The within-sample relative expression orderings (REOs) of genes, which are stable qualitative transcriptional characteristics, can provide abundant information for a disease. Methods based on REO comparisons have been proposed for identifying differentially expressed genes (DEGs) at the individual level and for detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. Here, we evaluated the effects of common potential confounding factors, including age, cigarette smoking, sex, and race, on the REOs of gene pairs within normal lung tissues transcriptome. Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in nonsmokers’ lung tissues are reversed in smokers’ lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR <0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism, and other pathways (hypergeometric test, FDR <0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR <0.05) which introduced a few reversal REOs between the two races. In summary, the REO-based study should take into account the confounding factors of cigarette smoking, sex, and race.
Abstract FOLFOX (5‐fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within‐sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F ‐score in predicting pathological response of CRC to FOLFOX. The REO‐based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F ‐score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F ‐score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression‐free survival than the predicted non‐responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5‐fluorouracil‐based signatures. In conclusion, the REO‐based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen.
Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair ( TOP2A , SLC7A5 ; NMU , PDSS1 ) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07); this signature was verified using an independent dataset (logrank p = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.
// Haidan Yan 1, * , Jun He 1, * , Qingzhou Guan 1 , Hao Cai 1 , Lin Zhang 4 , Weicheng Zheng 1 , Lishuang Qi 2 , Suyun Zhang 3 , Huaping Liu 1 , Hongdong Li 1 , Wenyuan Zhao 2 , Sheng Yang 3 and Zheng Guo 1, 2 1 Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China 2 Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China 3 Department of Medical Oncology, Fujian Medical University Union Hospital, Fuzhou, China 4 Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China * These authors contributed equally to this work Correspondence to: Zheng Guo, email: guoz@ems.hrbmu.edu.cn Sheng Yang, email: yangshjxh@163.com Keywords: colorectal cancer, DNA methylation, relative methylation level orderings, differentially methylated CpG sites, biomarkers Received: September 23, 2016 Accepted: April 21, 2017 Published: May 07, 2017 ABSTRACT A big challenge to clinical diagnosis and therapy of colorectal cancer (CRC) is its extreme heterogeneity, and thus it would be of special importance if we could find common biomarkers besides subtype-specific biomarkers for CRC. Here, with DNA methylation data produced by different laboratories, we firstly revealed that the relative methylation-level orderings (RMOs) of CpG sites within colorectal normal tissues are highly stable but widely disrupted in the CRC tissues. This finding provides the basis for using the RankComp algorithm to identify differentially methylated (DM) CpG sites in every individual CRC sample through comparing the RMOs within the individual sample with the stable RMOs predetermined in normal tissues. For 75 CRC samples, RankComp detected averagely 4,062 DM CpG sites per sample and reached an average precision of 91.34% in terms that the hypermethylation or hypomethylation states of the DM CpG sites detected for each cancer sample were consistent with the observed differences between this cancer sample and its paired adjacent normal sample. Finally, we applied RankComp to identify DM CpG sites for each of the 268 CRC samples from The Cancer Genome Atlas and found 26 and 143 genes whose promoter regions included CpG sites that were hypermethylated and hypomethylated, respectively, in more than 95% of the 268 CRC samples. Individualized pathway analysis identified six pathways that were significantly enriched with DM genes in more than 90% of the CRC tissues. These universal DNA methylation biomarkers could be important diagnostic makers and therapy targets for CRC.
Vascular endothelial cells maintain the vascular integrity and play a vital role in acting as an intravascular and extravascular barrier. Pyroptosis, discovered in recent years, is a new form of programmed cell death accompanied by inflammatory reaction and immune response. Sigma 1R，distributed in different cells, is an opioid receptor and shows multifunctional features. However, the role of sigma 1R in COPD remains obscure. This project investigates the pyroptosis occurrence of endothelium and pathogenesis in COPD induced by cigarette smoke extract (CSE). Our results indicate that CSE increased the expression of sigma 1R, promoted the endothelial pyroptosis, and reduced the cell viability in endothelial cells. Meanwhile, CSE induced the increased expression of autophagy proteins and NO in cells. Inhibition of sigma 1R through BD1047 decreased the pyroptosis, and reduced the expression levels of autophagy proteins and NO. Treatment of endothelial cells with inhibitor of autophagy and NO could suppress endothelial pyroptosis, and restore the declining viability caused by CSE. Moreover, inhibition of autophagy decreased the NO level. Our results elucidated a link between CSE-induced pyroptosis and sigma1R in endothelial cells. These findings reveal the pathogenesis of COPD to provide new insights for further study and drug development for COPD.
Abstract Background and Aim Differentially expressed (DE) genes detected at the population‐level through case–control comparison provide no information on the dysregulation frequencies of DE genes in a cancer. In this work, we aimed to identify the genes with universally upregulated or downregulated expressions in colorectal cancer (CRC). Methods We firstly clarified that DE genes in an individual cancer tissue should be the disease‐relevant genes, which are dysregulated in the cancer tissue in comparison with its own previously normal state. Then, we identified DE genes at the individual level for 2233 CRC samples collected from multiple data sources using the RankComp algorithm. Results We found 26 genes that were upregulated or downregulated in almost all the 2233 CRC samples and validated the results using 124 CRC tissues with paired adjacent normal tissues. Especially, we found that two genes ( AJUBA and EGFL6 ), upregulated universally in CRC tissues, were extremely lowly expressed in normal colorectal tissues, which were considered to be oncogenes in CRC oncogenesis and development. Oppositely, we found that one gene ( LPAR1 ), downregulated universally in CRC tissues, was silenced in CRC tissues but highly expressed in normal colorectal tissues, which were considered to be tumor suppressor genes in CRC. Functional evidences revealed that these three genes may induce CRC through deregulating pathways for ribosome biogenesis, cell proliferation, and cell cycle. Conclusions In conclusion, the individual‐level DE genes analysis can help us find genes dysregulated universally in CRC tissues, which may be important diagnostic biomarkers and therapy targets.
