882, that will potentially inhibit the activity of the desired protein and help to fight against neuroblastoma and related diseases. However, further evaluations through in vitro and in vivo assays are suggested to identify their efficacy against the desired protein and disease.

White Paper: Mimetics of Class 2 Tumor Suppressor Proteins as Novel Drug Candidates for Personalized Cancer Therapy

Simple Summary

A concept is presented for a new therapeutic approach, still in its early stages, which focuses on the phenotypic mimicry (“mimesis”) of proteins encoded by highly disease-relevant class 2 tumor suppressor genes that are silenced by DNA promoter methylation. Proteins derived from tumor suppressor genes are usually considered control systems of cells against oncogenic properties. Thus they represent the brakes in the “car-of-life.” Restoring this “brake function” in tumors by administering mimetic drugs may have a significant therapeutic effect. The proposed approach could thus open up a new, hitherto unexploited area of research for the development of anticancer drugs for difficult-to-treat cancers.

Abstract

The aim of our proposed concept is to find new target structures for combating cancers with unmet medical needs. This, unfortunately, still applies to the majority of the clinically most relevant tumor entities such as, for example, liver cancer, pancreatic cancer, and many others. Current target structures almost all belong to the class of oncogenic proteins caused by tumor-specific genetic alterations, such as activating mutations, gene fusions, or gene amplifications, often referred to as cancer “driver alterations” or just “drivers.” However, restoring the lost function of tumor suppressor genes (TSGs) could also be a valid approach to treating cancer. TSG-derived proteins are usually considered as control systems of cells against oncogenic properties; thus, they represent the brakes in the “car-of-life.” Restoring these tumor-defective brakes by gene therapy has not been successful so far, with a few exceptions. It can be assumed that most TSGs are not being inactivated by genetic alteration (class 1 TSGs) but rather by epigenetic silencing (class 2 TSGs or short “C2TSGs”). Reactivation of C2TSGs in cancer therapy is being addressed by the use of DNA demethylating agents and histone deacetylase inhibitors which act on the whole cancer cell genome. These epigenetic therapies have neither been particularly successful, probably because they are “shotgun” approaches that, although acting on C2TSGs, may also reactivate epigenetically silenced oncogenic sequences in the genome. Thus, new strategies are needed to exploit the therapeutic potential of C2TSGs, which have also been named DNA methylation cancer driver genes or “DNAme drivers” recently. Here we present a concept for a new translational and therapeutic approach that focuses on the phenotypic imitation (“mimesis”) of proteins encoded by highly disease-relevant C2TSGs/DNAme drivers. Molecular knowledge on C2TSGs is used in two complementary approaches having the translational concept of defining mimetic drugs in common: First, a concept is presented how truncated and/or genetically engineered C2TSG proteins, consisting solely of domains with defined tumor suppressive function can be developed as biologicals. Second, a method is described for identifying small molecules that can mimic the effect of the C2TSG protein lost in the cancer cell. Both approaches should open up a new, previously untapped discovery space for anticancer drugs.

Three categories of similarities between the placenta and cancer that can aid cancer treatment: Cells, the microenvironment, and metabolites

Cancer is one of the most harmful diseases, while pregnancy is a common condition of females. Placenta is the most important organ for fetal growth, which has not been fully understand. It's well known that placenta and solid tumor have some similar biological behaviors. What's more, decidua, the microenvironment of placenta, and metabolism all undergo adaptive shift for healthy pregnancy. Interestingly, decidua and the tumor microenvironment (TME); metabolism changes during pregnancy and cancer cachexia all have underlying links. However, whether the close link between pregnancy and cancer can bring some new ideas to treat cancer is still unclear. So, in this review we note that pregnancy may offer clues to treat cancer related to three categories: from cell perspective, through the shared development process of the placenta and cancer; from microenvironment perspective, though the shared features of the decidua and TME; and from metabolism perspective, through shared metabolites changes during pregnancy and cancer cachexia. Firstly, comparing gene mutations of both placenta and cancer, which is the underlying mechanism of many similar biological behaviors, helps us understand the origin of cancer and find the key factors to restore tumorigenesis. Secondly, exploring how decidua affect placenta development and similarities of decidua and TME is helpful to reshape TME, then to inhibit cancer. Thirdly, we also illustrate the possibility that the altered metabolites during pregnancy may reverse cancer cachexia. So, some key molecules changed in circulation of pregnancy may help relieve cachexia and make survival with cancer realized.

Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types

BACKGROUND

The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence, there is no definitive treatment for most cancer types. This reveals the importance of developing new pipelines to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types.

RESULTS

In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in many samples to identify cancer subtypes. We apply our pipeline to 12,270 samples collected from the international cancer genome consortium, covering 19 cancer types. As a result, we identify 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways.

CONCLUSIONS

This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. Additionally, we analyze the mutational signatures for samples in each subtype, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on "gene-motif" suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer .

The vulnerable primed cancer stem cells in disguise: demystifying the role of Maspin

Epithelial-specific Maspin is widely known as a tumor suppressor. However, while the level of maspin expression is inversely correlated with tumor grade and stage, emerging clinical evidence shows a correlation between seemingly better differentiated tumor cells that express Maspin in both the nucleus and the cytoplasm, (n + c)Maspin, with a poor prognosis of many types of cancer. Biological studies demonstrate that Maspin plays an essential role in stem cell differentiation. In light of the recently established characterization of primed stem cells (P-SCs) in development, we propose, for the first time, that cancer stem cells (CSCs) also need to undergo priming (P-CSCs) before their transition to various progeny phenotypes. We envisage major differences in the steady state kinetics between P-SCs and P-CSCs. We further propose that P-CSCs of carcinoma are both marked and regulated by (n + c)Maspin. The concept of P-CSCs helps explain the apparent dichotomous relationships of (n + c)Maspin expression with cancer diagnosis and prognosis, and is supported by the evidence from mechanistic studies. We believe that the potential utility of (n + c)Maspin as a molecular marker of P-CSCs may significantly accelerate the advancement in our understanding of the genesis of tumor phenotypic plasticity in response to changes of tumor microenvironments (TME) or drug treatments. The vulnerabilities of the cellular state of (n + c)Maspin-expressing P-CSCs are also discussed as the rationale for future development of P-CSC-targeted chemotherapeutic and immunotherapeutic strategies.

Roles of the tumor suppressor inhibitor of growth family member 4 (ING4) in cancer

Inhibitor of growth family member 4 (ING4) is best known as a tumor suppressor that is frequently downregulated, deleted, or mutated in many cancers. ING4 regulates a broad array of tumor-related processes including proliferation, apoptosis, migration, autophagy, invasion, angiogenesis, DNA repair and chromatin remodeling. ING4 alters local chromatin structure by functioning as an epigenetic reader of H3K4 trimethylation histone marks (H3K4Me3) and regulating gene transcription through directing histone acetyltransferase (HAT) and histone deacetylase (HDAC) protein complexes. ING4 may serve as a useful prognostic biomarker for many cancer types and help guide treatment decisions. This review provides an overview of ING4's central functions in gene expression and summarizes current literature on the role of ING4 in cancer and its possible use in therapy.

Drivers of Gene Expression Dysregulation in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease with a poor prognosis. The functional consequences of common genetic aberrations and their roles in treatment strategies have been extensively reviewed. In addition to these genomic aberrations, consideration of non-genetic drivers of altered oncogene expression is essential to account for the diversity in PDAC phenotypes. In this review we seek to assess our current understanding of mechanisms of gene expression dysregulation. We focus on four drivers of gene expression dysregulation, including mutations, transcription factors, epigenetic regulators, and RNA stability/isoform regulation, in the context of PDAC pathogenesis. Recent studies provide much-needed insight into the role of gene expression dysregulation in dissecting tumor heterogeneity and stratifying patients for the development of personalized treatment strategies.

From multi-omics integration towards novel genomic interaction networks to identify key cancer cell line characteristics

Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene-gene and gene-DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers.

