Genetic variants in the inositol phosphate metabolism pathway and risk of different types of cancer

Identification of novel genetic variants associated with oral squamous cell carcinoma (OSCC) in South-West coast of India using targeted exome sequencing

Oral squamous cell carcinoma (OSCC) is a subset of head and neck squamous cell carcinoma (HNSCC). This study explores the genetic landscape of oral squamous cell carcinoma (OSCC) in a cohort of 33 patients from Southern India using targeted exome sequencing. Our analysis revealed a diverse range of mutations across the cohort, with missense mutations being the most prevalent. Pathogenic mutations, as classified by ClinVar, exhibited significant individual variation, highlighting the heterogeneity of OSCC. Seventy-five genes were identified to harbor pathogenic or potentially pathogenic mutations, with notable recurrence in genes such as TP53, PDGFRA, and RAD50 among others. Comparison with high-frequency mutation genes in HNSCC from TCGA database revealed significant overlap, emphasizing the relevance of these mutations across different populations. Additionally, several novel mutations were identified, including those in CHD8, ITPKB, and HNF1A, shedding light on potential genetic risk factors specific to this population. Functional annotation and pathway analysis underscored the involvement of these mutated genes in various cancer-related pathways. Despite limitations such as sample size and the need for further validation, this study contributes to a deeper understanding of OSCC pathogenesis and highlights potential genetic markers for prognosis and targeted interventions, especially in the Indian context.

erarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Emerging role of inositol monophosphatase in cancer

Inositol monophosphatase (IMPase) is an enzyme with two homologs-IMPA1 and IMPA2-that is responsible for dephosphorylating myo-inositol monophosphate to generate myo-inositol. IMPase has been extensively studied in neuropsychiatric diseases and is regarded as a susceptibility gene. Recently, emerging evidence has implied that IMPase is linked to cancer development and progression and correlates with patient survival outcomes. Interestingly, whether it acts as a tumor-promoter or tumor-suppressor is inconsistent among different research studies. In this review, we summarize the latest findings on IMPase in cancer, focusing on exploring the underlying mechanisms for its pro- and anticancer roles. In addition, we discuss the potential methods of IMPase regulation in cancer cells and the possible approaches for IMPase intervention in clinical practice.

Sunitinib versus Pazopanib Dilemma in Renal Cell Carcinoma: New Insights into the In Vitro Metabolic Impact, Efficacy, and Safety

Sunitinib and pazopanib are tyrosine kinase inhibitors (TKIs) used as first-line therapy for metastatic renal cell carcinoma (RCC). Although these TKIs are associated with similar survival outcomes, some differences have been reported in their safety profiles. In this work, traditional toxicological endpoints (cell viability and growth, oxidative stress, and nuclear morphology) and 1H NMR spectroscopy-based metabolomics analysis were used to provide new insights into the cytotoxicity and metabolic mechanisms underlying sunitinib and pazopanib treatments. Tumoral (Caki-1) and non-tumoral (HK-2) human renal cells were exposed to clinically relevant concentrations of sunitinib (2 µM) or pazopanib (50 µM). Sunitinib showed selectivity for cancer cells, inhibiting proliferation, and inducing apoptotic death of Caki-1 cells, whereas pazopanib had a similar cytotoxic effect in both tumoral and non-tumoral cells. 1H-NMR metabolomics unveiled a higher impact of sunitinib on the levels of intracellular metabolites of Caki-1 cells (seven dysregulated metabolites), suggesting dysregulations on amino acid, glutathione and glycerophospholipid metabolisms. In contrast, pazopanib had a higher impact on the levels of extracellular metabolites of Caki-1 cells (seven dysregulated metabolites in culture medium), unveiling alterations on amino acid and energetic metabolisms. In HK-2 cells, sunitinib caused only a minor increase in intracellular isoleucine levels, whereas pazopanib induced several alterations on the intracellular (three dysregulated metabolites) and extracellular (three dysregulated metabolites) compartments suggesting changes on amino acid, glycerophospholipid, and energy metabolisms. Our results demonstrate that these TKIs elicit distinct cellular and metabolic responses, with sunitinib showing better in vitro efficacy against target RCC cells and lesser nephrotoxic potential than pazopanib.

