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At present, most patients with oral squamous cell carcinoma (OSCC) are in the middle or advanced stages at the time of diagnosis. Advanced OSCC patients have a poor prognosis after traditional therapy, and the complex heterogeneity of OSCC has been proven to be one of the main reasons. Single-cell sequencing technology provides a powerful tool for dissecting the heterogeneity of cancer. However, most of the current studies at the single-cell level are static, while the development of cancer is a dynamic process. Thus, understanding the development of cancer from a dynamic perspective and formulating corresponding therapeutic measures for achieving precise treatment are highly necessary, and this is also one of the main study directions in the field of oncology. In this study, we combined the static and dynamic analysis methods based on single-cell RNA-Seq data to comprehensively dissect the complex heterogeneity and evolutionary process of OSCC. Subsequently, for clinical practice, we revealed the association between cancer heterogeneity and the prognosis of patients. More importantly, we pioneered the concept of pseudo-time score of patients, and we quantified the levels of heterogeneity based on the dynamic development process to evaluate the relationship between the score and the survival status at the same stage, finding that it is closely related to the prognostic status. The pseudo-time score of patients could not only reflect the tumor status of patients but also be used as an indicator of the effects of drugs on the patients so that the medication strategy can be adjusted on time. Finally, we identified candidate drugs and proposed precision medication strategies to control the condition of OSCC in two respects: treatment and blocking.

BackgroundBreast cancer (BC) is a complex disease with high heterogeneity, which often leads to great differences in treatment results. Current common molecular typing method is PAM50, which shows positive results for precision medicine; however, room for improvement still remains because of the different prognoses of subtypes. Therefore, in this article, we used lncRNAs, which are more tissue-specific and developmental stage-specific than other RNAs, as typing markers and combined single-cell expression profiles to retype BC, to provide a new method for BC classification and explore new precise therapeutic strategies based on this method.MethodsBased on lncRNA expression profiles of 317 single cells from 11 BC patients, SC3 was used to retype BC, and differential expression analysis and enrichment analysis were performed to identify biological characteristics of new subtypes. The results were validated for survival analysis using data from TCGA. Then, the downstream regulatory genes of lncRNA markers of each subtype were searched by expression correlation analysis, and these genes were used as targets to screen therapeutic drugs, thus proposing new precision treatment strategies according to the different subtype compositions of patients.ResultsSeven lncRNA subtypes and their specific biological characteristics are obtained. Then, 57 targets and 210 drugs of 7 subtypes were acquired. New precision medicine strategies were proposed according to the different compositions of patient subtypes.ConclusionsFor patients with different subtype compositions, we propose a strategy to select different drugs for different patients, which means using drugs targeting multi subtype or combinations of drugs targeting a single subtype to simultaneously kill different cancer cells by personalized treatment, thus reducing the possibility of drug resistance and even recurrence.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Due to the lack of early diagnosis methods and warning signals of CRC and its strong heterogeneity, the determination of accurate treatments for CRC and the identification of specific early warning signals are still urgent problems for researchers. In this study, the expression profiles of cancer tissues and the expression profiles of tumour-adjacent tissues in 28 CRC patients were combined into a human protein–protein interaction (PPI) network to construct a specific network for each patient. A network propagation method was used to obtain a mutant giant cluster (GC) containing more than 90% of the mutation information of one patient. Next, mutation selection rules were applied to the GC to mine the mutation sequence of driver genes in each CRC patient. The mutation sequences from patients with the same type CRC were integrated to obtain the mutation sequences of driver genes of different types of CRC, which provide a reference for the diagnosis of clinical CRC disease progression. Finally, dynamic network analysis was used to mine dynamic network biomarkers (DNBs) in CRC patients. These DNBs were verified by clinical staging data to identify the critical transition point between the pre-disease state and the disease state in tumour progression. Twelve known drug targets were found in the DNBs, and 6 of them have been used as targets for anticancer drugs for clinical treatment. This study provides important information for the prognosis, diagnosis and treatment of CRC, especially for pre-emptive treatments. It is of great significance for reducing the incidence and mortality of CRC.

