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Xu C, Xia P, Li J, Lewis KB, Ciombor KK, Wang L, Smith JJ, Beauchamp RD, Chen XS. Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer. Cell Rep Med 2024:101661. [PMID: 39059386 DOI: 10.1016/j.xcrm.2024.101661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/30/2023] [Accepted: 07/02/2024] [Indexed: 07/28/2024]
Abstract
Identifying patients with stage II and III colon cancer who will benefit from 5-fluorouracil (5-FU)-based adjuvant chemotherapy is crucial for the advancement of personalized cancer therapy. We employ a semi-supervised machine learning approach to analyze a large dataset with 933 stage II and III colon cancer samples. Our analysis leverages gene regulatory networks to discover an 18-gene prognostic signature and to explore a 10-gene signature that potentially predicts chemotherapy benefits. The 10-gene signature demonstrates strong prognostic power and shows promising potential to predict chemotherapy benefits. We establish a robust clinical assay on the NanoString nCounter platform, validated in a retrospective formalin-fixed paraffin-embedded (FFPE) cohort, which represents an important step toward clinical application. Our study lays the groundwork for improving adjuvant chemotherapy and potentially expanding into immunotherapy decision-making in colon cancer. Future prospective studies are needed to validate and establish the clinical utility of the 10-gene signature in clinical settings.
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Affiliation(s)
- Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Peng Xia
- School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China
| | - Jie Li
- Academy of Biomedical Engineering, Kunming Medical University, Kunming 650500, China
| | - Keeli B Lewis
- Section of Surgical Sciences, Department of Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kristen K Ciombor
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Lily Wang
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - J Joshua Smith
- Colorectal Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
| | - R Daniel Beauchamp
- Section of Surgical Sciences, Department of Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - X Steven Chen
- Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA; Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
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Shao M, Jiang C, Yu C, Jia H, Wang Y, Mao X. Capecitabine inhibits epithelial‑to‑mesenchymal transition and proliferation of colorectal cancer cells by mediating the RANK/RANKL pathway. Oncol Lett 2022; 23:96. [PMID: 35154427 PMCID: PMC8822391 DOI: 10.3892/ol.2022.13216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 10/06/2021] [Indexed: 11/08/2022] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent malignancy globally. Capecitabine is an important form of chemotherapy for colorectal cancer. The present study aims to investigate the underlying mechanism of action of the drug in CRC cells. In the present study, 50 pairs of CRC and adjacent normal tissues were collected, and CRC cell lines (SW480, SW620, HT29, LOVO and HCT116) and NCM460 colonic epithelial cells were also purchased and used. Western blotting was used to measure the expression levels of proteins involved in the receptor activator of nuclear factor-κB (RANK)/receptor activator of nuclear factor-κB ligand (RANKL) pathway and epithelial-to-mesenchymal transition (EMT), including RANK, RANKL, osteoprotegerin (OPG), E-cadherin, vimentin and N-cadherin. Proliferation and migration were measured using MTT, Cell Counting Kit-8, EdU, Transwell and wound healing assays, respectively. In the present study, it was found that the RANK/RANKL pathway was activated in cancer tissues and cells. Additionally, it was observed that capecitabine treatment reduced the protein expression of RANK, RANKL and OPG in HT29 cells, suggesting that capecitabine has a repressive effect on the RANK/RANKL pathway. Furthermore, functional experiments revealed that the proliferative ability and the EMT process observed in HT29 cells were inhibited after they were treated with capecitabine or transfected with si-RANK. Rescue assays were then performed, which revealed that the promotion of RANK via transfection of cells with 50 nM pcDNA3.1-RANK reversed the inhibitory effects of capecitabine on HT29 cell proliferation and EMT. These findings suggest that the regulatory role of capecitabine is at least partially mediated through the RANK/RANKL pathway in colorectal cancer. The present study demonstrated that capecitabine-induced repression of CRC is exerted by inhibiting the RANK/RANKL pathway, where this new mechanism potentially provides a novel therapeutic target.
