Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Integrative analysis of expression profile indicates the ECM receptor and LTP dysfunction in the glioma-related epilepsy.

BACKGROUND: Seizures are a common symptom in glioma patients, and they can cause brain dysfunction. However, the mechanism by which glioma-related epilepsy (GRE) causes alterations in brain networks remains elusive. OBJECTIVE: To investigate the potential pathogenic mechanism of GRE by analyzing the dynamic expression profiles of microRNA/ mRNA/ lncRNA in brain tissues of glioma patients. METHODS: Brain tissues of 16 patients with GRE and 9 patients with glioma without epilepsy (GNE) were collected. The total RNA was dephosphorylated, labeled, and hybridized to the Agilent Human miRNA Microarray, Release 19.0, 8 × 60 K. The cDNA was labeled and hybridized to the Agilent LncRNA + mRNA Human Gene Expression Microarray V3.0, 4 × 180 K. The raw data was extracted from hybridized images using Agilent Feature Extraction, and quantile normalization was performed using the Agilent GeneSpring. P-value < 0.05 and absolute fold change > 2 were considered the threshold of differential expression data. Data analyses were performed using R and Bioconductor. RESULTS: We found that 3 differentially expressed miRNAs (miR-10a-5p, miR-10b-5p, miR-629-3p), 6 differentially expressed lncRNAs (TTN-AS1, LINC00641, SNHG14, LINC00894, SNHG1, OIP5-AS1), and 49 differentially expressed mRNAs play a vitally critical role in developing GRE. The expression of GABARAPL1, GRAMD1B, and IQSEC3 were validated more than twofold higher in the GRE group than in the GNE group in the validation cohort. Pathways including ECM receptor interaction and long-term potentiation (LTP) may contribute to the disease's progression. Meanwhile, We built a lncRNA-microRNA-Gene regulatory network with structural and functional significance. CONCLUSION: These findings can offer a fresh perspective on GRE-induced brain network changes.

Targeting translation regulators improves cancer therapy.

Deregulation of protein synthesis may be involved in multiple aspects of cancer, such as gene expression, signal transduction and drive specific cell biological responses, resulting in promoting cancer growth, invasion and metastasis. Study the molecular mechanisms about translational control may help us to find more effective anti-cancer drugs and develop novel therapeutic opportunities. Recently, the researchers had focused on targeting translational machinery to overcome cancer, and various small molecular inhibitors targeting translation factors or pathways have been tested in clinical trials and exhibited improving outcomes in several cancer types. There is no doubt that an insight into the class of translation regulation protein would provide new target for pharmacologic intervention and further provide opportunities to develop novel anti-tumor therapeutic interventions. In this review, we summarized the developments of translational control in cancer survival and progression et al, and highlighted the therapeutic approach targeted translation regulation to overcome the cancer.

eIF3a R803K mutation mediates chemotherapy resistance by inducing cellular senescence in small cell lung cancer.

Drug resistance in small cell lung cancer (SCLC) significantly affects the efficacy of chemotherapy treatment. However, due to the lack of tumor tissue samples, especially serial tumor samples during chemotherapy, the mechanism of chemotherapy resistance has not been fully studied. Circulating tumor DNA, which can be obtained in a noninvasive manner, can complement tumor sampling approaches for research in this field. We identified an SCLC patient with acquired drug resistance from 52 SCLC patients for whom follow-up data were available. By comparing somatic mutations in circulating tumor DNA before and after chemotherapy, for the first time, we found that the somatic mutation eIF3A R803K may be related to acquired chemotherapy resistance. Then, the association between the eIF3A R803K mutation and chemotherapy resistance was confirmed by samples from 254 lung cancer patients receiving chemotherapy. We found that the eIF3a R803K mutation weakened the proliferation ability of tumor cells but increased their resistance to chemotherapy. Further studies revealed that the eIF3A R803K mutation promotes cellular senescence. In addition, fisetin showed a synergistic effect with chemotherapy in eIF3A R803K mutant cells. These results suggest that the eIF3A R803K somatic mutation has the potential to predict chemotherapy resistance in SCLC. Moreover, the eIF3A R803K mutation promotes chemotherapy resistance by inducing senescence. Furthermore, a senolytic drug, fisetin, can reverse chemotherapy resistance mediated by the eIF3A R803K mutation.

Response prediction biomarkers and drug combinations of PARP inhibitors in prostate cancer.

