Hypermutation of DPYD Deregulates Pyrimidine Metabolism and Promotes Malignant Progression

A metabolomics approach reveals metabolic disturbance of human cholangiocarcinoma cells after parthenolide treatment

ETHNOPHARMACOLOGICAL RELEVANCE

Tanacetum parthenium (L.) Schultz-Bip, commonly known as feverfew, has been traditionally used to treat fever, migraines, rheumatoid arthritis, and cancer. Parthenolide (PTL), the main bioactive ingredient isolated from the shoots of feverfew, is a sesquiterpene lactone with anti-inflammatory and antitumor properties. Previous studies showed that PTL exerts anticancer activity in various cancers, including hepatoma, cholangiocarcinoma, acute myeloid leukemia, breast, prostate, and colorectal cancer. However, the metabolic mechanism underlying the anticancer effect of PTL remains poorly understood.

AIM OF THE STUDY

To explore the anticancer activity and underlying mechanism of PTL in human cholangiocarcinoma cells.

MATERIAL AND METHODS

In this investigation, the effects and mechanisms of PTL on human cholangiocarcinoma cells were investigated via a liquid chromatography/mass spectrometry (LC/MS)-based metabolomics approach. First, cell proliferation and apoptosis were evaluated using cell counting kit-8 (CCK-8), flow cytometry analysis, and western blotting. Then, LC/MS-based metabolic profiling along with orthogonal partial least-squares discriminant analysis (OPLS-DA) has been constructed to distinguish the metabolic changes between the negative control group and the PTL-treated group in TFK1 cells. Next, enzyme-linked immunosorbent assay (ELISA) was applied to investigate the changes of metabolic enzymes associated with significantly alerted metabolites. Finally, the metabolic network related to key metabolic enzymes, metabolites, and metabolic pathways was established using MetaboAnalyst 5.0 and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database.

RESULTS

PTL treatment could induce the proliferation inhibition and apoptosis of TFK1 in a concentration-dependent manner. Forty-three potential biomarkers associated with the antitumor effect of PTL were identified, which primarily related to glutamine and glutamate metabolism, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, arginine and proline metabolism, glutathione metabolism, nicotinate and nicotinamide metabolism, pyrimidine metabolism, fatty acid metabolism, phospholipid catabolism, and sphingolipid metabolism. Pathway analysis of upstream and downstream metabolites, we found three key metabolic enzymes, including glutaminase (GLS), γ-glutamyl transpeptidase (GGT), and carnitine palmitoyltransferase 1 (CPT1), which mainly involved in glutamine and glutamate metabolism, glutathione metabolism, and fatty acid metabolism. The changes of metabolic enzymes associated with significantly alerted metabolites were consistent with the levels of metabolites, and the metabolic network related to key metabolic enzymes, metabolites, and metabolic pathways was established. PTL may exert its antitumor effect against cholangiocarcinoma by disturbing metabolic pathways. Furthermore, we selected two positive control agents that are considered as first-line chemotherapy standards in cholangiocarcinoma therapy to verify the reliability and accuracy of our metabolomic study on PTL.

CONCLUSION

This research enhanced our comprehension of the metabolic profiling and mechanism of PTL treatment on cholangiocarcinoma cells, which provided some references for further research into the anti-cancer mechanisms of other drugs.

Integrative analyses of N6-methyladenosine-associated single-nucleotide polymorphisms (m6A-SNPs) identify tumor suppressor gene AK9 in lung cancer