Omics Analysis of Chemoresistant Triple Negative Breast Cancer Cells Reveals Novel Metabolic Vulnerabilities

The emergence of drug resistance in cancer poses the greatest hurdle for successful therapeutic results and is associated with most cancer deaths. In triple negative breast cancer (TNBC), due to the lack of specific therapeutic targets, systemic chemotherapy is at the forefront of treatments, but it only benefits a fraction of patients because of the development of resistance. Cancer cells may possess an innate resistance to chemotherapeutic agents or develop new mechanisms of acquired resistance after long-term drug exposure. Such mechanisms involve an interplay between genetic, epigenetic and metabolic alterations that enable cancer cells to evade therapy. In this work, we generated and characterized a chemoresistant TNBC cell line to be used for the investigation of mechanisms that drive resistance to paclitaxel. Transcriptomic analysis highlighted the important role of metabolic-associated pathways in the resistant cells, prompting us to employ 1H-NMR to explore the metabolome and lipidome of these cells. We identified and described herein numerous metabolites and lipids that were significantly altered in the resistant cells. Integrated analysis of our omics data revealed MSMO1, an intermediate enzyme of cholesterol biosynthesis, as a novel mediator of chemoresistance in TNBC. Overall, our data provide a critical insight into the metabolic adaptations that accompany acquired resistance in TNBC and pinpoint potential new targets.

Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

Ovarian cancer (OV) is a complex gynecological disease, and its molecular characteristics are not clear. In this study, the molecular characteristics of OV subtypes based on metabolic genes were explored through the comprehensive analysis of genomic data. A set of transcriptome data of 2752 known metabolic genes was used as a seed for performing non negative matrix factorization (NMF) clustering. Three subtypes of OV (C1, C2 and C3) were found in analysis. The proportion of various immune cells in C1 was higher than that in C2 and C3 subtypes. The expression level of immune checkpoint genes TNFRSF9 in C1 was higher than that of other subtypes. The activation scores of cell cycle, RTK-RAS, Wnt and angiogenesis pathway and ESTIMATE immune scores in C1 group were higher than those in C2 and C3 groups. In the validation set, grade was significantly correlated with OV subtype C1. Functional analysis showed that the extracellular matrix related items in C1 subtype were significantly different from other subtypes. Drug sensitivity analysis showed that C2 subtype was more sensitive to immunotherapy. Survival analysis of differential genes showed that the expression of PXDN and CXCL11 was significantly correlated with survival. The results of tissue microarray immunohistochemistry showed that the expression of PXDN was significantly correlated with tumor size and pathological grade. Based on the genomics of metabolic genes, a new OV typing method was developed, which improved our understanding of the molecular characteristics of human OV.

Monocarboxylate Transporter-2 Expression Restricts Tumor Growth in a Murine Model of Lung Cancer: A Multi-Omic Analysis