PARP inhibitors are a group of inhibitors targeting poly(ADP-ribose) polymerases (PARP1 or PARP2) involved in DNA repair and transcriptional regulation, which may induce synthetic lethality in BRCAness tumors. Systematic analyzes of genomic sequencing in prostate cancer show that ~10%-19% of patients with primary prostate cancer have inactivated DNA repair genes, with a notably higher proportion of 23%-27% in patients with metastatic castration-resistant prostate cancer (mCRPC). These characteristic genomic alterations confer possible vulnerability to PARP inhibitors in patients with mCRPC who benefit only modestly from other therapies. However, only a small proportion of patients with mCRPC shows sensitivity to PARP inhibitors, and these sensitive patients cannot be fully identified by existing response prediction biomarkers. In this review, we provide an overview of the potential response prediction biomarkers and synergistic combinations studied in the preclinical and clinical stages, which may expand the population of patients with prostate cancer who may benefit from PARP inhibitors.

Integrative analyses identify a DNA damage repair gene signature for prognosis prediction in lower grade gliomas.

Background: The DNA damage repair (DDR) pathways play important roles for regulating cancer progression and therapeutic response. IDH mutations, well-known prognosis biomarkers for glioma, lead to hypermethylation of tumor cells and affect genes' expression. Whether IDH mutations affect glioma prognosis through influencing the expression of DDR genes remains unclear. Methods: A total of 272 DDR genes were selected for differential expression and survival analysis. The identified genes were then utilized to construct the prognosis predicting model. Results: PARPBP, PLK3, POLL and WEE1 were found differential expressed between IDH mutations carriers and wild-type carriers, and were associated with survival of low grade glioma (LGG) patients. The predicting algorithm can predicts the prognosis of LGG patients. Conclusion: IDH mutations may affect LGG prognosis through regulation of DDR pathways.

Precision dosing of warfarin: open questions and strategies.

Warfarin has a very narrow therapeutic window and obvious interindividual variability in its effects, with many factors contributing to the body's response. Algorithms incorporating multiple genetic, environment and clinical factors have been established to select a precision dose for each patient. A number of randomized controlled trials (RCTs) were conducted to explore whether patients could benefit from these algorithms; however, the results were inconsistent. Some questions remain to be resolved. Recently, new genetic and non-genetic factors have been discovered to contribute to variability in optimal warfarin doses. The results of further RCTs have been unveiled, and guidelines for pharmacogenetically guided warfarin dosing have been updated. Based on these most recent advancements, we summarize some open questions in this field and try to propose possible strategies to resolve them.

The IDO genetic polymorphisms and postpartum depressive symptoms: an association study in Chinese parturients who underwent cesarean section.

Postpartum depressive symptoms (PDS) are not an uncommon mood disorder in postpartum women. Our previous research indicated a role for increased tryptophan (TRP) metabolism along the kynurenine pathway (KP) in the pathogenesis of PDS. Accordingly, this study was going to investigate the association of indoleamine-2,3-dioxygenase (IDO, a key enzyme of KP) genetic polymorphisms with PDS. Seven hundred twenty-five women receiving cesarean section were enrolled in this study. PDS was determined by an Edinburgh Postnatal Depression Scale (EPDS) score ≥ 13. Subsequently, 48 parturients with PDS and 48 parturients without PDS were selected for investigation of perinatal serum concentrations of TRP, kynurenine (KYN), and KYN/TRP ratio, the latter is the representative of IDO activity. In addition, seven single nucleotide polymorphisms of the IDO gene were examined. Following this genotyping, 50 parturients carrying the IDO rs10108662 AA genotype and 50 parturients carrying the IDO rs10108662 AC + CC genotype were selected for comparisons of TRP, KYN, and KYN/TRP ratio levels. This study showed the PDS incidence of 6.9% in the Chinese population, with PDS characterized by increased IDO activity (p < 0.05), versus women without PDS. We also found that the variations of IDO1 gene rs10108662 were significantly related to PDS incidence (p < 0.05). Furthermore, there was a significant difference in IDO activity between the IDO rs10108662 CA + AA, versus CC, genotypes. Our findings indicate a role of the kynurenine pathway in the development of PDS, rs10108662 genetic polymorphism resulting in changes of IDO activity might contribute to PDS pathogenesis.

Resistin facilitates metastasis of lung adenocarcinoma through the TLR4/Src/EGFR/PI3K/NF-κB pathway.