Hepatocellular carcinoma (HCC) is the most frequent type of liver cancer with poor survival rate and high mortality. Despite efforts on the mechanism of HCC, new molecular markers are needed for exact diagnosis, evaluation and treatment. Here, we combined transcriptome of HCC with networks and pathways to identify reliable molecular markers. Through integrating 249 differentially expressed genes with syncretic protein interaction networks, we constructed a HCC-specific network, from which we further extracted 480 pivotal genes. Based on the cross-talk between the enriched pathways of the pivotal genes, we finally identified a HCC signature of 45 genes, which could accurately distinguish HCC patients with normal individuals and reveal the prognosis of HCC patients. Among these 45 genes, 15 showed dysregulated expression patterns and a part have been reported to be associated with HCC and/or other cancers. These findings suggested that our identified 45 gene signature could be potential and valuable molecular markers for diagnosis and evaluation of HCC.

// Hongbo Xie 1, * , Diheng Zeng 1, * , Xiujie Chen 1, * , Diwei Huo 2, * , Lei Liu 1 , Denan Zhang 1 , Qing Jin 1 , Kehui Ke 1 and Ming Hu 1 1 Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, PR China 2 The 2nd Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150081, PR China * These authors contributed equally to this work Correspondence to: Xiujie Chen, email: chenxiujie@ems.hrbmu.edu.cn Denan Zhang, email: zhangdenan@ems.hrbmu.edu.cn Keywords: drug-induced liver injury, personalized medicine, homology-modeling, molecular simulation, risk population prediction Received: July 06, 2017 Accepted: September 20, 2017 Published: October 05, 2017 ABSTRACT Idiosyncratic adverse drug reactions are drug reactions that occur rarely and unpredictably among the population. These reactions often occur after a drug is marketed, which means that they are strongly related to the genotype of the population. The prediction of such adverse reactions is a major challenge because of the lack of appropriate test models during the drug development process. In this study, we chose withdrawn drugs because the reasons why they were withdrawn and from which countries or regions is easily obtained. We selected Dilevalol and its chiral drug (Labetalol) as the investigatory drugs, as they have been withdrawn from a European market (Britain) because of serious hepatotoxicity. First, we searched for and obtained the Dilevalol-induced- liver-injury related protein, multidrug resistance protein 1 (MDR1), from the Comparative Toxicogenomics Database (CTD). Then, we searched and extracted 477 non-synonymous single nucleotide polymorphisms (nsSNP) on MDR1 in the dbSNP database. Second, we used the VarMod tool to predict the functional changes of MDR1 induced by these nsSNPs, from which we extracted the nsSNPs that significantly change the functions of this protein. Third, we built the three-dimensional structures of those variant proteins and used AutoDock to perform a docking study, choosing the best model to determine the sites of nsSNPs. Finally, we used the data from the 1000 Genomes Project to verify the dominant population distribution of the risk SNP. We applied the same strategy to the post-marketing drug-induced liver injury drugs to further test the feasibility of our method.

// Xiujie Chen 1, * , Lei Liu 1, * , YunFeng Wang 1, * , Bo Liu 1, * , Diheng Zeng 1 , Qing Jin 1 , MengJian Li 1 , DeNan Zhang 1 , Qiuqi Liu 1 , Hongbo Xie 1 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China * These authors contributed equally to this work Correspondence to: Xiujie Chen, email: chenxiujie@ems.hrbmu.edu.cn Keywords: breast cancer, normal tissue, tumor tissue, recurrence, risk factors Received: September 30, 2015 Accepted: July 26, 2016 Published: August 23, 2016 ABSTRACT The recurrence of breast cancer (BC) is a serious therapeutic problem, and the risk factors for recurrence urgently need to be identified. In this study, we examined the functional pathways in tumor and normal tissues to more comprehensively identify biomarkers for the risk of BC recurrence. We collected tumor and normal tissue gene expression profiles of recurrent BC patients and non-recurrent BC patients from the TCGA database.We derived an expression interval (mean ± 1.96SD) based on non-recurrent patients rather than a single value, such as a mean or median. If the expression of a gene was significantly different from its normal expression interval, it was considered a differentially expressed gene. Eight pathways that significantly distinguished recurrent and non-recurrent BC patients were obtained based on 65% accuracy, and these pathways were all associated with the immune response and sensitivity to drugs. The genes in these eight pathways were also used to analyze survival, and the significance level reached 0.003 in an independent dataset ( p = 0.02 in tumor and p = 0.03 in normal tissue). Our results reveal that the integration of tumor and normal tissue functional analyses can comprehensively enhance the understanding of BC prognosis.

Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.