DNMT3A governs tyrosine kinase inhibitors responses through IAPs and in a cell senescence-dependent manner in non-small cell lung cancer.

Patients with non-small cell lung cancer (NSCLC) treated with tyrosine kinase inhibitors (TKIs) inevitably exhibit drug resistance, which diminishes therapeutic effects. Nonetheless, the molecular mechanisms of TKI resistance in NSCLC remain obscure. In this study, data from clinical and TCGA databases revealed an increase in DNMT3A expression, which was correlated with a poor prognosis. Using NSCLC organoid models, we observed that high DNMT3A levels reduced TKI susceptibility of NSCLC cells via upregulating inhibitor of apoptosis proteins (IAPs). Simultaneously, the DNMT3Ahigh subset, which escaped apoptosis, underwent an early senescent-like state in a CDKN1A-dependent manner. Furthermore, the cellular senescence induced by TKIs was observed to be reversible, whereas DNMT3Ahigh cells reacquired their proliferative characteristics in the absence of TKIs, resulting in subsequent tumour recurrence and growth. Notably, the blockade of DNMT3A/IAPs signals enhanced the efficacy of TKIs in DNMT3Ahigh tumour-bearing mice, which represented a promising strategy for the effective treatment of NSCLC.

Application and prospect of cutting-edge information technology in biomedical big data.

In recent years, with the development of various high-throughput omics based biological technologies (BT), biomedical research began to enter the era of big data. In the face of high-dimensional, multi-domain and multi-modal biomedical big data, scientific research requires a new paradigm of data intensive scientific research. The vigorous development of cutting-edge information technologies (IT) such as cloud computing, blockchain and artificial intelligence provides technical means for the practice of this new research paradigm. Here,we describe the application of such cutting-edge information technologies in biomedical big data, and propose a forward-looking prospect for the construction of a new paradigm supporting environment for data intensive scientific research. We expect to establish a new research scheme and new scientific research paradigm integrating BT & IT technology, which can finally promote the great leap forward development of biomedical research.

Pan-cancer network disorders revealed by overall and local signaling entropy.

Tumor development is a process involving loss of the differentiation phenotype and acquisition of stem-like characteristics, which is driven by intracellular rewiring of signaling network. The measurement of network reprogramming and disorder would be challenging due to the complexity and heterogeneity of tumors. Here, we proposed signaling entropy (SR) to assess the degree of tumor network disorder. We calculated SR for 33 tumor types in The Cancer Genome Atlas database based on transcriptomic and proteomic data. The SR of tumors was significantly higher than that of normal samples and was highly correlated with cell stemness, cancer type, tumor grade, and metastasis. We further demonstrated the sensitivity and accuracy of using local SR in prognosis prediction and drug response evaluation. Overall, SR could reveal cancer network disorders related to tumor malignant potency, clinical prognosis, and drug response.

DysRegSig: an R package for identifying gene dysregulations and building mechanistic signatures in cancer.

SUMMARY: Dysfunctional regulations of gene expression programs relevant to fundamental cell processes can drive carcinogenesis. Therefore, systematically identifying dysregulation events is an effective path for understanding carcinogenesis and provides insightful clues to build predictive signatures with mechanistic interpretability for cancer precision medicine. Here, we implemented a machine learning-based gene dysregulation analysis framework in an R package, DysRegSig, which is capable of exploring gene dysregulations from high-dimensional data and building mechanistic signature based on gene dysregulations. DysRegSig can serve as an easy-to-use tool to facilitate gene dysregulation analysis and follow-up analysis. AVAILABILITY AND IMPLEMENTATION: The source code and user's guide of DysRegSig are freely available at Github: https://github.com/SCBIT-YYLab/DysRegSig. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrated Omics of Metastatic Colorectal Cancer.

We integrate the genomics, proteomics, and phosphoproteomics of 480 clinical tissues from 146 patients in a Chinese colorectal cancer (CRC) cohort, among which 70 had metastatic CRC (mCRC). Proteomic profiling differentiates three CRC subtypes characterized by distinct clinical prognosis and molecular signatures. Proteomic and phosphoproteomic profiling of primary tumors alone successfully distinguishes cases with metastasis. Metastatic tissues exhibit high similarities with primary tumors at the genetic but not the proteomic level, and kinase network analysis reveals significant heterogeneity between primary colorectal tumors and their liver metastases. In vivo xenograft-based drug tests using 31 primary and metastatic tumors show personalized responses, which could also be predicted by kinase-substrate network analysis no matter whether tumors carry mutations in the drug-targeted genes. Our study provides a valuable resource for better understanding of mCRC and has potential for clinical application.

Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer.

The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA.

BACKGROUND: Hybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelic-fraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based 'liquid biopsy'. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations. RESULTS: Based on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted. CONCLUSIONS: Due to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.

Establishment of a hepatocellular carcinoma patient-derived xenograft platform and its application in biomarker identification.

Using a method optimized in hepatocellular carcinoma (HCC), we established patient-derived xenograft (PDX) models with an increased take rate (42.2%) and demonstrated that FBS +10% dimethyl sulfoxide exhibited the highest tumor take rate efficacy. Among 254 HCC patients, 103 stably transplantable xenograft lines that could be serially passaged, cryopreserved and revived were established. These lines maintained the diversity of HCC and the essential features of the original specimens at the histological, transcriptome, proteomic and genomic levels. Tumor engraftment was associated with lack of encapsulation, poor tumor differentiation, large size and overexpression of cancer stem cell biomarkers, and was an independent predictor for overall survival and tumor recurrence after resection. To confirm the preclinical value of the PDX model in HCC treatment, several antitumor agents were tested in 16 selected PDX models. The results revealed a high degree of pharmacologic heterogeneity in the cohort, as well as heterogeneity to different agents in the same individual. The sorafenib responses observed between HCC patients and the corresponding PDXs were also consistent. After molecular characterization of the PDX models, we explored the predictive markers for sorafenib response and found that mitogen-activated protein kinase kinase kinase 1 (MAP3K1) might play an important role in sorafenib resistance and sorafenib response is impaired in patients with MAP3K1 downexpression. Our results indicated that PDX models could accurately reproduce patient tumors biology and could aid in the discovery of new treatments to advance in precision medicine.

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models.

BACKGROUND: One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform. RESULTS: In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated. CONCLUSION: DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.

A novel mutation KCNQ1p.Thr312del is responsible for long QT syndrome type 1.

Patients with high-risk long QT syndrome (LQTS) mutations may experience life-threatening cardiac events. The present study sought to characterize a novel pathogenic mutation, KCNQ1p.Thr312del, in a Chinese LQT1 family. Clinical and genetic analyses were performed to identify this novel causative gene mutation in this LQTS family. Autosomal dominant inheritance of KCNQ1p.T312del was demonstrated in the three-generation pedigree. All mutation carriers presented with prolonged QT intervals and experienced recurrent syncope during exercise or emotional stress. The functional consequences of the mutant channel were investigated by computer homology modeling as well as whole-cell patch-clamp, western-blot and co-immunoprecipitation techniques using transfected mammalian cells. T312 is in the selectivity filter (SF) of the pore region of the KCNQ1-encoded channel. Homology modeling suggested that secondary structure was altered in the mutant SF compared with the wild-type (WT) SF. There were no significant differences in Kv7.1 expression, membrane trafficking or physical interactions with KCNE1-encoded subunits between the WT and mutant transfected channels. However, the KCNQ1p.T312del channels expressed in transfected cells were non-functional in the absence or presence of auxiliary KCNE1-subunits. Dominant-negative suppression of current density and decelerated activation kinetics were observed in cells expressing KCNQ1WT and KCNQ1p.T312del combined with KCNE1 (KCNQ1WT/p.T312del + KCNE1 channels). Those electrophysiological characteristics underlie the pathogenesis of this novel mutation and also suggest a high risk of cardiac events in patients carrying KCNQ1p.T312del. Although protein kinase A-dependent current increase was preserved, a significant suppression of rate-dependent current facilitation was noted in the KCNQ1WT/p.T312del + KCNE1 channels compared to the WT channels during 1- and 2-Hz stimulation, which was consistent with the patients' phenotype being triggered by exercise. Overall, KCNQ1p.Thr312del induces a loss of function in channel electrophysiology, and it is a high-risk mutation responsible for LQT1.

A comparison of next-generation sequencing analysis methods for cancer xenograft samples.

The application of next-generation sequencing (NGS) technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft (PDX) models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.

PDXliver: a database of liver cancer patient derived xenograft mouse models.