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Affiliation(s)
- Minghai Shao
- Department of Radiation Oncology, Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000, P.R. China
| | - Caiping Jiang
- Department of Radiation Oncology, Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000, P.R. China
| | - Changhui Yu
- Department of Radiation Oncology, Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000, P.R. China
| | - Haijian Jia
- Department of Radiation Oncology, Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000, P.R. China
| | - Yanli Wang
- Department of Radiation Oncology, Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000, P.R. China
| | - Xinli Mao
- Department of Gastroenterology, Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang 317000, P.R. China
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You T, Song K, Guo W, Fu Y, Wang K, Zheng H, Yang J, Jin L, Qi L, Guo Z, Zhao W. A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer. Front Genet 2020; 11:971. [PMID: 33193579 PMCID: PMC7658404 DOI: 10.3389/fgene.2020.00971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 07/31/2020] [Indexed: 12/24/2022] Open
Abstract
A part of colorectal cancer which is characterized by simultaneous numerous hypermethylation CpG islands sites is defined as CpG island methylator phenotype (CIMP) status. Stage II and III CIMP−positive (CIMP+) right-sided colon cancer (RCC) patients have a better prognosis than CIMP−negative (CIMP−) RCC treated with surgery alone. However, there is no gold standard available in defining CIMP status. In this work, we selected the gene pairs whose relative expression orderings (REOs) were associated with the CIMP status, to develop a qualitative transcriptional signature to individually predict CIMP status for stage II and III RCC. Based on the REOs of gene pairs, a signature composed of 19 gene pairs was developed to predict the CIMP status of RCC through a feature selection process. A sample is predicted as CIMP+ when the gene expression orderings of at least 12 gene pairs vote for CIMP+; otherwise the CIMP−. The difference of prognosis between the predicted CIMP+ and CIMP− groups was more significantly different than the original CIMP status groups. There were more differential methylation and expression characteristics between the two predicted groups. The hierarchical clustering analysis showed that the signature could perform better for predicting CIMP status of RCC than current methods. In conclusion, the qualitative transcriptional signature for classifying CIMP status at the individualized level can predict outcome and guide therapy for RCC patients.
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Affiliation(s)
- Tianyi You
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenbing Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yelin Fu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Kai Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hailong Zheng
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jing Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liangliang Jin
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Fujian Provincial Key Laboratory on Hematology, Fujian Medical University, Fuzhou, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Zhang ZM, Wang JS, Zulfiqar H, Lv H, Dao FY, Lin H. Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method. Front Cell Dev Biol 2020; 8:582864. [PMID: 33178697 PMCID: PMC7593596 DOI: 10.3389/fcell.2020.582864] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 09/15/2020] [Indexed: 12/16/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients' survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non-PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jia-Shu Wang
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lv
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center for Informational Biology, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
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The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6418460. [PMID: 32802863 PMCID: PMC7424369 DOI: 10.1155/2020/6418460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/29/2020] [Indexed: 11/18/2022]
Abstract
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.
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Chen T, Zhao Z, Chen B, Wang Y, Yang F, Wang C, Dong Q, Liu Y, Liang H, Zhao W, Qi L, Xu Y, Gu Y. An individualized transcriptional signature to predict the epithelial-mesenchymal transition based on relative expression ordering. Aging (Albany NY) 2020; 12:13172-13186. [PMID: 32639951 PMCID: PMC7377874 DOI: 10.18632/aging.103407] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/25/2020] [Indexed: 12/14/2022]
Abstract
The epithelial-mesenchymal transition (EMT) process is involved in cancer cell metastasis and immune system activation. Hence, identification of gene expression signatures capable of predicting the EMT status of cancer cells is essential for development of therapeutic strategies. However, quantitative identification of EMT markers is limited by batch effects, the platform used, or normalization methods. We hypothesized that a set of EMT-related relative expression orderings are highly stable in epithelial samples yet are reversed in mesenchymal samples. To test this hypothesis, we analyzed transcriptome data for ovarian cancer cohorts from publicly available databases, to develop a qualitative 16-gene pair signature (16-GPS) that effectively distinguishes the mesenchymal from epithelial phenotype. Our method was superior to previous quantitative methods in terms of classification accuracy and applicability to individualized patients without requiring data normalization. Patients with mesenchymal-like ovarian cancer showed poorer overall survival compared to patients with epithelial-like ovarian cancer. Additionally, EMT score was positively correlated with expression of immune checkpoint genes and metastasis. We, therefore, established a robust EMT 16-GPS that is independent of detection platform, batch effects and individual variations, and which represents a qualitative signature for investigating the EMT and providing insights into immunotherapy for ovarian cancer patients.