Metastasis is the main cause of lung cancer-related death. The tumor microenvironment greatly contributes to tumor metastasis. Resistin, mainly secreted by tumor-associated macrophages in tumor tissues, is a 12.5-kDa cysteine-rich secretory protein that is found at significantly higher levels in the serum or plasma of cancer patients compared with healthy controls. In this study, we explored the expression and role of resistin in lung adenocarcinoma. Our study showed that resistin was strongly expressed in lung adenocarcinoma tissues and promoted the migration and invasion of lung adenocarcinoma cells in a dose-dependent manner. Toll-like receptor 4 (TLR4) was the functional receptor of resistin for migration and invasion in A549 cells. Src/epidermal growth factor receptor (EGFR) was involved in resistin-induced migration and invasion. Resistin increased the phosphorylation of EGFR through the TLR4/Src pathway. We also found that PI3K/nuclear factor (NF)-κB were the intracellular downstream effectors mediating resistin-induced migration and invasion. Taken together, our results suggested that resistin promoted lung adenocarcinoma metastasis through the TLR4/Src/EGFR/PI3K/NF-κB pathway.

Copper efflux transporters ATP7A and ATP7B: Novel biomarkers for platinum drug resistance and targets for therapy.

Platinum-based chemotherapy agents are widely used in the treatment of various solid malignancies. However, their efficacy is limited by drug resistance. Recent studies suggest that copper efflux transporters, which are encoded by ATP7A and ATP7B, play an important role in platinum drug resistance. Over-expressions of ATP7A and ATP7B are observed in multiple cancers. Moreover, their expressions are associated with cancer prognosis and treatment outcomes of platinum-based chemotherapy. In our review, we highlight the roles of ATP7A/7B in platinum drug resistance and cancer progression. We also discuss the possible mechanisms of platinum drug resistance mediated by ATP7A/7B and provide novel strategies for overcoming resistance. This review may be helpful for understanding the roles of ATP7A and ATP7B in platinum drug resistance. © 2018 IUBMB Life, 70(3):183-191, 2018.

Sodium Valproate Ameliorates Neuronal Apoptosis in a Kainic Acid Model of Epilepsy via Enhancing PKC-Dependent GABAAR γ2 Serine 327 Phosphorylation.

GABA is a dominant inhibitory neurotransmitter in the brain and A type GABA receptor (GABAAR) phosphorylation is critical for GABA-mediated inhibitory effect. However, its role in the neuroprotective effect of sodium valproate (VPA), a prevalent drug for treating patients with epilepsy, remains elusive. The present study was conducted to explore the role of GABAAR phosphorylation in the neuroprotection of VPA against a kainic acid-induced epileptic rat model and the potential molecular mechanisms. Neuronal apoptosis was evaluated by TUNEL assay, PI/Annexin V double staining, caspase-3 activity detection and Bax and Bcl-2 proteins expression via Western blot analysis. The primary rat hippocampal neurons were cultivated and cell viability was measured by CCK8 detection following KA- or free Mg2+-induced neuronal impairment. Our results found that VPA treatment significantly reduced neuronal apoptosis in the KA-induced rat model (including reductions of TUNEL-positive cells, caspase-3 activity and Bax protein expression, and increase of Bcl-2 protein level). In the in vitro experiments, VPA at the concentration of 1 mM for 24 h also increased cell survival and suppressed cell apoptosis in KA- or no Mg2+-induced models via CCK8 assay and PI/Annexin V double staining, respectively. What is more important, the phosphorylation of γ2 subunit at serine 327 residue for GABAAR was found to be robustly enhanced both in the KA-induced epileptic rat model and neuronal cultures following KA exposure after VPA treatment, while no evident alteration was found in terms of GABAAR ß3 phosphorylation (408 or 409 serine residue). Additionally, pharmacological inhibition of protein kinase C (PKC) clearly abrogated the neuroprotective potential of VPA against KA- or free Mg2+-associated neuronal injury, indicating a critical role of PKC in the effect of GABAAR γ2 serine 327 phosphorylation in VPA's protection. In summary, our work reveals that VPA mitigates neuronal apoptosis in KA-triggered epileptic seizures, at least, via augmenting PKC-dependent GABAAR γ2 phosphorylation at serine 327 residue.

Repaglinide-irbesartan drug interaction: effects of SLCO1B1 polymorphism on repaglinide pharmacokinetics and pharmacodynamics in Chinese population.