BACKGROUND: Liver cancer is the second leading cause of cancer-related deaths and characterized by heterogeneity and drug resistance. Patient-derived xenograft (PDX) models have been widely used in cancer research because they reproduce the characteristics of original tumors. However, the current studies of liver cancer PDX mice are scattered and the number of available PDX models are too small to represent the heterogeneity of liver cancer patients. To improve this situation and to complement available PDX models related resources, here we constructed a comprehensive database, PDXliver, to integrate and analyze liver cancer PDX models. DESCRIPTION: Currently, PDXliver contains 116 PDX models from Chinese liver cancer patients, 51 of them were established by the in-house PDX platform and others were curated from the public literatures. These models are annotated with complete information, including clinical characteristics of patients, genome-wide expression profiles, germline variations, somatic mutations and copy number alterations. Analysis of expression subtypes and mutated genes show that PDXliver represents the diversity of human patients. Another feature of PDXliver is storing drug response data of PDX mice, which makes it possible to explore the association between molecular profiles and drug sensitivity. All data can be accessed via the Browse and Search pages. Additionally, two tools are provided to interactively visualize the omics data of selected PDXs or to compare two groups of PDXs. CONCLUSION: As far as we known, PDXliver is the first public database of liver cancer PDX models. We hope that this comprehensive resource will accelerate the utility of PDX models and facilitate liver cancer research. The PDXliver database is freely available online at: http://www.picb.ac.cn/PDXliver/.

Differential networking meta-analysis of gastric cancer across Asian and American racial groups.

BACKGROUND: Gastric Carcinoma is one of the most lethal cancer around the world, and is also the most common cancers in Eastern Asia. A lot of differentially expressed genes have been detected as being associated with Gastric Carcinoma (GC) progression, however, little is known about the underlying dysfunctional regulation mechanisms. To address this problem, we previously developed a differential networking approach that is characterized by involving differential coexpression analysis (DCEA), stage-specific gene regulatory network (GRN) modelling and differential regulation networking (DRN) analysis. RESULT: In order to implement differential networking meta-analysis, we developed a novel framework which integrated the following steps. Considering the complexity and diversity of gastric carcinogenesis, we first collected three datasets (GSE54129, GSE24375 and TCGA-STAD) for Chinese, Korean and American, and aimed to investigate the common dysregulation mechanisms of gastric carcinogenesis across racial groups. Then, we constructed conditional GRNs for gastric cancer corresponding to normal and carcinoma, and prioritized differentially regulated genes (DRGs) and gene links (DRLs) from three datasets separately by using our previously developed differential networking method. Based on our integrated differential regulation information from three datasets and prior knowledge (e.g., transcription factor (TF)-target regulatory relationships and known signaling pathways), we eventually generated testable hypotheses on the regulation mechanisms of two genes, XBP1 and GIF, out of 16 common cross-racial DRGs in gastric carcinogenesis. CONCLUSION: The current cross-racial integrative study from the viewpoint of differential regulation networking provided useful clues for understanding the common dysfunctional regulation mechanisms of gastric cancer progression and discovering new universal drug targets or biomarkers for gastric cancer.

Whole-exome mutational and transcriptional landscapes of combined hepatocellular cholangiocarcinoma and intrahepatic cholangiocarcinoma reveal molecular diversity.

BACKGROUND: Primary liver cancer (PLC) is the third largest contributor to cancer mortality in the world. PLC is a heterogeneous disease that encompasses several biologically distinct subtypes including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular-cholangiocarcinoma (CHC). CHC is a distinct, albeit rare, subtype of PLC and is comprised of cells with histopathological features of both HCC and ICC. Several studies have focused on the mutation and expression landscapes of HCC and ICC. However, studies of CHC were rare. OBJECTIVE: The aim of the current study was to identify genetic and gene expression alterations in the carcinogenesis and development of CHC and ICC in the Chinese population. Unraveling both similar and differing patterns among these subtypes may help to identify personalized medicine approaches that could improve patient survival. METHODS: Whole genome sequencing (WGS), whole exome sequencing (WES) and RNA-seq were performed on 10 ICC and 10 CHC samples, matched with adjacent non-tumor liver tissue specimens. Comparative analysis was performed using HCC datasets from The Cancer Genome Atlas (TCGA). RESULTS: Mutational and transcriptional landscapes of CHC and ICC were clearly delineated. TP53 and CTNNB1 were identified as exhibiting mutations in CHC. ARID1A, PBRM1, and IDH1 were frequently mutated in ICC. RYR3, FBN2, and KCNN3 are associated with cell migration and metastasis and might be driver genes in CHC. KCNN3 was identified as also exhibiting mutations in ICC. The ECM-receptor interaction pathway associated fibrogenic hepatic progenitor cell differentiation and liver fibrosis may play an important role in carcinogenesis of PLC. Chromatin remodeling and chromosome organization are key processes in carcinogenesis and development in PLC. P53 related pathways showed alterations in CHC and HCC. Inflammation may be a key factor involved in ICC carcinogenesis. CONCLUSION: CHC and ICC are different subtypes of PLC. This study discusses predominantly the molecular genetic details of PLC subtypes and highlights the need for an accurate diagnosis and treatment of specific PLC subtypes to optimize patient management.