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Affiliation(s)
- Tingting Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhangxiang Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Bo Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yuquan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Fan Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chengyu Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qi Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yaoyao Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haihai Liang
- College of Pharmacy, Harbin Medical University, Harbin, China
| | - Wenyuan Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lishuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yunyan Gu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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Wang K, Song K, Ma Z, Yao Y, Liu C, Yang J, Xiao H, Zhang J, Zhang Y, Zhao W. Identification of EMT-related high-risk stage II colorectal cancer and characterisation of metastasis-related genes. Br J Cancer 2020; 123:410-417. [PMID: 32435058 PMCID: PMC7403418 DOI: 10.1038/s41416-020-0902-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/25/2020] [Accepted: 05/01/2020] [Indexed: 11/09/2022] Open
Abstract
Background Our laboratory previously reported an individual-level prognostic signature for patients with stage II colorectal cancer (CRC). However, this signature was not applicable for RNA-sequencing datasets. In this study, we constructed a robust epithelial-to-mesenchymal transition (EMT)- related gene pair prognostic signature. Methods Based on EMT-related genes, metastasis-associated gene pairs were identified between metastatic and non-metastatic samples. Then, we selected prognosis-associated gene pairs, which were significantly correlated with disease-free survival of stage II CRC using multivariate Cox regression model, as the EMT-related prognosis signature. Results An EMT-related signature composed of fifty-one gene pairs (51-GPS) for prediction-relapse risk of patients with stage II CRC was developed, whose prognostic efficiency was validated in independent datasets. Moreover, 51-GPS achieved better predictive performance than other reported signatures, including a commercial signature Oncotype Dx colon cancer and an immune-related gene pair signature. Besides, EMT-related functional gene sets achieved high enrichment scores in high-risk samples. Especially, loss-of-function antisense approach showed that DEGs between the predicted two clusters were metastasis-related. Conclusions The EMT-related gene pair signature can identify the high relapse-risk patients with stage II CRC, which can facilitate individualised management of patients.
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Affiliation(s)
- Kai Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Zhigang Ma
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, 150001, China
| | - Yang Yao
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, 150001, China
| | - Chao Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, 150001, China
| | - Jing Yang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Huiting Xiao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Jiashuai Zhang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yanqiao Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No. 150, Haping Road, Nangang District, Harbin, 150001, China.
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
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Li X, Huang H, Zhang J, Jiang F, Guo Y, Shi Y, Guo Z, Ao L. A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy. Prostate 2020; 80:376-387. [PMID: 31961962 PMCID: PMC7065139 DOI: 10.1002/pros.23952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 01/02/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND The qualitative transcriptional characteristics, the within-sample relative expression orderings (REOs) of genes, are highly robust against batch effects and sample quality variations. Hence, we develop a qualitative transcriptional signature based on REOs to predict the biochemical recurrence risk of prostate cancer (PCa) patients after radical prostatectomy. METHODS Gene pairs with REOs significantly correlated with the biochemical recurrence-free survival (BFS) were identified from 131 PCa samples in the training data set. From these gene pairs, we selected a qualitative transcriptional signature based on the within-sample REOs of gene pairs which could predict the recurrence risk of PCa patients after radical prostatectomy. RESULTS A signature consisting of 74 gene pairs, named 74-GPS, was developed for predicting the recurrence risk of PCa patients after radical prostatectomy based on the majority voting rule that a sample was assigned as high risk when at least 37 gene pairs of the 74-GPS voted for high risk; otherwise, low risk. The signature was validated in six independent datasets produced by different platforms. In each of the validation datasets, the Kaplan-Meier survival analysis showed that the average BFS of the low-risk group was significantly better than that of the high-risk group. Analyses of multiomics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups. CONCLUSIONS The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multiomics characteristics. Hence, 74-GPS may serve as a helpful tool for guiding the management of PCa patients with radical prostatectomy at the individual level.