PURPOSE: On account of the potential inhibition of OATP1B1 (organic anion transporting polypeptide) by angiotensin II receptor blockers (ARBs) and the effects of SLCO1B1 (solute carrier organic anion transporter family member) polymorphism, the aim of current study is to assess the impact of ARBs on the pharmacokinetics (PK) and pharmacodynamics (PD) of repaglinide in Chinese healthy volunteers with different SLCO1B1 genotypes. METHODS: The in vitro study was conducted on irbesartan, valsartan, olmesartan, and losartan by using HEK293 cells transfected with OATP1B1. Data on drug interactions between repaglinide and irbesartan from 21 healthy Chinese-Han male volunteers were collected and analyzed. RESULTS: IC50 from in vitro study suggested irbesartan was the most potent inhibitor of OATP1B1 transporter. Clinical data from single dose of repaglinide indicated SLCO1B1 c.521 T>C polymorphism influenced the PK and PD of repaglinide in healthy Chinese-Han male volunteers. In subjects with SLCO1B1 c.521 TT genotype, irbesartan comedication increased the exposure of repaglinide. In details, the peak plasma concentration [Cmax] increased 84% (P = 0.003) and the area under the curve of plasma concentration 0-8 h [AUC0-8] increased 34% (P = 0.004), while the minimum blood glucose concentration [Cmin] decreased 33.8% (P = 0.005). No significant change was observed in repaglinide exposure in subjects with SLCO1B1 c.521 TC genotype in presence or absence of irbesartan. CONCLUSION: SLCO1B1 c.521 T>C polymorphism affects the PK of repaglinide in Chinese population. Irbesartan increased repaglinide exposure in subjects with SLCO1B1 c.521 TT genotype, but not SLCO1B1 c.521 TC genotype.

Overexpression of Fn14 in gliomas: tumor progression and poor prognosis.

AIM: To confirm whether the expression level of Fn14 could affect progression or prognosis of glioma patients. METHODS: Glioma cohorts in The Cancer Genome Atlas, Gene Expression Omnibus and Chinese Glioma Genome Atlas databases were comprehensively analyzed. RESULTS: Low-grade patients had lower expression level of Fn14, while patients with higher expression of Fn14 tended to harbor shorter overall survival and disease-free survival. The expression level of Fn14 was downregulated by IDH1/IDH2 mutations while its gene body methylation was upregulated. After adjusting age, the expression level of Fn14 was still significantly associated with overall survival and disease-free survival in low-grade gliomas. In a cell line data analysis, Fn14 expression was positively correlated with temozolomide dosage. CONCLUSION: Fn14 was an independent predictive biomarker for the progression and prognosis in low-grade gliomas.

Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis.

BACKGROUND: Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology. RESULTS: Weighted gene co-expression network analysis, a powerful technique used to extract co-expressed gene networks from mRNA expressions, was conducted to identify 11 co-regulated modules in a discovery dataset with 461 patients. A transcriptional module enriched in cell cycle processes was correlated with the recurrence-free survival of the CC patients in the discovery (HR = 0.59; 95% CI = 0.42-0.81) and validation (HR = 0.51; 95% CI = 0.25-1.05) datasets. The prognostic potential of the hub gene Centromere Protein-A (CENPA) was also identified and the upregulation of this gene was associated with good survival. Another cell cycle phase-related gene module was correlated with the survival of the patients with a KRAS mutation CC subtype. The downregulation of several genes, including those found in this co-expression module, such as cyclin-dependent kinase 1 (CDK1), was associated with poor survival. CONCLUSION: Network-based approaches may facilitate the discovery of biomarkers for the prognosis of a subset of patients with stage II or III CC, these approaches may also help direct personalised therapies.

Genome-wide DNA methylation profiling reveals novel epigenetic signatures in squamous cell lung cancer.

BACKGROUND: Epigenetic alterations are strongly associated with the development of cancer. The aim of this study was to identify epigenetic pattern in squamous cell lung cancer (LUSC) on a genome-wide scale. RESULTS: Here we performed DNA methylation profiling on 24 LUSC and paired non-tumor lung (NTL) tissues by Illumina Human Methylation 450 K BeadArrays, and identified 5214 differentially methylated probes. By integrating DNA methylation and mRNA expression data, 449 aberrantly methylated genes accompanied with altered expression were identified. Ingenuity Pathway analysis highlighted these genes which were closely related to the carcinogenesis of LUSC, such as ERK family, NFKB signaling pathway, Hedgehog signaling pathway, providing new clues for understanding the molecular mechanisms of LUSC pathogenesis. To verify the results of high-throughput screening, we used 56 paired independent tissues for clinical validation by pyrosequencing. Subsequently, another 343 tumor tissues from the Cancer Genome Atlas (TCGA) database were utilized for further validation. Then, we identified a panel of DNA methylation biomarkers (CLDN1, TP63, TBX5, TCF21, ADHFE1 and HNF1B) in LUSC. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the performance of biomarkers individually, suggesting that they could be suitable as potential diagnostic biomarkers for LUSC. Moreover, hierarchical clustering analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation. CONCLUSIONS: Collectively, these results suggest that DNA methylation plays critical roles in lung tumorigenesis and may potentially be proposed as a diagnostic biomarker. TRIAL REGISTRATION: ChiCTR-RCC-12002830 Date of registration: 2012-12-17.