The mTOR inhibitor AZD8055 overcomes tamoxifen resistance in breast cancer cells by down-regulating HSPB8.

Tamoxifen, an important endocrine therapeutic agent, is widely used for the treatment of estrogen receptor positive (ER+) breast cancer. However, de novo or acquired resistance prevents patients from benefitting from endocrine approaches and necessitates alternative treatments. In this study, we report that small heat protein beta-8 (HSPB8) may serve as an important molecule in tamoxifen resistance. HSPB8 expression is enhanced in MCF-7 cells resistant to tamoxifen (MCF-7/R) compared to parent cells. Moreover, high expression of HSPB8 associates with poor prognosis in ER+ breast cancer patients but not in patients without classification. Stimulating ER signaling by heterogeneous expression of ERa or 17ß-estradiol promotes HSPB8 expression and reduces the cell population in G1 phase. In contrast, blockage of ER signaling by tamoxifen down-regulates the expression of HSPB8. In addition, knocking down HSPB8 by specific siRNAs induces significant cell cycle arrest at G1 phase. AZD8055 was found to be more potent against the proliferation of MCF-7/R cells than that of parent cells, which was associated with down-regulation of HSPB8. We found that the anti-proliferative activity of AZD8055 was positively correlated with the HSPB8 expression level in ER+ breast cancer cells. Thus, AZD8055 was able to overcome tamoxifen resistance in breast cancer cells, and the expression of HSPB8 may predict the efficacy of AZD8055 in ER+ breast cancer. This hypothesis deserves further investigation.

Shanghai Score: A Prognostic and Adjuvant Treatment-evaluating System Constructed for Chinese Patients with Hepatocellular Carcinoma after Curative Resection.

BACKGROUND: For Chinese patients with hepatocellular carcinoma (HCC), surgical resection is the most important treatment to achieve long-term survival for patients with an early-stage tumor, and yet the prognosis after surgery is diverse. We aimed to construct a scoring system (Shanghai Score) for individualized prognosis estimation and adjuvant treatment evaluation. METHODS: A multivariate Cox proportional hazards model was constructed based on 4166 HCC patients undergoing resection during 2001-2008 at Zhongshan Hospital. Age, hepatitis B surface antigen, hepatitis B e antigen, partial thromboplastin time, total bilirubin, alkaline phosphatase, γ-glutamyltransferase, α-fetoprotein, tumor size, cirrhosis, vascular invasion, differentiation, encapsulation, and tumor number were finally retained by a backward step-down selection process with the Akaike information criterion. The Harrell's concordance index (C-index) was used to measure model performance. Shanghai Score is calculated by summing the products of the 14 variable values times each variable's corresponding regression coefficient. Totally 1978 patients from Zhongshan Hospital undergoing resection during 2009-2012, 808 patients from Eastern Hepatobiliary Surgery Hospital during 2008-2010, and 244 patients from Tianjin Medical University Cancer Hospital during 2010-2011 were enrolled as external validation cohorts. Shanghai Score was also implied in evaluating adjuvant treatment choices based on propensity score matching analysis. RESULTS: Shanghai Score showed good calibration and discrimination in postsurgical HCC patients. The bootstrap-corrected C-index (confidence interval [CI]) was 0.74 for overall survival (OS) and 0.68 for recurrence-free survival (RFS) in derivation cohort (4166 patients), and in the three independent validation cohorts, the CI s for OS ranged 0.70-0.72 and that for RFS ranged 0.63-0.68. Furthermore, Shanghai Score provided evaluation for adjuvant treatment choices (transcatheter arterial chemoembolization or interferon-α). The identified subset of patients at low risk could be ideal candidates for curative surgery, and subsets of patients at moderate or high risk could be recommended with possible adjuvant therapies after surgery. Finally, a web server with individualized outcome prediction and treatment recommendation was constructed. CONCLUSIONS: Based on the largest cohort up to date, we established Shanghai Score - an individualized outcome prediction system specifically designed for Chinese HCC patients after surgery. The Shanghai Score web server provides an easily accessible tool to stratify the prognosis of patients undergoing liver resection for HCC.

Differential regulation analysis reveals dysfunctional regulatory mechanism involving transcription factors and microRNAs in gastric carcinogenesis.