Monocarboxylate transporter 2 (MCT2) is a major high-affinity pyruvate transporter encoded by the SLC16A7 gene, and is associated with glucose metabolism and cancer. Changes in the gut microbiota and host immune system are associated with many diseases, including cancer. Using conditionally expressed MCT2 in mice and the TC1 lung carcinoma model, we examined the effects of MCT2 on lung cancer tumor growth and local invasion, while also evaluating potential effects on fecal microbiome, plasma metabolome, and bulk RNA-sequencing of tumor macrophages. Conditional MCT2 mice were generated in our laboratory using MCT2^loxP mouse intercrossed with mCre-Tg mouse to generate MCT2^loxP/loxP; Cre⁺ mouse (MCT2 KO). Male MCT2 KO mice (8 weeks old) were treated with tamoxifen (0.18 mg/g BW) KO or vehicle (CO), and then injected with mouse lung carcinoma TC1 cells (10 × 10⁵/mouse) in the left flank. Body weight, tumor size and weight, and local tumor invasion were assessed. Fecal DNA samples were extracted using PowerFecal kits and bacterial 16S rRNA amplicons were also performed. Fecal and plasma samples were used for GC−MS Polar, as well as non-targeted UHPLC-MS/MS, and tumor-associated macrophages (TAMs) were subjected to bulk RNAseq. Tamoxifen-treated MCT2 KO mice showed significantly higher tumor weight and size, as well as evidence of local invasion beyond the capsule compared with the controls. PCoA and hierarchical clustering analyses of the fecal and plasma metabolomics, as well as microbiota, revealed a distinct separation between the two groups. KO TAMs showed distinct metabolic pathways including the Acetyl-coA metabolic process, activation of immune response, b-cell activation and differentiation, cAMP-mediated signaling, glucose and glutamate processes, and T-cell differentiation and response to oxidative stress. Multi-Omic approaches reveal a substantial role for MCT2 in the host response to TC1 lung carcinoma that may involve alterations in the gut and systemic metabolome, along with TAM-related metabolic pathway. These findings provide initial opportunities for potential delineation of oncometabolic immunomodulatory therapeutic approaches.

Identifying cancer patient subgroups by finding co-modules from the driver mutation profiles and downstream gene expression profiles

Identifying cancer subtypes shed new light on effective personalized cancer medicine, future therapeutic strategies and minimizing treatment-related costs. Recently, there are many clustering methods have been proposed in categorizing cancer patients. However, these methods still fail to fully use the prior known biological information in the model designing process to improve precision and efficiency. It is acknowledged that the driver gene always regulates its downstream genes in the net-work to perform a certain function. By analyzing the known clinic cancer subtype data, we found some special co-pathways between the driver genes and the downstream genes in the cancer patients of the same subgroup. Hence, we proposed a novel model named DDCMNMF(Driver and Downstream gene Co-Module Assisted Multiple Non-negative Matrix Factorization model) that first stratify cancer sub-types by identifying co-modules of driver genes and downstream genes. We applied our model on lung and breast cancer datasets and compared it with the other four state-of-the-art models. The final results show that our model could identify the cancer subtypes with high compactness and separateness and achieve a high degree of consistency with the known cancer subtypes. The survival time analysis further proves the significant clinical characteristic of identified cancer subgroups by our model.

Analysis of the Protein-Protein Interaction Network Identifying c-Met as a Target of Gigantol in the Suppression of Lung Cancer Metastasis

BACKGROUND/AIM

c-Met (mesenchymal-epithelial transition factor) facilitates cancer progression and is recognized as a promising drug target. The molecular target of gigantol from Dendrobium draconis in suppressing cancer metastasis is largely unknown.

MATERIALS AND METHODS

Proteins affected by gigantol treatment were subjected to proteomic and bioinformatic analysis. Protein-Protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes (STRING). The Kyoto Encyclopedia of Genes and Genomes (KEGG) database and hub gene were used to enrich the dominant pathways. Western blot analysis and immunofluorescence were used to validate the effect of gigantol on the target protein and signaling.

RESULTS

Gigantol down-regulates 41 adhesion proteins and 39-migratory proteins, while it up-regulates 30 adhesion-related proteins and 22 proteins controlling cell migration. The key components of our constructed PPI network comprised 41 proteins of cell adhesion enriched in 40 nodes with 25 edges, 39 proteins of cell migration enriched in 39 nodes with 76 edges in down-regulated proteins, 30 proteins of cell adhesion enriched in 30 nodes with 21 edges, and 22 proteins of cell migration enriched in 22 nodes with 22 edges in up-regulated protein. c-Met was identified as a central protein of the PPI network in the largest degree. KEGG mapper further suggested that c-Met, PI3K, and AKT were the regulatory proteins affected by gigantol. To confirm, the effects of gigantol on c-Met, the p-PI3K, PI3K, p-AKT, and AKT proteins were investigated by western blotting and the results showed a consistent effect of gigantol in the suppression of the c-Met/PI3K/AKT signal. Next, immunofluorescence showed a dramatic decrease in c-Met, PI3K and AKT activation in response to gigantol.