Genome wide CRISPR screening reveals a role for sialylation in the tumorigenesis and chemoresistance of acute myeloid leukemia cells

Aberrant activation of cytokine and growth factor signal transduction pathways confers enhanced survival and proliferation properties to acute myeloid leukemia (AML) cells. However, the mechanisms underlying the deregulation of signaling pathways in leukemia cells are unclear. To identify genes capable of independently supporting cytokine-independent growth, we employed a genome-wide CRISPR/Cas9-mediated loss-of-function screen in GM-CSF-dependent human AML TF-1 cells. More than 182 genes (p < 0.01) were found to suppress the cytokine-independent growth of TF-1 cells. Among the top hits, genes encoding key factors involved in sialylation biosynthesis were identified; these included CMAS, SLC35A1, NANS, and GNE. Knockout of either CMAS or SLC35A1 enabled cytokine-independent proliferation and survival of AML cells. Furthermore, NSG (NOD/SCID/IL2Rγ^-/-) mice injected with CMAS or SLC35A1-knockout TF-1 cells exhibited a shorter survival than mice injected with wild-type cells. Mechanistically, abrogation of sialylation biosynthesis in TF-1 cells induced a strong activation of ERK signaling, which sensitized cells to MEK inhibitors but conferred resistance to JAK inhibitors. Further, the surface level of α2,3-linked sialic acids was negatively correlated with the sensitivity of AML cell lines to MEK/ERK inhibitors. We also found that sialylation modulated the expression and stability of the CSF2 receptor. Together, these results demonstrate a novel role of sialylation in regulating oncogenic transformation and drug resistance development in leukemia. We propose that altered sialylation could serve as a biomarker for targeted anti-leukemic therapy.

The importance of CDC27 in cancer: molecular pathology and clinical aspects

Background

CDC27 is one of the core components of Anaphase Promoting complex/cyclosome. The main role of this protein is defined at cellular division to control cell cycle transitions. Here we review the molecular aspects that may affect CDC27 regulation from cell cycle and mitosis to cancer pathogenesis and prognosis.

Main text

It has been suggested that CDC27 may play either like a tumor suppressor gene or oncogene in different neoplasms. Divergent variations in CDC27 DNA sequence and alterations in transcription of CDC27 have been detected in different solid tumors and hematological malignancies. Elevated CDC27 expression level may increase cell proliferation, invasiveness and metastasis in some malignancies. It has been proposed that CDC27 upregulation may increase stemness in cancer stem cells. On the other hand, downregulation of CDC27 may increase the cancer cell survival, decrease radiosensitivity and increase chemoresistancy. In addition, CDC27 downregulation may stimulate efferocytosis and improve tumor microenvironment.

Conclusion

CDC27 dysregulation, either increased or decreased activity, may aggravate neoplasms. CDC27 may be suggested as a prognostic biomarker in different malignancies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01860-9.

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks

The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart.

The Potential Role of CERS1 in Autophagy Through PI3K/AKT Signaling Pathway in Hypophysoma

To explore the role and mechanism of CERS1 in hypophysoma and investigate whether CERS1 overexpression can change the autophagy process of hypophysoma, and then to explore whether CERS1’s effect was regulated by the PI3K/AKT signaling pathway. Western blot and RT-PCR were used to analyze the expression or mRNA level of CERS1 at different tissues or cell lines. Afterwards, the occurrence and development of hypophysoma in vivo and in vitro, respectively, was observed by using CERS1 overexpression by lentivirus. Finally, MK-2206 and LY294002 were applied to discuss whether the role of CERS1 was regulated by the PI3K/AKT signaling pathway. Results show that the CERS1 expression and mRNA level in tumor or AtT-20 cells were decreased. CERS1 over-expressed by lentivirus could inhibit hypophysoma development in vivo and in vitro by reducing tumor volume and weight, weakening tumor proliferation and invasion, and enhancing apoptosis. In addition, shCERS1 could reverse the process. The above results indicate that CERS1 is possibly able to enhance autophagy in hypophysoma through the PI3K/AKT signaling pathway.