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Affiliation(s)
- Xiang Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
- Key Laboratory of Medical BioinformaticsFujian Medical UniversityFuzhouChina
- Fujian Key Laboratory of Tumor MicrobiologyFujian Medical UniversityFuzhouChina
| | - Haiyan Huang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
| | - Jiahui Zhang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
| | - Fengle Jiang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
| | - Yating Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
| | - Yidan Shi
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
| | - Zheng Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
- Key Laboratory of Medical BioinformaticsFujian Medical UniversityFuzhouChina
- Fujian Key Laboratory of Tumor MicrobiologyFujian Medical UniversityFuzhouChina
| | - Lu Ao
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, The School of Basic Medical SciencesFujian Medical UniversityFuzhouChina
- Key Laboratory of Medical BioinformaticsFujian Medical UniversityFuzhouChina
- Fujian Key Laboratory of Tumor MicrobiologyFujian Medical UniversityFuzhouChina
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Zhang ZM, Tan JX, Wang F, Dao FY, Zhang ZY, Lin H. Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method. Front Bioeng Biotechnol 2020; 8:254. [PMID: 32292778 PMCID: PMC7122481 DOI: 10.3389/fbioe.2020.00254] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/12/2020] [Indexed: 12/18/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancer-related death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved “11-gene-pair” which could produce outstanding results. We further investigated the discriminate capability of the “11-gene-pair” for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiu-Xin Tan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Fu Y, Qi L, Guo W, Jin L, Song K, You T, Zhang S, Gu Y, Zhao W, Guo Z. A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer. BMC Genomics 2019; 20:769. [PMID: 31646964 PMCID: PMC6813057 DOI: 10.1186/s12864-019-6129-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 09/23/2019] [Indexed: 12/16/2022] Open
Abstract
Background Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples. Results Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS. Conclusions The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.
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Affiliation(s)
- Yelin Fu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Wenbing Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Liangliang Jin
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Tianyi You
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Shuobo Zhang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. .,Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China. .,Key Laboratory of Medical Bioinformatics, Fujian Province, Fuzhou, 350122, China.
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11
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Li Y, Zhang H, Guo Y, Cai H, Li X, He J, Lai HM, Guan Q, Wang X, Guo Z. A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection. Front Oncol 2019; 9:629. [PMID: 31355144 PMCID: PMC6635465 DOI: 10.3389/fonc.2019.00629] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 06/25/2019] [Indexed: 01/26/2023] Open
Abstract
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.
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Affiliation(s)
- Yawei Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Huarong Zhang
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Hao Cai
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xiangyu Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jun He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Hung-Ming Lai
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Qingzhou Guan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Xianlong Wang
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Key Laboratory of Medical Bioinformatics, Fujian Medical University, Fuzhou, China
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12
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Song K, Guo Y, Wang X, Cai H, Zheng W, Li N, Song X, Ao L, Guo Z, Zhao W. Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit. FASEB J 2018; 33:151-162. [PMID: 29957060 DOI: 10.1096/fj.201800222rrr] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The current study suggests that the identification of predictive signatures of fluorouracil (5-FU) response for stage II and III colorectal cancer (CRC) could be confounded by chemotherapy-irrelevant low relapse risk. Using the samples of patients with stage II and III CRC who were treated with curative surgery only, we identified a signature with which to predict chemotherapy-irrelevant relapse risk for patients after curative surgery. By applying this signature to the samples of patients with stage II and III CRC who were treated with 5-FU-based adjuvant chemotherapy (ACT) after surgery, we predicted the relapse risk if treated with surgery only. From high-risk samples, we further identified another signature with which to predict therapeutic benefit from 5-FU-based ACT. On the basis of the relative expression orderings of gene pairs, a postsurgery relapse risk signature that consisted of 44 gene pairs was developed and verified in 3 independent data sets. A 5-FU therapeutic benefit signature that consisted of 4 gene pairs was then developed to predict the response of 5-FU-based ACT for those patients with high relapse risk after curative surgery. The signature was verified in 4 independent datasets. For patients with stage II and III CRC, the coupled signatures can first identify patients with high relapse risk after curative surgery, then predict therapeutic benefit from 5-FU-based ACT.-Song, K., Guo, Y., Wang, X., Cai, H., Zheng, W., Li, N., Song, X., Ao, L., Guo, Z., Zhao, W. Transcriptional signatures for coupled predictions of stage II and III colorectal cancer metastasis and fluorouracil-based adjuvant chemotherapy benefit.