CONCLUSION

c-Met is an important target of gigantol treatment in lung cancer cells. Gigantol suppresses metastasis-related cell motility through decreasing c-Met resulting in PI3K/AKT signaling disruption.

Plasma metabolomic profile associated with fatigue in cancer patients

BACKGROUND

Metabolomics is the newest -omics methodology and allows for a functional snapshot of the biochemical activity and cellular state. The goal of this study is to characterize metabolomic profiles associated with cancer-related fatigue, a debilitating symptom commonly reported by oncology patients.

METHODS

Untargeted ultrahigh performance liquid chromatography/mass spectrometry metabolomics approach was used to identify metabolites in plasma samples collected from a total of 197 participants with or without cancer. Partial least squares-discriminant analysis (PLS-DA) was used to identify discriminant metabolite features, and diagnostic performance of selected classifiers was quantified using area under the receiver operating characteristics (AUROC) curve analysis. Pathway enrichment analysis was performed using Fisher's exact test and the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database.

FINDINGS

The global metabolomics approach yielded a total of 1120 compounds of known identity. Significant metabolic pathways unique to fatigued cancer versus control groups included sphingolipid metabolism, histidine metabolism, and cysteine and methionine metabolism. Significant pathways unique to non-fatigued cancer versus control groups included inositol phosphate metabolism, primary bile acid biosynthesis, ascorbate and aldarate metabolism, starch and sucrose metabolism, and pentose and glucuronate interconversions. Pathways shared between the two comparisons included caffeine metabolism, tyrosine metabolism, steroid hormone biosynthesis, sulfur metabolism, and phenylalanine metabolism.

CONCLUSIONS

We found significant metabolomic profile differences associated with cancer-related fatigue. By comparing metabolic signatures unique to fatigued cancer patients with metabolites associated with, but not unique to, fatigued cancer individuals (overlap pathways) and metabolites associated with cancer but not fatigue, we provided a broad view of the metabolic phenotype of cancer-related fatigue.

MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

During tumor progression, cancer cells rewire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture conditions. For MCTS, we selected two-time points during their development to recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA expression patterns associated with metabolism. As a result, we identified a set of new direct putative regulatory interactions between miRNAs and metabolic mRNAs representative for proliferative and quiescent stages. Notably, our study allows us to suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, we found that the overexpression of several miRNAs could directly overturn the expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this set of miRNAs downregulates their expression in the quiescent stage, the same set is upregulated in proliferative stages. This last finding suggests an additional metabolic switch of the above mentioned metabolic pathways between the quiescent and proliferative stages. Our results contribute to a better understanding of how miRNAs modulate the metabolic landscape in breast cancer MCTS, which eventually will help to design new strategies to mitigate cancer phenotype.

Genetic analysis of endometriosis and depression identifies shared loci and implicates causal links with gastric mucosa abnormality

Evidence from observational studies indicates that endometriosis and depression often co-occur. However, conflicting evidence exists, and the etiology as well as biological mechanisms underlying their comorbidity remain unknown. Utilizing genome-wide association study (GWAS) data, we comprehensively assessed the relationship between endometriosis and depression. Single nucleotide polymorphism effect concordance analysis (SECA) found a significant genetic overlap between endometriosis and depression (P_{Fsig-permuted} = 9.99 × 10^-4). Linkage disequilibrium score regression (LDSC) analysis estimated a positive and highly significant genetic correlation between the two traits (r_G = 0.27, P = 8.85 × 10^-27). A meta-analysis of endometriosis and depression GWAS (sample size = 709,111), identified 20 independent genome-wide significant loci (P < 5 × 10^-8), of which eight are novel. Mendelian randomization analysis (MR) suggests a causal effect of depression on endometriosis. Combining gene-based association results across endometriosis and depression GWAS, we identified 22 genes with a genome-wide significant Fisher's combined P value (FCP_gene < 2.75 × 10^-6). Genes with a nominal gene-based association (P_gene < 0.05) were significantly enriched across endometriosis and depression (P_{binomial-test} = 2.90 × 10^-4). Also, genes overlapping the two traits at P_gene < 0.1 (P_{binomial-test} = 1.31 × 10^-5) were significantly enriched for the biological pathways 'cell-cell adhesion', 'inositol phosphate metabolism', 'Hippo-Merlin signaling dysregulation' and 'gastric mucosa abnormality'. These results reveal a shared genetic etiology for endometriosis and depression. Indeed, additional analyses found evidence of a causal association between each of endometriosis and depression and at least one abnormal condition of gastric mucosa. Our study confirms the comorbidity of endometriosis and depression, implicates links with gastric mucosa abnormalities in their causal pathways and reveals potential therapeutic targets for further investigation.