The Challenge of Classifying Metastatic Cell Properties by Molecular Profiling Exemplified with Cutaneous Melanoma Cells and Their Cerebral Metastasis from Patient Derived Mouse Xenografts

The prediction of metastatic properties from molecular analyses still poses a major challenge. Here we aimed at the classification of metastasis-related cell properties by proteome profiling making use of cutaneous and brain-metastasizing variants from single melanomas sharing the same genetic ancestry. Previous experiments demonstrated that cultured cells derived from these xenografted variants maintain a stable phenotype associated with a differential metastatic behavior: The brain metastasizing variants produce more spontaneous micro-metastases than the corresponding cutaneous variants. Four corresponding pairs of cutaneous and metastatic cells were obtained from four individual patients, resulting in eight cell-lines presently investigated. Label free proteome profiling revealed significant differences between corresponding pairs of cutaneous and cerebellar metastases from the same patient. Indeed, each brain metastasizing variant expressed several apparently metastasis-associated proteomic alterations as compared with the corresponding cutaneous variant. Among the differentially expressed proteins we identified cell adhesion molecules, immune regulators, epithelial to mesenchymal transition markers, stem cell markers, redox regulators and cytokines. Similar results were observed regarding eicosanoids, considered relevant for metastasis, such as PGE2 and 12-HETE. Multiparametric morphological analysis of cells also revealed no characteristic alterations associated with the cutaneous and brain metastasis variants. However, no correct classification regarding metastatic potential was yet possible with the present data. We thus concluded that molecular profiling is able to classify cells according to known functional categories but is not yet able to predict relevant cell properties emerging from networks consisting of many interconnected molecules. The presently observed broad diversity of molecular patterns, irrespective of restricting to one tumor type and two main classes of metastasis, highlights the important need to develop meta-analysis strategies to predict cell properties from molecular profiling data. Such base knowledge will greatly support future individualized precision medicine approaches.

Combinatorial Epigenetics Impact of Polyphenols and Phytochemicals in Cancer Prevention and Therapy

Polyphenols are potent micronutrients that can be found in large quantities in various food sources and spices. These compounds, also known as phenolics due to their phenolic structure, play a vital nutrient-based role in the prevention of various diseases such as diabetes, cardiovascular diseases, neurodegenerative diseases, liver disease, and cancers. However, the function of polyphenols in disease prevention and therapy depends on their dietary consumption and biological properties. According to American Cancer Society statistics, there will be an expected rise of 23.6 million new cancer cases by 2030. Due to the severity of the increased risk, it is important to evaluate various preventive measures associated with cancer. Relatively recently, numerous studies have indicated that various dietary polyphenols and phytochemicals possess properties of modifying epigenetic mechanisms that modulate gene expression resulting in regulation of cancer. These polyphenols and phytochemicals, when administrated in a dose-dependent and combinatorial-based manner, can have an enhanced effect on epigenetic changes, which play a crucial role in cancer prevention and therapy. Hence, this review will focus on the mechanisms of combined polyphenols and phytochemicals that can impact various epigenetic modifications such as DNA methylation and histone modifications as well as regulation of non-coding miRNAs expression for treatment and prevention of various types of cancer.

Regulat-INGs in tumors and diseases: Focus on ncRNAs

ING family genes (Inhibitor of Growth) are tumor suppressor genes that play a vital role in cell homeostasis. It has been shown that their expression is lost or diminished in many cancers and other diseases. The main mechanisms by which they are regulated in oncogenesis have not yet been fully elucidated. The involvement of non-coding RNAs (ncRNAs) and in particular microRNAs (miRNAs) in post-transcriptional gene regulation is well established. miRNAs are short sequences (18-25 nucleotides) that can bind to the 3 'UTR sequence of the targeted messenger RNA (mRNA), leading to its degradation or translational repression. Interactions between the ING family and miRNAs have been described in some cancers but also in other diseases. The involvement of miRNAs in ING family regulation opens up new fields of investigation, particularly for targeted therapies. In this review, we will summarize the regulatory mechanisms at the RNA and protein level of the ING family and focus on the interactions with ncRNAs.

Cancer-specific expression quantitative loci are affected by expression dysregulation

Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.