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Affiliation(s)
- Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - You Guo
- First Affiliated Hospital, Gannan Medical University, Ganzhou, Jiangxi
| | - Xianlong Wang
- Key Laboratory of the Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Hao Cai
- Key Laboratory of the Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Weicheng Zheng
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of the Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Na Li
- Key Laboratory of the Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Xuekun Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lu Ao
- Key Laboratory of the Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Key Laboratory of the Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of the Tumor Microbiology, Fujian Medical University, Fuzhou, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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13
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Circumvent the uncertainty in the applications of transcriptional signatures to tumor tissues sampled from different tumor sites. Oncotarget 2018; 8:30265-30275. [PMID: 28427173 PMCID: PMC5444741 DOI: 10.18632/oncotarget.15754] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 01/30/2017] [Indexed: 11/25/2022] Open
Abstract
The expression measurements of thousands of genes are correlated with the proportions of tumor epithelial cell (PTEC) in clinical samples. Thus, for a tumor diagnostic or prognostic signature based on a summarization of expression levels of the signature genes, the risk score for a patient may dependent on the tumor tissues sampled from different tumor sites with diverse PTEC for the same patient. Here, we proposed that the within-samples relative expression orderings (REOs) based gene pairs signatures should be insensitive to PTEC variations. Firstly, by analysis of paired tumor epithelial cell and stromal cell microdissected samples from 27 cancer patients, we showed that above 80% of gene pairs had consistent REOs between the two cells, indicating these REOs would be independent of PTEC in cancer tissues. Then, by simulating tumor tissues with different PTEC using each of the 27 paired samples, we showed that about 90% REOs of gene pairs in tumor epithelial cells were maintained in tumor samples even when PTEC decreased to 30%. Especially, the REOs of gene pairs with larger expression differences in tumor epithelial cells tend to be more robust against PTEC variations. Finally, as a case study, we developed a gene pair signature which could robustly distinguish colorectal cancer tissues with various PTEC from normal tissues. We concluded that the REOs-based signatures were robust against PTEC variations.
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14
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Chen R, Guan Q, Cheng J, He J, Liu H, Cai H, Hong G, Zhang J, Li N, Ao L, Guo Z. Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples. Oncotarget 2018; 8:6652-6662. [PMID: 28036264 PMCID: PMC5351660 DOI: 10.18632/oncotarget.14257] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 12/02/2016] [Indexed: 12/19/2022] Open
Abstract
Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.
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Affiliation(s)
- Rou Chen
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Qingzhou Guan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Jun Cheng
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Jun He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Huaping Liu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Hao Cai
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Guini Hong
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Jiahui Zhang
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Na Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Lu Ao
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
| | - Zheng Guo
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
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15
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Guan Q, Yan H, Chen Y, Zheng B, Cai H, He J, Song K, Guo Y, Ao L, Liu H, Zhao W, Wang X, Guo Z. Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer. BMC Genomics 2018; 19:99. [PMID: 29378509 PMCID: PMC5789529 DOI: 10.1186/s12864-018-4446-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/11/2018] [Indexed: 12/20/2022] Open
Abstract
Background Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Results Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Conclusions Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms. Electronic supplementary material The online version of this article (10.1186/s12864-018-4446-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Qingzhou Guan
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Haidan Yan
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Yanhua Chen
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Baotong Zheng
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Hao Cai
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Jun He
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Kai Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - You Guo
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China.,Department of Preventive Medicine, School of Basic Medicine Sciences, Gannan Medical University, Ganzhou, 341000, China
| | - Lu Ao
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Huaping Liu
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China
| | - Wenyuan Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Xianlong Wang
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China.
| | - Zheng Guo
- Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, China. .,Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350122, China. .,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
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16
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Liu H, Li Y, He J, Guan Q, Chen R, Yan H, Zheng W, Song K, Cai H, Guo Y, Wang X, Guo Z. Robust transcriptional signatures for low-input RNA samples based on relative expression orderings. BMC Genomics 2017; 18:913. [PMID: 29179677 PMCID: PMC5704640 DOI: 10.1186/s12864-017-4280-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 11/03/2017] [Indexed: 11/18/2022] Open
Abstract
Background It is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals. Results We compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples. Conclusions REOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree. Electronic supplementary material The online version of this article (10.1186/s12864-017-4280-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Huaping Liu
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.,Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yawei Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Jun He
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Qingzhou Guan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Rou Chen
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Haidan Yan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Weicheng Zheng
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Hao Cai
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - You Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China
| | - Xianlong Wang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
| | - Zheng Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China. .,Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350122, China. .,Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. .,Key Laboratory of Medical bioinformatics, Fujian Province, China.