Downregulated SPINK4 is associated with poor survival in colorectal cancer

BACKGROUND

SPINK4 is known as a gastrointestinal peptide in the gastrointestinal tract and is abundantly expressed in human goblet cells. The clinical significance of SPINK4 in colorectal cancer (CRC) is largely unknown.

METHODS

We retrieved the expression data of 1168 CRC patients from 3 Gene Expression Omnibus (GEO) datasets (GSE24551, GSE39582, GSE32323) and The Cancer Genome Atlas (TCGA) to compare the expression level of SPINK4 between CRC tissues and normal colorectal tissues and to evaluate its value in predicting the survival of CRC patients. At the protein level, these results were further confirmed by data mining in the Human Protein Atlas and by immunohistochemical staining of samples from 81 CRC cases in our own center.

RESULTS

SPINK4 expression was downregulated in CRC compared with that in normal tissues, and decreased SPINK4 expression at both the mRNA and protein levels was associated with poor prognosis in CRC patients from all 3 GEO datasets, the TCGA database and our cohort. Additionally, lower SPINK4 expression was significantly related to higher TNM stage. Moreover, in multivariate regression, SPINK4 was confirmed as an independent indicator of poor survival in CRC patients in all databases and in our own cohort.

CONCLUSIONS

We concluded that reduced expression of SPINK4 relates to poor survival in CRC, functioning as a novel indicator.

Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer

Background

The interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by integrating of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease.

Results

We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration (https://navicell.curie.fr/pages/maps_ReconMap%202.html). In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated networks ACSN-ReconMap 2.0 are accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated networks were applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified.

Conclusions

As knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 networks will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2682-z) contains supplementary material, which is available to authorized users.

Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail. In this study, we utilize publicly available single cell and bulk RNA sequencing datasets of breast cancer cells from primary tumors and immortalized cell lines. Breast cancer is very heterogeneous with well defined molecular subtypes and was therefore chosen for this study. RNA-seq data were explored in terms of genes, isoforms abundance and splicing events. The study was conducted from an average based approach (gene level expression) to detailed and deeper ones (isoforms abundance/splicing events) to perform a comparative analysis, and, thus, highlight the importance of the splicing machinery in defining the tumor heterogeneity. Moreover, here we demonstrate how the investigation of gene isoforms expression can help to identify the appropriate in vitro models. We furthermore extracted marker isoforms, and alternatively spliced genes between and within the different single cell populations to improve the classification of the breast cancer subtypes.

Whole-genome de novo sequencing reveals unique genes that contributed to the adaptive evolution of the Mikado pheasant

Background

The Mikado pheasant (Syrmaticus mikado) is a nearly endangered species indigenous to high-altitude regions of Taiwan. This pheasant provides an opportunity to investigate evolutionary processes following geographic isolation. Currently, the genetic background and adaptive evolution of the Mikado pheasant remain unclear.