Non plasmonic semiconductor quantum SERS probe as a pathway for in vitro cancer detection

Surface-enhanced Raman scattering (SERS)-based cancer diagnostics is an important analytical tool in early detection of cancer. Current work in SERS focuses on plasmonic nanomaterials that suffer from coagulation, selectivity, and adverse biocompatibility when used in vitro, limiting this research to stand-alone biomolecule sensing. Here we introduce a label-free, biocompatible, ZnO-based, 3D semiconductor quantum probe as a pathway for in vitro diagnosis of cancer. By reducing size of the probes to quantum scale, we observed a unique phenomenon of exponential increase in the SERS enhancement up to ~106 at nanomolar concentration. The quantum probes are decorated on a nano-dendrite platform functionalized for cell adhesion, proliferation, and label-free application. The quantum probes demonstrate discrimination of cancerous and non-cancerous cells along with biomolecular sensing of DNA, RNA, proteins and lipids in vitro. The limit of detection is up to a single-cell-level detection.

A history of exploring cancer in context

The concept that progression of cancer is regulated by interactions of cancer cells with their microenvironment was postulated by Stephen Paget over a century ago. Contemporary tumour microenvironment (TME) research focuses on the identification of tumour-interacting microenvironmental constituents, such as resident or infiltrating non-tumour cells, soluble factors and extracellular matrix components, and the large variety of mechanisms by which these constituents regulate and shape the malignant phenotype of tumour cells. In this Timeline article, we review the developmental phases of the TME paradigm since its initial description. While illuminating controversies, we discuss the importance of interactions between various microenvironmental components and tumour cells and provide an overview and assessment of therapeutic opportunities and modalities by which the TME can be targeted.

Nuclear Phosphatidylinositol-Phosphate Type I Kinase α-Coupled Star-PAP Polyadenylation Regulates Cell Invasion

Star-PAP, a nuclear phosphatidylinositol (PI) signal-regulated poly(A) polymerase (PAP), couples with type I PI phosphate kinase α (PIPKIα) and controls gene expression. We show that Star-PAP and PIPKIα together regulate 3′-end processing and expression of pre-mRNAs encoding key anti-invasive factors (KISS1R, CDH1, NME1, CDH13, FEZ1, and WIF1) in breast cancer. Consistently, the endogenous Star-PAP level is negatively correlated with the cellular invasiveness of breast cancer cells. While silencing Star-PAP or PIPKIα increases cellular invasiveness in low-invasiveness MCF7 cells, Star-PAP overexpression decreases invasiveness in highly invasive MDA-MB-231 cells in a cellular Star-PAP level-dependent manner. However, expression of the PIPKIα-noninteracting Star-PAP mutant or the phosphodeficient Star-PAP (S6A mutant) has no effect on cellular invasiveness. These results strongly indicate that PIPKIα interaction and Star-PAP S6 phosphorylation are required for Star-PAP-mediated regulation of cancer cell invasion and give specificity to target anti-invasive gene expression. Our study establishes Star-PAP–PIPKIα-mediated 3′-end processing as a key anti-invasive mechanism in breast cancer.

Epithelial Mesenchymal Transition (EMT) in Metastatic Breast Cancer in Omani Women

Breast cancer (BC) in Oman affects younger women and has a more aggressive course. Clinical and biological variables like age, pregnancy, tumor size, type, grade, receptor expression and proliferation predict disease aggression but there is no direct predictor of metastasis except lymphovascular invasion. Epithelial-mesenchymal transition (EMT) is characterized by epithelial cells losing epithelial and acquiring mesenchymal morpho-immunophenotypic characteristics. In tumors, EMT-like transitions may signify a metastatic phenotype and have features in common with cancer stem cells (CSC) which show resistance to chemotherapy. This study aimed to identify EMT and CSC phenotypes in metastatic and non-metastatic breast cancer in Omani women and their association with conventional clinico-pathological predictors of BC. In a retrospective study of ninety-six Omani women with breast cancer, the association of age, pregnancy/lactation, tumor size, type, grade, ductal carcinoma insitu (DCIS), lymphovascular invasion, hormone/ HER2 receptor expression and Ki67 proliferation index (Ki67 PI) was tested with EMT/ CSC phenotype and metastasis. Young age ≤ 40 years, lymphovascular invasion and EMT had a strong association with metastasis; CSC approached significance. Vimentin expression in tumor cells, fibronectin and MMP-11 in stroma were reliable markers of EMT; dual EMT and CSC phenotype (Vim+/ CD44+/ CD 24-/low) had a strong association with apocrine variant, basal-like tumors and triple negative cancers. EMT had a strong association with Ki67 proliferation index (PI) and CSC with HER2-like tumors and distant metastasis. These select markers may be useful in metastasis-prediction in pre-treatment biopsies.

Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes

BACKGROUND

Identification of driver genes related to certain types of cancer is an important research topic. Several systems biology approaches have been suggested, in particular for the identification of breast cancer (BRCA) related genes. Such approaches usually rely on differential gene expression and/or mutational landscape data. In some cases interaction network data is also integrated to identify cancer-related modules computationally.

RESULTS

We provide a framework for the comparative graph-theoretical analysis of networks integrating the relevant gene expression, mutations, and potein-protein interaction network data. The comparisons involve a graph-theoretical analysis of normal and tumor network pairs across all instances of a given set of breast cancer samples. The network measures under consideration are based on appropriate formulations of various centrality measures: betweenness, clustering coefficients, degree centrality, random walk distances, graph-theoretical distances, and Jaccard index centrality.

CONCLUSIONS

Among all the studied centrality-based graph-theoretical properties, we show that a betweenness-based measure differentiates BRCA genes across all normal versus tumor network pairs, than the rest of the popular centrality-based measures. The AUROC and AUPR values of the gene lists ordered with respect to the measures under study as compared to NCBI BioSystems pathway and the COSMIC database of cancer genes are the largest with the betweenness-based differentiation, followed by the measure based on degree centrality. In order to test the robustness of the suggested measures in prioritizing cancer genes, we further tested the two most promising measures, those based on betweenness and degree centralities, on randomly rewired networks. We show that both measures are quite resilient to noise in the input interaction network. We also compared the same measures against a state-of-the-art alternative disease gene prioritization method, MUFFFINN. We show that both our graph-theoretical measures outperform MUFFINN prioritizations in terms of ROC and precions/recall analysis. Finally, we filter the ordered list of the best measure, the betweenness-based differentiation, via a maximum-weight independent set formulation and investigate the top 50 genes in regards to literature verification. We show that almost all genes in the list are verified by the breast cancer literature and three genes are presented as novel genes that may potentialy be BRCA-related but missing in literature.

An involvement of phospholipase A/acyltransferase family proteins in peroxisome regulation and plasmalogen metabolism

The H-Ras-like suppressor (HRASLS) is a protein family consisting of five members in humans. Despite their discovery as tumor suppressors, we demonstrated that all these proteins are phospholipid-metabolizing enzymes, such as phospholipase (PL) A₁ /A₂ and acyltransferase. We thus proposed to rename HRASLS1-5 as PLA/acyltransferase (PLAAT)-1-5. Notably, PLAATs exhibit N-acyltransferase activity to biosynthesize N-acylated ethanolamine phospholipids, including N-acyl-plasmalogen, which serve as precursors of bioactive N-acylethanolamines. Furthermore, the overexpression of PLAAT-3 in animal cells causes disappearance of peroxisomes and a remarkable reduction in plasmalogen levels. This finding might be related to the inhibitory effect of PLAAT-3 on the chaperone activity of the peroxin PEX19. In this article, we will review our recent findings about PLAAT proteins, with special reference to their roles in peroxisome biogenesis and plasmalogen metabolism.

CanProVar 2.0: An Updated Database of Human Cancer Proteome Variation

Identification and annotation of the mutations involved in oncogenesis and tumor progression are crucial for both cancer biology and clinical applications. Previously, we developed a public resource CanProVar, a human cancer proteome variation database for storing and querying single amino acid alterations in the human cancers. Since the publication of CanProVar, extensive cancer genomics efforts have revealed the enormous genomic complexity of various types of human cancers. Thus, there is an overwhelming need for comprehensive annotation of the genomic alterations at the protein level and making such knowledge easily accessible. Here, we describe CanProVar 2.0, a significantly expanded version of CanProVar, in which the amount of cancer-related variations and noncancer specific variations was increased by about 10-fold as compared to the previous version. To facilitate the interpretation of the variations, we added to the database functional data on potential impact of the cancer-related variations on 3D protein interaction and on the differential expression of the variant-bearing proteins between cancer and normal samples. The web interface allows for flexible queries based on gene or protein IDs, cancer types, chromosome locations, or pathways. An integrated protein sequence database containing variations that can be directly used for proteomics database searching can be downloaded.