N6 -methyladenosine (m6 A) modification has been identified as one of the most important epigenetic regulation mechanisms in the development of human cancers. However, the association between m6 A-associated single-nucleotide polymorphisms (m6 A-SNPs) and lung cancer risk remains largely unknown. Here, we identified m6 A-SNPs and examined the association of these m6 A-SNPs with lung cancer risk in 13,793 lung cancer cases and 14,027 controls. In silico functional annotation was used to identify causal m6 A-SNPs and target genes. Furthermore, methylated RNA immunoprecipitation and quantitative real-time polymerase chain reaction (MeRIP-qPCR) assay was performed to assess the m6 A modification level of different genotypes of the causal SNP. In vitro assays were performed to validate the potential role of the target gene in lung cancer. A total of 8794 m6 A-SNPs were detected, among which 397 SNPs in nine susceptibility loci were associated with lung cancer risk, including six novel loci. Bioinformatics analyses indicated that rs1321328 in 6q21 was located around the m6 A modification site of AK9 and significantly reduced AK9 expression (β = -0.15, p = 2.78 × 10-8 ). Moreover, AK9 was significantly downregulated in lung cancer tissues than that in adjacent normal tissues of samples from the Cancer Genome Atlas and Nanjing Lung Cancer Cohort. MeRIP-qPCR assay suggested that C allele of rs1321328 could significantly decrease the m6 A modification level of AK9 compared with G allele. In vitro assays verified the tumor-suppressing role of AK9 in lung cancer. These findings shed light on the pathogenic mechanism of lung cancer susceptibility loci linked with m6 A modification.

Identification of DNA repair gene signature and potential molecular subtypes in hepatocellular carcinoma

DNA repair is a critical factor in tumor progression as it impacts tumor mutational burden, genome stability, PD-L1 expression, immunotherapy response, and tumor-infiltrating lymphocytes (TILs). In this study, we present a prognostic model for hepatocellular carcinoma (HCC) that utilizes genes related to the DNA damage response (DDR). Patients were stratified based on their risk score, and groups with lower risk scores demonstrated better survival rates compared to those with higher risk scores. The prognostic model's accuracy in predicting 1-, 3-, and 5-year survival rates for HCC patients was analyzed using receiver operator curve analysis (ROC). Results showed good accuracy in predicting survival rates. Additionally, we evaluated the prognostic model's potential as an independent factor for HCC prognosis, along with tumor stage. Furthermore, nomogram was employed to determine the overall survival year of patients with HCC based on this independent factor. Gene set enrichment analysis (GSEA) revealed that in the high-risk group, apoptosis, cell cycle, MAPK, mTOR, and WNT cascades were highly enriched. We used training and validation datasets to identify potential molecular subtypes of HCC based on the expression of DDR genes. The two subtypes differed in terms of checkpoint receptors for immunity and immune cell filtration capacity.Collectively, our study identified potential biomarkers of HCC prognosis, providing novel insights into the molecular mechanisms underlying HCC.

Structural investigation of pathogenic variants in dihydropyrimidinase using molecular dynamics simulations

Dihydropyrimidinase (DHP) is an enzyme that catabolizes the degradation of pyrimidine and fluoropyrimidine drugs such as 5-fluorouracil. DHP deficiency triggers various clinical symptoms and increases the risk of fluoropyrimidine drug toxicity. Various pathogenic variants of DHP cause DHP deficiency, and their catalytic activities have been well studied. However, the three-dimensional structures of DHP variants have not been clarified. In this study, we investigated the effects of mutations on DHP structures using the molecular dynamics simulations. Simulations of the wild type and 10 variants were performed and compared. In the T68R, D81G, G278D, and L337P variants, the flexibilities of structures related to the interaction for oligomer formation increased in comparison with those of the wild type. W117R, T343A, and R412 M mutations affected the structures of stereochemistry gate loops or the substrate-binding pocket. The three-dimensional structures of W360R and G435R variants were suggested to collapse. On the other hand, only slight structural changes were observed in the R181W variant, whose experimentally observed activity was similar to that of the wild type. The computational results are expected to clarify the relationship between clinical mutations and structural effects of drug-metabolizing enzymes.

The Somatic Mutation Landscape of UDP-Glycosyltransferase (UGT) Genes in Human Cancers