Gastric cancer (GC) is one of the most incident malignancies in the world. Although lots of featured genes and microRNAs (miRNAs) have been identified to be associated with gastric carcinogenesis, underlying regulatory mechanisms still remain unclear. In order to explore the dysfunctional mechanisms of GC, we developed a novel approach to identify carcinogenesis relevant regulatory relationships, which is characterized by quantifying the difference of regulatory relationships between stages. Firstly, we applied the strategy of differential coexpression analysis (DCEA) to transcriptomic datasets including paired mRNA and miRNA of gastric samples to identify a set of genes/miRNAs related to gastric cancer progression. Based on these genes/miRNAs, we constructed conditional combinatorial gene regulatory networks (cGRNs) involving both transcription factors (TFs) and miRNAs. Enrichment of known cancer genes/miRNAs and predicted prognostic genes/miRNAs was observed in each cGRN. Then we designed a quantitative method to measure differential regulation level of every regulatory relationship between normal and cancer, and the known cancer genes/miRNAs proved to be ranked significantly higher. Meanwhile, we defined differentially regulated link (DRL) by combining differential regulation, differential expression and the regulation contribution of the regulator to the target. By integrating survival analysis and DRL identification, three master regulators TCF7L1, TCF4, and MEIS1 were identified and testable hypotheses of dysfunctional mechanisms underlying gastric carcinogenesis related to them were generated. The fine-tuning effects of miRNAs were also observed. We propose that this differential regulation network analysis framework is feasible to gain insights into dysregulated mechanisms underlying tumorigenesis and other phenotypic changes.

Integrated Differential Regulatory Analysis Reveals a Novel Prognostic 36-Gene Signature for Gastric Cancer in Asian Population.

AIM AND OBJECTIVE: Gastric cancer is one of the most common cancers and has very high incidence and mortality rate in Asian population. To tackle the problems of infiltration and heterogeneity, more accurate biomarkers for diagnosis and prognosis as well as effective targets for treatment are needed to achieve better outcomes of gastric cancer patients. Recently, methods and algorithms for analyzing high-throughput sequencing data have greatly facilitated the molecular profiling of gastric cancer. Nevertheless, prognostic biomarkers for gastric cancer that can be potentially applied in clinic are still lacking. MATERIALS AND METHODS: In this study, we performed differential regulatory analysis based on gene co-expression network for four different cohorts of Asian gastric cancer samples and their clinical data. RESULTS: We identified a 36-gene prognostic signature specific for gastric cancer, particularly for Asian population. We further analyzed differential regulatory patterns related to these featured genes, such as C1S, and suggested hypotheses for investigating their roles in gastric cancer pathogenesis. CONCLUSION: Findings from present study suggest a 36-gene signature which is based on differential regulatory analysis and can predict the prognosis of gastric cancer. Our research explores molecular mechanism of gastric cancer at transcriptional regulation level and provides potential drug targets. This integrated biomarker searching scheme is extendable to other cancer study for not only prognostic prediction, but also pathogenesis.

A novel integrated gene coexpression analysis approach reveals a prognostic three-transcription-factor signature for glioma molecular subtypes.

BACKGROUND: Glioma is the most common brain tumor and it has very high mortality rate due to its infiltration and heterogeneity. Precise classification of glioma subtype is essential for proper therapeutic treatment and better clinical prognosis. However, the molecular mechanism of glioma is far from clear and the classical classification methods based on traditional morphologic and histopathologic knowledge are subjective and inconsistent. Recently, classification methods based on molecular characteristics are developed with rapid progress of high throughput technology. METHODS: In the present study, we designed a novel integrated gene coexpression analysis approach, which involves differential coexpression and differential regulation analysis (DCEA and DRA), to investigate glioma prognostic biomarkers and molecular subtypes based on six glioma transcriptome data sets. RESULTS: We revealed a novel three-transcription-factor signature including AHR, NFIL3 and ZNF423 for glioma molecular subtypes. This three-TF signature clusters glioma patients into three major subtypes (ZG, NG and IG subtypes) which are significantly different in patient survival as well as transcriptomic patterns. Notably, ZG subtype is featured with higher expression of ZNF423 and has better prognosis with younger age at diagnosis. NG subtype is associated with higher expression of NFIL3 and AHR, and has worse prognosis with elder age at diagnosis. According to our inferred differential networking information and previously reported signalling knowledge, we suggested testable hypotheses on the roles of AHR and NFIL3 in glioma carcinogenesis. CONCLUSIONS: With so far the least biomarkers, our approach not only provides a novel glioma prognostic molecular classification scheme, but also helps to explore its dysregulation mechanisms. Our work is extendable to prognosis-related classification and signature identification in other cancer researches.