Results

We present the draft genome of the Mikado pheasant, which consists of 1.04 Gb of DNA and 15,972 annotated protein-coding genes. The Mikado pheasant displays expansion and positive selection of genes related to features that contribute to its adaptive evolution, such as energy metabolism, oxygen transport, hemoglobin binding, radiation response, immune response, and DNA repair. To investigate the molecular evolution of the major histocompatibility complex (MHC) across several avian species, 39 putative genes spanning 227 kb on a contiguous region were annotated and manually curated. The MHC loci of the pheasant revealed a high level of synteny, several rapidly evolving genes, and inverse regions compared to the same loci in the chicken. The complete mitochondrial genome was also sequenced, assembled, and compared against four long-tailed pheasants. The results from molecular clock analysis suggest that ancestors of the Mikado pheasant migrated from the north to Taiwan about 3.47 million years ago.

Conclusions

This study provides a valuable genomic resource for the Mikado pheasant, insights into its adaptation to high altitude, and the evolutionary history of the genus Syrmaticus, which could potentially be useful for future studies that investigate molecular evolution, genomics, ecology, and immunogenetics.

L1 retrotransposition is a common feature of mammalian hepatocarcinogenesis

The retrotransposon Long Interspersed Element 1 (LINE-1 or L1) is a continuing source of germline and somatic mutagenesis in mammals. Deregulated L1 activity is a hallmark of cancer, and L1 mutagenesis has been described in numerous human malignancies. We previously employed retrotransposon capture sequencing (RC-seq) to analyze hepatocellular carcinoma (HCC) samples from patients infected with hepatitis B or hepatitis C virus and identified L1 variants responsible for activating oncogenic pathways. Here, we have applied RC-seq and whole-genome sequencing (WGS) to an Abcb4 (Mdr2)-/- mouse model of hepatic carcinogenesis and demonstrated for the first time that L1 mobilization occurs in murine tumors. In 12 HCC nodules obtained from 10 animals, we validated four somatic L1 insertions by PCR and capillary sequencing, including TF subfamily elements, and one GF subfamily example. One of the TF insertions carried a 3' transduction, allowing us to identify its donor L1 and to demonstrate that this full-length TF element retained retrotransposition capacity in cultured cancer cells. Using RC-seq, we also identified eight tumor-specific L1 insertions from 25 HCC patients with a history of alcohol abuse. Finally, we used RC-seq and WGS to identify three tumor-specific L1 insertions among 10 intra-hepatic cholangiocarcinoma (ICC) patients, including one insertion traced to a donor L1 on Chromosome 22 known to be highly active in other cancers. This study reveals L1 mobilization as a common feature of hepatocarcinogenesis in mammals, demonstrating that the phenomenon is not restricted to human viral HCC etiologies and is encountered in murine liver tumors.

PLCE1 polymorphisms and expression combined with serum AFP level predicts survival of HBV-related hepatocellular carcinoma patients after hepatectomy

Polymorphisms in the phospholipase C epsilon (PLCE) 1 gene play a crucial role in the development and progression of several types of cancer. The present study investigated the prognostic significance of PLCE1 gene polymorphisms and expression combined with serum α-fetoprotein (AFP) level in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Single nucleotide polymorphisms were genotyped by sequencing DNA isolated from surgically resected tumor samples of 421 HBV-related HCC patients, and expression profiles were generated based on the GSE14520 dataset. A joint-effects analysis of PLCE1 haplotypes (Ars2274223Crs3765524; Grs2274223Trs3765524) with AFP level stratified at 20 ng/ml showed a significant association with overall survival(OS) of HBV-related HCC patients(log-rank P=0.0003). Patients with AC and GT haplotypes with AFP level ≥ 20 ng/ml had an increased risk of death as compared to those with the AC haplotype and AFP level < 20 ng/ml (adjusted P=0.029 and 0.041, respectively). Patients with the GT haplotype and AFP level < 20 ng/ml also had an increased risk of death, although with a non-significant P value (adjusted P=0.092). Joint-effects analysis of PLCE1 mRNA expression with serum AFP level stratified at 300 ng/ml was significantly associated with HBV-related HCC recurrence and OS. Our results demonstrate that PLCE1 haplotypes (including rs2274223 and rs3765524) and expression combined with serum AFP level may predict postoperative outcome of HBV-related HCC patients.