The human UDP-glycosyltransferase (UGTs) superfamily has a critical role in the metabolism of anticancer drugs and numerous pro/anti-cancer molecules (e.g., steroids, lipids, fatty acids, bile acids and carcinogens). Recent studies have shown wide and abundant expression of UGT genes in human cancers. However, the extent to which UGT genes acquire somatic mutations within tumors remains to be systematically investigated. In the present study, our comprehensive analysis of the somatic mutation profiles of 10,069 tumors from 33 different TCGA cancer types identified 3427 somatic mutations in UGT genes. Overall, nearly 18% (1802/10,069) of the assessed tumors had mutations in UGT genes with huge variations in mutation frequency across different cancer types, ranging from over 25% in five cancers (COAD, LUAD, LUSC, SKCM and UCSC) to less than 5% in eight cancers (LAML, MESO, PCPG, PAAD, PRAD, TGCT, THYM and UVM). All 22 UGT genes showed somatic mutations in tumors, with UGT2B4, UGT3A1 and UGT3A2 showing the largest number of mutations (289, 307 and 255 mutations, respectively). Nearly 65% (2260/3427) of the mutations were missense, frame-shift and nonsense mutations that have been predicted to code for variant UGT proteins. Furthermore, about 10% (362/3427) of the mutations occurred in non-coding regions (5' UTR, 3' UTR and splice sites) that may be able to alter the efficiency of translation initiation, miRNA regulation or the splicing of UGT transcripts. In conclusion, our data show widespread somatic mutations of UGT genes in human cancers that may affect the capacity of cancer cells to metabolize anticancer drugs and endobiotics that control pro/anti-cancer signaling pathways. This highlights their potential utility as biomarkers for predicting therapeutic efficacy and clinical outcomes.

Clinical and Prognostic Value of PPIA, SQSTM1, and CCL20 in Hepatocellular Carcinoma Patients by Single-Cell Transcriptome Analysis

Hepatocellular carcinoma (HCC) is the most malignant and poor-prognosis subtype of primary liver cancer. The scRNA-seq approach provides unique insight into tumor cell behavior at the single-cell level. Cytokine signaling in the immune system plays an important role in tumorigenesis and has both pro-tumorigenic and anti-tumorigenic functions. A biomarker of cytokine signaling in immune-related genes (CSIRG) is urgently required to assess HCC patient diagnosis and treatment. By analyzing the expression profiles of HCC single cells, TCGA, and ICGC data, we discovered that three important CSIRG (PPIA, SQSTM1, and CCL20) were linked to the overall survival of HCC patients. Cancer status and three hub CSIRG were taken into account while creating a risk nomogram. The nomogram had a high level of predictability and accuracy. Based on the CSIRG risk score, a distinct pattern of somatic tumor mutational burden (TMB) was detected between the two groups. The enrichment of the pyrimidine metabolism pathway, purine metabolism pathway, and lysosome pathway in HCC was linked to the CSIRG high-risk scores. Overall, scRNA-seq and bulk RNA-seq were used to create a strong CSIRG signature for HCC diagnosis.

m6A-related metabolism molecular classification with distinct prognosis and immunotherapy response in soft tissue sarcoma

N6-methyladenosine (m6A) methylation, one of the most crucial RNA modifications, has been proven to play a key role that affect prognosis of soft tissue sarcoma (STS). However, m6A methylation potential role in STS metabolic processes remains unknown. We comprehensively estimated the m6A metabolic molecular subtypes and corresponding survival, immunity, genomic and stemness characteristics based on 568 STS samples and m6A related metabolic pathways. Then, to quantify the m6A metabolic subtypes, machine learning algorithms were used to develop the m6A-metabolic Scores of individual patients. Finally, two distinct m6A metabolic subtypes (Cluster A and Cluster B) among the STS patients were identified. Compared to Cluster B subtype, the Cluster A subtype was mainly characterized by better survival advantages, activated anti-tumor immune microenvironment, lower gene mutation frequency and higher anti-PD-1 immunotherapy response rates. We also found that the m6A-metabolic Scores could accurately predict the molecular subtype of STS, prognosis, the abundance of immune cell infiltration, tumor metastasis status, sensitivity to chemotherapeutics and immunotherapy response. In general, this study revealed that m6A-regulated tumor metabolism processes played a key role in terms of prognosis of STS, tumor progression, and immune microenvironment. The identification of metabolic molecular subtypes and the construction of m6A-metabolic Score will help to more effectively guide immunotherapy, metabolic therapy and chemotherapy in STS.