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17
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Qi L, Li Y, Qin Y, Shi G, Li T, Wang J, Chen L, Gu Y, Zhao W, Guo Z. An individualised signature for predicting response with concordant survival benefit for lung adenocarcinoma patients receiving platinum-based chemotherapy. Br J Cancer 2016; 115:1513-1519. [PMID: 27855439 PMCID: PMC5155365 DOI: 10.1038/bjc.2016.370] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 10/12/2016] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND For lung adenocarcinoma (LUAD) patients receiving platinum-based adjuvant chemotherapy (ACT), predictive signatures extracted from survival data solely are not directly associated with platinum response. Another limitation of reported signatures, commonly based on risk scores summarised from gene expressions, is that they could not be applied directly to samples measured by different laboratories due to experimental batch effects. METHODS Using 60 samples of LUAD patients receiving platinum-based ACT in TCGA, we pre-selected gene pairs whose within-samples relative expression orderings (REOs) were significantly associated with both pathological response and 5-year survival, from which we selected an optimal signature whose within-samples REOs could identify responders with improved 5-year survival rate. RESULTS A predictive signature consisting of three gene pairs was developed. In an independent data set integrated from five small data sets, the predicted responders had a significantly higher 5-year survival rate than the predicted non-responders if and only if they received platinum-based ACT (log-rank P=0.0006). The predicted responders showed a 22% absolute benefit of platinum-based ACT in 5-year survival rate compared with untreated patients (log-rank P=0.0019). CONCLUSIONS The REO-based signature can individually predict response to platinum-based ACT with concordant survival benefit directly for LUAD samples measured by different laboratories.
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Affiliation(s)
- Lishuang Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Yang Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Yuan Qin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Gengen Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Tianhao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Jiasheng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Libin Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Yunyan Gu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Wenyuan Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
| | - Zheng Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou 350001, China
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18
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Tong M, Zheng W, Li H, Li X, Ao L, Shen Y, Liang Q, Li J, Hong G, Yan H, Cai H, Li M, Guan Q, Guo Z. Multi-omics landscapes of colorectal cancer subtypes discriminated by an individualized prognostic signature for 5-fluorouracil-based chemotherapy. Oncogenesis 2016; 5:e242. [PMID: 27429074 PMCID: PMC5399173 DOI: 10.1038/oncsis.2016.51] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 05/27/2016] [Accepted: 06/17/2016] [Indexed: 12/11/2022] Open
Abstract
Until recently, few prognostic signatures for colorectal cancer (CRC) patients receiving 5-fluorouracil (5-FU)-based chemotherapy could be used in clinical practice. Here, using transcriptional profiles for a panel of cancer cell lines and three cohorts of CRC patients, we developed a prognostic signature based on within-sample relative expression orderings (REOs) of six gene pairs for stage II-III CRC patients receiving 5-FU-based chemotherapy. This REO-based signature had the unique advantage of being insensitive to experimental batch effects and free of the impractical data normalization requirement. After stratifying 184 CRC samples with multi-omics data from The Cancer Genome Atlas into two prognostic groups using the REO-based signature, we further revealed that patients with high recurrence risk were characterized by frequent gene copy number aberrations reducing 5-FU efficacy and DNA methylation aberrations inducing distinct transcriptional alternations to confer 5-FU resistance. In contrast, patients with low recurrence risk exhibited deficient mismatch repair and carried frequent gene mutations suppressing cell adhesion. These results reveal the multi-omics landscapes determining prognoses of stage II-III CRC patients receiving 5-FU-based chemotherapy.
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Affiliation(s)
- M Tong
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - W Zheng
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - H Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - X Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - L Ao
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Y Shen
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Q Liang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - J Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - G Hong
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - H Yan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - H Cai
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - M Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Q Guan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
| | - Z Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China
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