Genetic variants in the histone methylation and acetylation pathway and their risks in eight types of cancers

OBJECTIVES

The histone methylation and acetylation pathway genes regulate cell growth and survival. Aberrations in this pathway are implicated in a variety of cancers. This study aimed to identify germline genetic variants in histone methylation and acetylation pathway genes that may contribute to risk in eight types of cancers and to explore the relation between the whole pathway and their risks in these types of cancers.

METHODS

Germline genetic variants in 89 genes in the histone methylation and acetylation pathway were explored. Gene-based and pathway-based associations with eight types of cancers were analyzed using logistic regression models and the permutation-based adaptive rank-truncated product method, respectively.

RESULTS

Gene-level associations revealed that genetic variants in 45 genes were significantly associated with the risk of cancer. The total histone methylation and acetylation pathway was significantly associated with the risk of esophageal squamous cell carcinoma (P = 0.0492) and prostate (P = 0.0038), lung (P = 0.00015), and bladder cancer (P = 0.00135), but not with breast (P = 0.182), pancreatic (P = 0.336) and gastric cancer (P = 0.347) and renal cell carcinoma (P =0.828).

CONCLUSIONS

Our study suggested there is an association between germline genetic variation at the overall histone methylation and acetylation pathway level and some individual genes with cancer risk. Further studies are needed to validate these relations and to explore relative mechanisms.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Association between single nucleotide polymorphisms in the PI3K/AKT/mTOR pathway and bladder cancer risk in a sample of Iranian population

In the past few years several investigations have focused on the role of PI3K/AKT/mTOR pathway and its deregulations in different cancers. This study aimed to examine genetic polymorphisms of this pathway in bladder cancer (BC). In this case-control study, 235 patients with pathologically confirmed bladder cancer and 254 control subjects were examined. PIK3CA, AKT1 and mTOR variants were analyzed using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The findings proposed that the PIK3CA rs6443624 SNP significantly decreased the risk of BC (OR=0.44, 95 % CI=0.30-0.65, p<0.0001 CA vs CC; OR=0.35, 95 % CI=0.16-0.78, p=0.0107, AA vs CC; OR=0.60, 95 % CI=0.46-0.79, p=0.0002, A vs T). The AKT1 rs2498801 variant is associated with a decreased risk of BC (OR=0.57, 95 % CI=0.39-0.82, p=0.003, AG vs AA; OR=0.74, 95 % CI=0.56-0.97, p=0.032, G vs A) while, AKT1 rs1130233 polymorphism considerably increased the risk of BC (OR=3.70, 95 % CI=2.52-5.43, p<0.0001, GA vs GG; OR=5.81, 95 % CI=1.53-21.97, p=0.010, AA vs GG; OR=2.71, 95 % CI=1.98-3.70, p<0.0001, A vs G). Additionally, mTOR rs2295080 variant notably increased the risk of BC (OR=2.25, 95 % CI=1.50-3.38, p<0.0001, GT vs GG; OR=4.75, 95 % CI=2.80-8.06, p<0.0001, TT vs GG; OR=3.10, 95 % CI=2.34-4.10, p<0.0001, T vs G). None of the other examined polymorphisms (AKT1 rs1130214, AKT1 rs3730358, mTOR rs1883965) revealed significant association with BC. In conclusion, our findings suggest that PIK3CA rs6443624, AKT1 rs2498801, AKT1 rs1130233, as well mTOR rs2295080 polymorphism may be related to bladder cancer development in a sample of Iranian population. Validation of our findings in larger sample sizes of different ethnicities would provide evidence on the role of variants of PI3K/AKT/mTOR pathway in developing BC.