Determination of Breast Metabolic Phenotypes and Their Associations With Immunotherapy and Drug-Targeted Therapy: Analysis of Single-Cell and Bulk Sequences

Breast cancer is highly prevalent and fatal worldwide. Currently, breast cancer classification is based on the presence of estrogen, progesterone, and human epidermal growth factor 2. Because cancer and metabolism are closely related, we established a breast cancer classification system based on the metabolic gene expression profile. We performed typing of metabolism-related genes using The Cancer Genome Atlas-Breast Cancer and 2010 (YAU). We included 2,752 metabolic genes reported in previous literature, and the genes were further identified according to statistically significant variance and univariate Cox analyses. These prognostic metabolic genes were used for non-negative matrix factorization (NMF) clustering. Then, we identified characteristic genes in each metabolic subtype using differential analysis. The top 30 characteristic genes in each subtype were selected for signature construction based on statistical parameters. We attempted to identify standard metabolic signatures that could be used for other cohorts for metabolic typing. Subsequently, to demonstrate the effectiveness of the 90 Signature, NTP and NMF dimensional-reduction clustering were used to analyze these results. The reliability of the 90 Signature was verified by comparing the results of the two-dimensionality reduction clusters. Finally, the submap method was used to determine that the C1 metabolic subtype group was sensitive to immunotherapy and more sensitive to the targeted drug sunitinib. This study provides a theoretical basis for diagnosing and treating breast cancer.

A Three-mRNA Signature Associated with Pyrimidine Metabolism for Prognosis of BRCA

Objective

Breast invasive carcinoma (BRCA), as a systemic disease, is currently the most malignant tumor among women. Early detection of BRCA will increase the probability of cure. Pyrimidine metabolism (PyM) stands for an essential metabolic pathway related to DNA replication of cancer cells, which may also serve as a diagnostic marker and therapeutic target. Therefore, the aim of this research is to discover a prognostic signature associated with PyM for BRCA.

Methods

The BRCA mRNA sequencing data along with microarray data were obtained based on The Cancer Genome Atlas (TCGA) database. In addition, 4 PyM-related gene sets were profiled through gene set enrichment analysis (GSEA); it revealed the core genes differentially expressed in cancer and paracancerous tissue. Thereafter, genes were subjected to univariate as well as multivariate regression for constructing an mRNA signature to independently predict BRCA prognosis. Then, the Kaplan-Meier (KM) curve was applied for validation. The prognostic power of the signature was verified against the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) database.

Results

We constructed a three-mRNA (RRM2B, NME3, and POLD2) gene signature related to PyM to predict overall survival (OS) for BRCA. The as-constructed gene signature was adopted to classify cases as high- or low-risk group, identifying patients with BRCA with poor prognosis. Additionally, the risk score obtained using our constructed 3-mRNA prognosis signature is independent from other clinical variables.

Conclusion

Our findings suggested that PyM-related mRNA signature might be a combined prognostic biomarker for BRCA and can provide important reference that are useful for individualized treatment for BRCA patients.

Analysis of the role of Ly-1 antibody reactive in different cancer types

LYAR (Ly-1 antibody reactive) is a transcription factor with a specific DNA-binding domain, which plays a key role in the regulation of embryonic stem cell self-renewal and differentiation. However, the role of LYAR in human cancers remains unclear. This study aimed to analyze the prognostic value of LYAR in cancer. In this study, we evaluated the prognostic value of LYAR in various tumors. We research found that, compared with normal tissues, LYAR levels werehigher in a variety of tumors. LYAR expression level was associated with poor overall survival, progression-free interval, and disease-specific survival. LYAR expression was also related to tumor grade, stage, age, and tumor status. Cell counting kit-8, Transwell, and wound healing assay showed that knocking out LYAR significantly inhibited the proliferation, migration, and invasion of hepatocellular carcinoma cells. In addition, this study found that LYARexpression was significantly positively correlated with MKI67IP, BZW2, and CCT2. Gene set enrichment analysis results showed that samples with high LYAR expression levels were rich in spliceosomes, RNA degradation, pyrimidine metabolism, cell cycle, nucleotide excision repair, and base excision repair.

Dihydropyrimidine dehydrogenase (DPYD) gene c.1627A>G A/G and G/G genotypes are risk factors for lymph node metastasis and distant metastasis of colorectal cancer

BACKGROUND

Dihydropyrimidine dehydrogenase (DPD) acts as the key enzyme catabolizing pyrimidines, and may affect the tumor progression. DPYD gene mutations affect DPD activity. The relationship between DPYD IVS14+1G>A, c.1627A>G, c.85T>C and lymph node metastasis (LNM) and distant metastasis (DM) of colorectal cancer (CRC) was investigated.