Association between well-characterized lung cancer lncRNA polymorphisms and platinum-based chemotherapy toxicity in Chinese patients with lung cancer.

Platinum-based chemotherapy is the standard first-line treatment for most lung cancer patients. However, the toxicity induced by platinum-based chemotherapy greatly impedes its clinical use. Previous studies showed that long non-coding RNAs (lncRNAs) with over 200 nucleotides in length affect drug response and toxicity. In the present study, we investigated the association of well-characterized lung cancer lncRNA polymorphisms with platinum-based chemotherapy toxicity in Chinese patients with lung cancer. A total of 467 lung cancer patients treated with platinum-based chemotherapy for at least two cycles were recruited. We primarily focused on gastrointestinal and hematological toxicities. A total of 14 potentially functional polymorphisms within 8 lncRNAs (HOTTIP, HOTAIT, H19, ANRIL, CCAT2, MALAT1, MEG3, and POLR2E) were genotyped. Unconditional logistical regression analysis was conducted to assess the associations. Gene-gene and gene-environment interactions were identified using the software generalized multifactor dimensionality reduction (GMDR). ANRIL rs1333049 was associated with severe overall toxicity in an additive model (adjusted OR=0.723, 95% CI=0.541-0.965, P=0.028). ANRIL rs1333049 was also associated with severe gastrointestinal toxicity in both the additive (adjusted OR=0.690, 95% CI=0.489-0.974, P=0.035) and dominant (adjusted OR=0.558, 95% CI=0.335-0.931, P=0.025) models. MEG3 rs116907618 was associated with severe gastrointestinal toxicity in an additive model (adjusted OR=1.717, 95% CI=1.007-2.927, P=0.047). GMDR identified the three-factor interaction model of POLR2E rs3787016-HOTTIP rs3807598-chemotherapy regimen as the best predictive model for hematological toxicity. In conclusion, ANRIL and MEG3 genetic polymorphisms are associated with severe platinum toxicity and could be considered as biomarkers for pretreatment evaluation in Chinese patients with lung cancer.

IL-3 and CTLA4 gene polymorphisms may influence the tacrolimus dose requirement in Chinese kidney transplant recipients.

The highly variable pharmacokinetics and narrow therapeutic window of tacrolimus (TAC) has hampered its clinical use. Genetic polymorphisms may contribute to the variable response, but the evidence is not compelling, and the explanation is unclear. In this study we attempted to find previously unknown genetic factors that may influence the TAC dose requirements. The association of 105 pathway-related single nucleotide polymorphisms (SNPs) with TAC dose-adjusted concentrations (C0/D) was examined at 7, 30 and 90 d post-operation in 382 Chinese kidney transplant recipients. In CYP3A5 non-expressers, the patients carrying the IL-3 rs181781 AA genotype showed a significantly higher TAC logC0/D than those with the AG genotype at 30 and 90 d post-operation (AA vs AG, 2.21±0.06 vs 2.01±0.03, P=0.004; and 2.17±0.06 vs 2.03±0.03, P=0.033, respectively), and than those with the GG genotype at 30 d (AA vs GG, 2.21±0.06 vs 2.04±0.03, P =0.011). At 30 d, the TAC logC0/D in the grouped AG+GG genotypes of CTLA4 rs4553808 was significantly lower than that in the AA genotype (P =0.041) in CYP3A5 expressers, but it was higher (P=0.008) in the non-expressers. We further validated the influence of CYP3A5 rs776746, CYP3A4 rs2242480 and rs4646437 on the TAC C0/D; other candidate SNPs were not associated with the differences in TAC C0/D. In conclusion, genetic polymorphisms in the immune genes IL-3 rs181781 and CTLA4 rs4553808 may influence the TAC C0/D. They may, together with CYP3A5 rs776746, CYP3A4 rs2242480 and rs4646437, contribute to the variation in TAC dose requirements. When conducting individualized therapy with tacrolimus, these genetic factors should be taken into account.