Due to the heterogeneity of cancer, identifying differentially methylated (DM) CpG sites between a set of cancer samples and a set of normal samples cannot tell us which patients have methylation aberrations in a particular DM CpG site.We firstly showed that the relative methylation-level orderings (RMOs) of CpG sites within individual normal lung tissues are highly stable but widely disrupted in lung adenocarcinoma tissues. This finding provides the basis of using the RankComp algorithm, previously developed for differential gene expression analysis at the individual level, to identify DM CpG sites in each cancer tissue compared with its own normal state. Briefly, through comparing with the highly stable normal RMOs predetermined in a large collection of samples for normal lung tissues, the algorithm finds those CpG sites whose hyper- or hypo-methylations may lead to the disrupted RMOs of CpG site pairs within a disease sample based on Fisher's exact test.Evaluated in 59 lung adenocarcinoma tissues with paired adjacent normal tissues, RankComp reached an average precision of 94.26% for individual-level DM CpG sites. Then, after identifying DM CpG sites in each of the 539 lung adenocarcinoma samples from TCGA, we found five and 44 CpG sites hypermethylated and hypomethylated in above 90% of the disease samples, respectively. These findings were validated in 140 publicly available and eight additionally measured paired cancer-normal samples. Gene expression analysis revealed that four of the five genes, HOXA9, TAL1, ATP8A2, ENG and SPARCL1, each harboring one of the five frequently hypermethylated CpG sites within its promoters, were also frequently down-regulated in lung adenocarcinoma.The common DNA methylation aberrations in lung adenocarcinoma tissues may be important for lung adenocarcinoma diagnosis and therapy.
Background: Previously reported transcriptional signatures for predicting the prognosis of stage I-III bladder cancer (BLCA) patients after surgical resection are commonly based on risk scores summarized from quantitative measurements of gene expression levels, which are highly sensitive to the measurement variation and sample quality and thus hardly applicable under clinical settings. It is necessary to develop a signature which can robustly predict recurrence risk of BLCA patients after surgical resection. Methods: The signature is developed based on the within-sample relative expression orderings (REOs) of genes, which are qualitative transcriptional characteristics of the samples. Results: A signature consisting of 12 gene pairs (12-GPS) was identified in training data with 158 samples. In the first validation dataset with 114 samples, the low-risk group of 54 patients had a significantly better overall survival than the high-risk group of 60 patients (HR = 3.59, 95% CI: 1.34~9.62, p = 6.61 × 10-03). The signature was also validated in the second validation dataset with 57 samples (HR = 2.75 × 1008, 95% CI: 0~Inf, p = 0.05). Comparison analysis showed that the transcriptional differences between the low- and high-risk groups were highly reproducible and significantly concordant with DNA methylation differences between the two groups. Conclusions: The 12-GPS signature can robustly predict the recurrence risk of stage I-III BLCA patients after surgical resection. It can also aid the identification of reproducible transcriptional and epigenomic features characterizing BLCA metastasis.
// Mengsha Tong 1 , Weicheng Zheng 1 , Xingrong Lu 2 , Lu Ao 1 , Xiangyu Li 1 , Qingzhou Guan 1 , Hao Cai 1 , Mengyao Li 1 , Haidan Yan 1 , You Guo 1 , Pan Chi 2 , Zheng Guo 1 1 Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China 2 Department of Colorectal & Anal Surgery, Affiliated Union Hospital of Fujian Medical University, Fuzhou, China Correspondence to: Zheng Guo, e-mail: guoz@ems.hrbmu.edu.cn Pan Chi, e-mail: chipan@yeah.net Keywords: drug-induced resistant cancer cell lines, drug treatment response, colorectal cancer, 5-fluorouracil, oxaliplatin Received: June 30, 2015 Accepted: September 12, 2015 Published: October 15, 2015 ABSTRACT Until recently, few molecular signatures of drug resistance identified in drug-induced resistant cancer cell models can be translated into clinical practice. Here, we defined differentially expressed genes (DEGs) between pre-chemotherapy colorectal cancer (CRC) tissue samples of non-responders and responders for 5-fluorouracil and oxaliplatin-based therapy as clinically relevant drug resistance genes (CRG 5-FU/L-OHP ). Taking CRG 5-FU/L-OHP as reference, we evaluated the clinical relevance of several types of genes derived from HCT116 CRC cells with resistance to 5-fluorouracil and oxaliplatin, respectively. The results revealed that DEGs between parental and resistant cells, when both were treated with the corresponding drug for a certain time, were significantly consistent with the CRG 5-FU/L-OHP as well as the DEGs between the post-chemotherapy CRC specimens of responders and non-responders. This study suggests a novel strategy to extract clinically relevant drug resistance genes from both drug-induced resistant cell models and post-chemotherapy cancer tissue specimens.
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