METHODS

A total of 537 CRC patients were enrolled in this study. DPYD polymorphisms were analyzed by polymerase chain reaction (PCR)-Sanger sequencing. The relationship between DPYD genotypes and clinical features of patients, metastasis of CRC was analyzed.

RESULTS

About DPYD c.1627A>G, A/A (57.7%) was the most common genotype, followed by A/G (35.6%), G/G (6.7%) genotypes. In c.85T>C, T/T, T/C, and C/C genotypes are accounted for 83.6%, 16.0%, and 0.4%, respectively. Logistic regression analysis revealed that DPYD c.1627A>G A/G and G/G genotypes in the dominant model (A/G + G/G vs. A/A) were significant risk factors for the LNM (p = 0.029, OR 1.506, 95% CI = 1.048-2.165) and DM (p = 0.039, OR 1.588, 95% CI = 1.041-2.423) of CRC. In addition, DPYD c.1627A>G polymorphism was more common in patients with abnormal serum carcinoembryonic antigen (CEA) (>5 ng/ml) (p = 0.003) or carbohydrate antigen 24-2 (CA24-2) (>20 U/ml) level (p = 0.015).

CONCLUSIONS

The results suggested that DPYD c.1627A>G A/G, G/G genotypes are associated with increased risk of LNM and DM of CRC.

The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes

ADME genes are a group of genes that are involved in drug absorption, distribution, metabolism, and excretion (ADME). The expression profiles of ADME genes within tumours is proposed to impact on cancer patient survival; however, this has not been systematically examined. In this study, our comprehensive analyses of pan-cancer datasets from the Cancer Genome Atlas (TCGA) revealed differential intratumoral expression profiles for ADME genes in 21 different cancer types. Most genes also showed high interindividual variability within cancer-specific patient cohorts. Using Kaplan-Meier plots and logrank tests, we showed that intratumoral expression levels of twenty of the thirty-two core ADME genes were associated with overall survival (OS) in these cancers. Of these genes, five showed significant association with unfavourable OS in three cancers, including SKCM (ABCC2, GSTP1), KIRC (CYP2D6, CYP2E1), PAAD (UGT2B7); sixteen showed significant associations with favourable OS in twelve cancers, including BLCA (UGT2B15), BRCA (CYP2D6), COAD (NAT1), HNSC (ABCB1), KIRC (ABCG2, CYP3A4, SLC22A2, SLC22A6), KIRP (SLC22A2), LIHC (CYP2C19, CYP2C8, CYP2C9, CYP3A5, SLC22A1), LUAD (SLC15A2), LUSC (UGT1A1), PAAD (ABCB1), SARC (ABCB1), and SKCM (ABCB1, DYPD). Overall, these data provide compelling evidence supporting ADME genes as prognostic biomarkers and potential therapeutic targets. We propose that intratumoral expression of ADME genes may impact cancer patient survival by multiple mechanisms that can include metabolizing/transporting anticancer drugs, activating anticancer drugs, and metabolizing/transporting a variety of endogenous molecules involved in metabolically fuelling cancer cells and/or controlling pro-growth signalling pathways.

Polymorphisms of xenobiotic-metabolizing and transporter genes, and the risk of gastric and colorectal cancer in an admixed population from the Brazilian Amazon

Colorectal (CRC) and gastric (GC) cancers are associated with increased morbidity and mortality. Single nucleotide polymorphisms (SNPs) of xenobiotic metabolism and transporter genes may play a role in the individual responses to exposure to substances implicated in susceptibility to cancer. The investigation of the genetic variation related to the activation and detoxification of xenobiotics may thus help to clarify the prevalence of neoplasms. We analyzed the role of 30 SNPs in xenobiotic-metabolizing and transporter genes in susceptibility to CRC and GC. The study included individuals diagnosed with CRC (n = 121) and GC (n = 95), and 141 controls (non-cancer patients) from the population of Belém, in the Brazilian Amazon. The results indicated an association between the polymorphisms rs2231142 (P = 0.013; OR = 3.01; 95% CI = 1.26-7.13), in the ABCG2 gene, and rs1801159 (P = 0.03; OR = 2.35; 95% CI = 1.14-5.05), in DPYD gene, with the risk of developing GC. The polymorphism rs17116806 of the DPYD gene was found to be associated with a lower risk of developing gastric (P≤0.0001; OR = 0.043; 95% CI = 0.015-0.12) or colorectal (P≤0.0001; OR = 0.076; 95% CI = 0.33-0.18) cancers, indicating that the same variant may play a similar role in different types of cancer tissue. Additionally, the carriers of the TT genotype of the polymorphism in the ABCB1 gene (rs1128503) presented a reduced probability of developing CRC (P = 0.0001; OR = 0.16; 95% CI = 0.06-0.41) as well as GC (P = 0.007; OR = 0.27; 95% CI = 0.1-0.7). Our findings indicate that polymorphisms in xenobiotic-metabolizing and transporter genes may modulate susceptibility to colorectal and gastric cancers.

Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology

TP53 mutation is one of the most common genetic changes in hepatocellular carcinoma (HCC). It is of great clinical significance to tailor specialized prognostication approach and to explore more therapeutic options for TP53-mutant HCCs. In this study, a total of 1135 HCC patients were retrospectively analyzed. We developed a random forest-based prediction model to estimate TP53 mutational status, tackling the problem of limited sample size in TP53-mutant HCCs. A multi-step process was performed to develop robust poor prognosis-associated signature (PPS). Compared with previous established population-based signatures, PPS manifested superior ability to predict survival in TP53-mutant patients. After in silico screening of 2249 drug targets and 1770 compounds, we found that three targets (CANT1, CBFB and PKM) and two agents (irinotecan and YM-155) might have potential therapeutic implications in high-PPS patients. The results of drug targets prediction and compounds prediction complemented each other, presenting a comprehensive view of potential treatment strategy. Overall, our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy.

Dihydropyrimidinase protects from DNA replication stress caused by cytotoxic metabolites

Imbalance in the level of the pyrimidine degradation products dihydrouracil and dihydrothymine is associated with cellular transformation and cancer progression. Dihydropyrimidines are degraded by dihydropyrimidinase (DHP), a zinc metalloenzyme that is upregulated in solid tumors but not in the corresponding normal tissues. How dihydropyrimidine metabolites affect cellular phenotypes remains elusive. Here we show that the accumulation of dihydropyrimidines induces the formation of DNA-protein crosslinks (DPCs) and causes DNA replication and transcriptional stress. We used Xenopus egg extracts to recapitulate DNA replication invitro. We found that dihydropyrimidines interfere directly with the replication of both plasmid and chromosomal DNA. Furthermore, we show that the plant flavonoid dihydromyricetin inhibits human DHP activity. Cellular exposure to dihydromyricetin triggered DPCs-dependent DNA replication stress in cancer cells. This study defines dihydropyrimidines as potentially cytotoxic metabolites that may offer an opportunity for therapeutic-targeting of DHP activity in solid tumors.

Characterization of Nme5-Like Gene/Protein from the Red Alga Chondrus Crispus

The Nme gene/protein family of nucleoside diphosphate kinases (NDPK) was originally named after its member Nm23-H1/Nme1, the first identified metastasis suppressor. Human Nme proteins are divided in two groups. They all possess nucleoside diphosphate kinase domain (NDK). Group I (Nme1-Nme4) display a single type NDK domain, whereas Group II (Nme5-Nme9) display a single or several different NDK domains, associated or not associated with extra-domains. Data strongly suggest that, unlike Group I, none of the members of Group II display measurable NDPK activity, although some of them autophosphorylate. The multimeric form is required for the NDPK activity. Group I proteins are known to multimerize, while there are no data on the multimerization of Group II proteins. The Group II ancestral type protein was shown to be conserved in several species from three eukaryotic supergroups. Here, we analysed the Nme protein from an early branching eukaryotic lineage, the red alga Chondrus crispus. We show that the ancestral type protein, unlike its human homologue, was fully functional multimeric NDPK with high affinity to various types of DNA and dispersed localization throughout the eukaryotic cell. Its overexpression inhibits both cell proliferation and the anchorage-independent growth of cells in soft agar but fails to deregulate cell apoptosis. We conclude that the ancestral gene has changed during eukaryotic evolution, possibly in correlation with the protein function.