miR-543 functions as a tumor suppressor in glioma in vitro and in vivo

Gliomas are the most common primary central nervous system tumors and account for approximately 80% of malignant brain tumors. MicroRNAs (miRNAs) are a class of small non-coding, regulatory RNA molecules that mediate the expression levels of specific proteins. As a member of the miRNA family, miR-543 plays a tumor suppressive or an oncogenic role in different types of tumors. However, the expression and role of miR-543 in glioma remain unknown. In the present study, the expression level of miR-543 in glioma cell lines and tissues was investigated. A series of in vitro and in vivo experiments was then performed to elucidate the function of miR-543 in glioma. Moreover, proteomic profiling was applied in this study to determine the landscape of differentially expressed proteins associated with miR-543-mediated carcinogenesis in glioma. We found that the expression level of miR-543 was greatly downregulated in glioma cell lines and tissues. Furthermore, the expression level of miR-543 was negatively associated with high-grade glioma. Functional studies demonstrated that miR-543 in glioma cells induced apoptosis and inhibited growth, the cell cycle, migration and invasion. In addition, the in vivo study showed that miR-543 suppressed tumorigenicity of glioma cells. In the present study, a label-free quantitative proteomic approach was performed and 339 proteins were identified as dysregulated after miR-543 was overexpressed. Among these dysregulated proteins, 165 were upregulated and 174 were downregulated. Moreover, multiple pathways were significantly enriched and were probably involved in miR-543-mediated tumorigenesis, including RNA degradation and the inositol phosphate metabolism pathway. In conclusion, miR-543 may function as a tumor suppressor in glioma and may serve as a future therapeutic target in therapy for patients with glioma.

The controversial role of phospholipase C epsilon (PLCε) in cancer development and progression

The phospholipase C (PLC) enzymes are important regulators of membrane phospholipid metabolism. PLC proteins can be activated by the receptor tyrosine kinases (RTK) or G-protein coupled receptors (GPCR) in response to the different extracellular stimuli including hormones and growth factors. Activated PLC enzymes hydrolyze phosphoinositides to increase the intracellular level of Ca²⁺ and produce diacylglycerol, which are important mediators of the intracellular signaling transduction. PLC family includes 13 isozymes belonging to 6 subfamilies according to their domain structures and functions. Although importance of PLC enzymes for key cellular functions is well established, the PLC proteins belonging to the ε, ζ and η subfamilies were identified and characterized only during the last decade. As a largest known PLC protein, PLCε is involved in a variety of signaling pathways and controls different cellular properties. Nevertheless, its role in carcinogenesis remains elusive.

The aim of this review is to provide a comprehensive and up-to-date overview of the experimental and clinical data about the role of PLCε in the development and progression of the different types of human and experimental tumors.

Gene expression profile of human esophageal squamous carcinoma cell line TE-1

Esophageal squamous cell carcinoma (ESCC) is one of the most common and deadly causes of cancer worldwide. However, to date, the mechanisms underlying its pathogenesis remain unclear. The present study investigated the gene expression profile of human esophageal cancer cell line TE-1, a cell model for ESCC, to gain insight to the genetic regulation of this disease. Human esophageal cancer TE-1 cells and normal esophageal HET-1A cells were cultured for isolation of total RNA. Differential expression of RNA transcripts was assessed using the Agilent 4×44 K microarray, combined with real-time PCR (qRT-PCR) for validation. Classification and function of the differential genes were illustrated by bioinformatics processing including hierarchical clustering and gene ontology (GO) analysis. We identified 4,986 transcripts with differential expression (fold-change ≥1.5, P<0.05), including 2,368 up-regulated and 2,618 down-regulated transcripts. GO analysis showed that the dysregulated transcripts were associated with biological process, cellular component, and molecular function. After bioinformatic analysis of significantly regulated signaling pathways, we found these transcripts may target 35 gene pathways, including p53 signaling, glioma, ubiquitin-mediated proteolysis, insulin signaling, cell cycle, inositol phosphate metabolism, mTOR signaling, and MAPK signaling. The differentially expressed transcripts were screened between the esophageal cancer cell line TE-1 and normal esophageal cell line HET-1A, as well as their target gene pathways. Further data mining is related to prevention and treatment of esophageal cancer.