Modulation of the p53 family network by RNA-binding proteins

Since its discovery more than three decades ago, tumor suppressor p53 has been shown to play pivotal roles in both maintaining genomic integrity and tumor suppression. p53 functions as a transcription factor responding to a multitude of cellular stressors, regulating the transcription of many genes involved in cell-cycle arrest, senescence, autophagy, and apoptosis. Extensive work has revealed that p53 is one of the most commonly mutated tumor suppressor genes. The last three decades have demonstrated that p53 activity is controlled through transcriptional regulation and posttranslational modifications. However, evolving work is now uncovering that p53, and other p53 family members, are post-transcriptionally regulated by multiple RNA-binding proteins (RBPs). Understanding the regulation of p53 by RBPs may potentially open up the possibility for cancer therapeutic intervention. This review focuses on the posttranscriptional regulation of p53, and p53 family members, by RNA binding proteins and the reciprocal feedback pathways between several RNA-biding proteins modulating p53, and p53 family members.

Septin Mutations in Human Cancers

Septins are GTP-binding proteins that are evolutionarily and structurally related to the RAS oncogenes. Septin expression levels are altered in many cancers and new advances point to how abnormal septin expression may contribute to the progression of cancer. In contrast to the RAS GTPases, which are frequently mutated and actively promote tumorigenesis, little is known about the occurrence and role of septin mutations in human cancers. Here, we review septin missense mutations that are currently in the Catalog of Somatic Mutations in Cancer (COSMIC) database. The majority of septin mutations occur in tumors of the large intestine, skin, endometrium and stomach. Over 25% of the annotated mutations in SEPT2, SEPT4, and SEPT9 belong to large intestine tumors. From all septins, SEPT9 and SEPT14 exhibit the highest mutation frequencies in skin, stomach and large intestine cancers. While septin mutations occur with frequencies lower than 3%, recurring mutations in several invariant and highly conserved amino acids are found across different septin paralogs and tumor types. Interestingly, a significant number of these mutations occur in the GTP-binding pocket and septin dimerization interfaces. Future studies may determine how these somatic mutations affect septin structure and function, whether they contribute to the progression of specific cancers and if they could serve as tumor-specific biomarkers.

Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods

BACKGROUND

Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology.

METHODS

In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins.

RESULTS

We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy.

CONCLUSIONS

Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy.

Bioregulation

During the 20th century great progress was made in genetics and biochemistry, and these were combined into a molecular biological understanding of functions of macromolecules. Further great discoveries will be made about bioregulations, applicable to scientific problems such as cell development and evolution, and to illnesses including heart disease through defective control of cholesterol production, and to neurological cell-based diseases. The "War Against Cancer" is still far from won. The present generation of scientists can develop clinical applications from recent basic science discoveries.

Tumor and the microenvironment: a chance to reframe the paradigm of carcinogenesis?

The somatic mutation theory of carcinogenesis has eventually accumulated an impressive body of shortfalls and paradoxes, as admittedly claimed by its own supporters given that the cell-based approach can hardly explain the emergence of tissue-based processes, like cancer. However, experimental data and alternatives theories developed during the last decades may actually provide a new framework on which cancer research should be reframed. Such issue may be fulfilled embracing new theoretical perspectives, taking the cells-microenvironment interplay as the privileged level of observation and assuming radically different premises as well as new methodological frameworks. Within that perspective, the tumor microenvironment cannot be merely considered akin to new “factor” to be added to an already long list of “signaling factors”; microenvironment represents the physical-biochemical support of the morphogenetic field which drives epithelial cells towards differentiation and phenotype transformation, according to rules understandable only by means of a systems biology approach. That endeavour entails three fundamental aspects: general biological premises, the level of observation (i.e., the systems to which we are looking for), and the principles of biological organization that would help in integrating and understanding experimental data.