In Vivo Expansion of Cancer Stemness Affords Novel Cancer Stem Cell Targets: Malignant Rhabdoid Tumor as an Example

Cancer stem cell (CSC) identification relies on transplantation assays of cell subpopulations sorted from fresh tumor samples. Here, we attempt to bypass limitations of abundant tumor source and predetermined immune selection by in vivo propagating patient-derived xenografts (PDX) from human malignant rhabdoid tumor (MRT), a rare and lethal pediatric neoplasm, to an advanced state in which most cells behave as CSCs. Stemness is then probed by comparative transcriptomics of serial PDXs generating a gene signature of epithelial to mesenchymal transition, invasion/motility, metastasis, and self-renewal, pinpointing putative MRT CSC markers. The relevance of these putative CSC molecules is analyzed by sorting tumorigenic fractions from early-passaged PDX according to one such molecule, deciphering expression in archived primary tumors, and testing the effects of CSC molecule inhibition on MRT growth. Using this platform, we identify ALDH1 and lysyl oxidase (LOX) as relevant targets and provide a larger framework for target and drug discovery in rare pediatric cancers.

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Human malignant rhabdoid tumor (MRT) can be propagated in vivo as tumor xenografts

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Long-term propagated PDX enrich for CSC frequency with no need for immune selection

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Distinct gene signature in stem-like MRT xenografts reveals putative CSC biomarkers

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Screening of putative CSC biomarkers allows identification of therapeutic targets

Identification of candidate aberrantly methylated and differentially expressed genes in thyroid cancer

Aberrant methylation of DNA sequences plays a criticle role in finding novel aberrantly methylated genes and pathways in thyroid cancer (THCA). This study aimed to integrate three cohorts profile datasets to find novel aberrantly methylated genes and pathways in THCA. Data of gene expression profiling microarrays (GSE33630 and GSE65144) and gene methylation profiling microarrays (GSE51090) were downloaded from the Gene Expression Omnibus database. Aberrantly methylated and differentially expressed genes were sorted and pathways were analyzed. Functional and enrichment analyses of selected genes were performed using the String database. A protein‐protein interaction network was constructed using the Cytoscape software, and module analysis was performed using Molecular Complex detection. In total, we identified 12 hypomethylation/high‐expression genes and 30 hypermethylation/low‐expression genes at the screening step and, finally, found 6 mostly changed hub genes including PPARGC1A, CREBBP, EP300, CD44, SPP1, and MMP9. Pathway analysis showed that aberrantly methylated differentially expressed genes were mainly associated with the thyroid hormone signaling pathway, AMP‐activated protein kinase (AMPK) signaling pathway, and cell cycle process in THCA. After validation in the Cancer Genome Atlas database, the methylation and expression status of hub genes was significantly altered and the same with our results. Taken together, we identified novel aberrantly methylated genes and pathways in THCA, which could improve our understanding of the cause and underlying molecular events, and these candidate genes could serve as aberrant methylation‐based biomarkers for precise diagnosis and treatment of THCA.

Systems biology analysis of mitogen activated protein kinase inhibitor resistance in malignant melanoma

BACKGROUND

Kinase inhibition in the mitogen activated protein kinase (MAPK) pathway is a standard therapy for cancer patients with activating BRAF mutations. However, the anti-tumorigenic effect and clinical benefit are only transient, and tumors are prone to treatment resistance and relapse. To elucidate mechanistic insights into drug resistance, we have established an in vitro cellular model of MAPK inhibitor resistance in malignant melanoma.

METHODS

The cellular model evolved in response to clinical dosage of the BRAF inhibitor, vemurafenib, PLX4032. We conducted transcriptomic expression profiling using RNA-Seq and RT-qPCR arrays. Pathways of melanogenesis, MAPK signaling, cell cycle, and metabolism were significantly enriched among the set of differentially expressed genes of vemurafenib-resistant cells vs control. The underlying mechanism of treatment resistance and pathway rewiring was uncovered to be based on non-genomic adaptation and validated in two distinct melanoma models, SK-MEL-28 and A375. Both cell lines have activating BRAF mutations and display metastatic potential.

RESULTS

Downregulation of dual specific phosphatases, tumor suppressors, and negative MAPK regulators reengages mitogenic signaling. Upregulation of growth factors, cytokines, and cognate receptors triggers signaling pathways circumventing BRAF blockage. Further, changes in amino acid and one-carbon metabolism support cellular proliferation despite MAPK inhibitor treatment. In addition, treatment-resistant cells upregulate pigmentation and melanogenesis, pathways which partially overlap with MAPK signaling. Upstream regulator analysis discovered significant perturbation in oncogenic forkhead box and hypoxia inducible factor family transcription factors.

CONCLUSIONS

The established cellular models offer mechanistic insight into cellular changes and therapeutic targets under inhibitor resistance in malignant melanoma. At a systems biology level, the MAPK pathway undergoes major rewiring while acquiring inhibitor resistance. The outcome of this transcriptional plasticity is selection for a set of transcriptional master regulators, which circumvent upstream targeted kinases and provide alternative routes of mitogenic activation. A fine-woven network of redundant signals maintains similar effector genes allowing for tumor cell survival and malignant progression in therapy-resistant cancer.

Genome-wide association study of borderline personality disorder reveals genetic overlap with bipolar disorder, major depression and schizophrenia

Borderline personality disorder (BOR) is determined by environmental and genetic factors, and characterized by affective instability and impulsivity, diagnostic symptoms also observed in manic phases of bipolar disorder (BIP). Up to 20% of BIP patients show comorbidity with BOR. This report describes the first case-control genome-wide association study (GWAS) of BOR, performed in one of the largest BOR patient samples worldwide. The focus of our analysis was (i) to detect genes and gene sets involved in BOR and (ii) to investigate the genetic overlap with BIP. As there is considerable genetic overlap between BIP, major depression (MDD) and schizophrenia (SCZ) and a high comorbidity of BOR and MDD, we also analyzed the genetic overlap of BOR with SCZ and MDD. GWAS, gene-based tests and gene-set analyses were performed in 998 BOR patients and 1545 controls. Linkage disequilibrium score regression was used to detect the genetic overlap between BOR and these disorders. Single marker analysis revealed no significant association after correction for multiple testing. Gene-based analysis yielded two significant genes: DPYD (P=4.42 × 10-7) and PKP4 (P=8.67 × 10-7); and gene-set analysis yielded a significant finding for exocytosis (GO:0006887, PFDR=0.019; FDR, false discovery rate). Prior studies have implicated DPYD, PKP4 and exocytosis in BIP and SCZ. The most notable finding of the present study was the genetic overlap of BOR with BIP (rg=0.28 [P=2.99 × 10-3]), SCZ (rg=0.34 [P=4.37 × 10-5]) and MDD (rg=0.57 [P=1.04 × 10-3]). We believe our study is the first to demonstrate that BOR overlaps with BIP, MDD and SCZ on the genetic level. Whether this is confined to transdiagnostic clinical symptoms should be examined in future studies.

Identification of a Potential Regulatory Variant for Colorectal Cancer Risk Mapping to 3p21.31 in Chinese Population

Genome-wide association studies (GWAS) have established chromosome 3p21.31 as a susceptibility locus for colorectal cancer (CRC) that lacks replication and exploration in the Chinese population. We searched potentially functional single nucleotide polymorphisms (SNPs) in the linkage disequilibrium (LD) block of 3p21.31 with chromatin immunoprecipitation-sequencing (ChIP-seq) data of histone modification, and tested their association with CRC via a case-control study involving 767 cases and 1397 controls in stage 1 and 528 cases and 678 controls in stage 2. In addition to the tag SNP rs8180040 (odds ratio (OR) = 0.875, 95% confidence interval (95% CI) = 0.793−0.966, P = 0.008, P-FDR (false discovery rate) = 0.040), rs1076394 presented consistently significant associations with CRC risk at both stages with OR = 0.850 (95% CI = 0.771−0.938, P = 0.001, P-FDR = 0.005) under the additive model in combined analyses. Supported by the analyses of data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it was suggested that rs1076394 served as an expression Quantitative Trait Loci (eQTL) for gene CCDC12 and NME6, while NME6’s expression was obviously higher in CRC tissues. Using biofeature information such as ChIP-seq and RNA sequencing (RNA-seq) data might help researchers to interpret GWAS results and locate functional variants for diseases in the post-GWAS era.