Verification of gene expression profiles for colorectal cancer using 12 internet public microarray datasets

Chang, Yu-Tien; Yao, Chung-Tay; Su, Sui-Lung; Chou, Yu‐Ching; Chu, Chi‐Ming; Huang, Cheng-Yang; Terng, Harn-Jing; Chou, Hsiu‐Ling; Wetter, Thomas C.; Chen, K.-H.; Chang, Chi‐Wen; Shih, Yun-Wen; Lai, Ching‐Huang

doi:10.3748/wjg.v20.i46.17476

Cited by 20 publications

(21 citation statements)

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“…In addition, we included microarray datasets obtained from our laboratory and published by Chang et al (2014b) ( , , , , , , , , , , , and ), which included 519 adenocarcinoma cases and 88 normal mucosa control cases.…”

Section: Methodsmentioning

confidence: 99%

“…The use of pooled GEO studies was secondary because only the microarray data were available. Previous studies have proposed that testing 55 genes in any experimental model is beneficial for colon cancer biology ( Chang et al, 2014a ; Chang et al, 2014b ; Chu et al, 2014a ; Chu et al, 2014b ). Therefore, in the present study, functional pathways related to the tumorigenesis of CRC were evaluated using GSEA software version 2.07.…”

Section: Methodsmentioning

confidence: 99%

“…Second, there is a shortage of large-scale studies because of the relatively small number of available patient samples, which leads to reduced statistical power ( Chan et al, 2008 ). Third, identifying data that would be the most informative and useful for the development of reliable clinical applications has been challenging ( Nannini et al, 2009 ; Chang et al, 2014a ; Chang et al, 2014b ; Chu et al, 2014a ; Chu et al, 2014b ).…”

Section: Introductionmentioning

confidence: 99%

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Risk analysis of colorectal cancer incidence by gene expression analysis

Shangkuan

Lin

Chang

et al. 2017

PeerJ

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BackgroundColorectal cancer (CRC) is one of the leading cancers worldwide. Several studies have performed microarray data analyses for cancer classification and prognostic analyses. Microarray assays also enable the identification of gene signatures for molecular characterization and treatment prediction.ObjectiveMicroarray gene expression data from the online Gene Expression Omnibus (GEO) database were used to to distinguish colorectal cancer from normal colon tissue samples.MethodsWe collected microarray data from the GEO database to establish colorectal cancer microarray gene expression datasets for a combined analysis. Using the Prediction Analysis for Microarrays (PAM) method and the GSEA MSigDB resource, we analyzed the 14,698 genes that were identified through an examination of their expression values between normal and tumor tissues.ResultsTen genes (ABCG2, AQP8, SPIB, CA7, CLDN8, SCNN1B, SLC30A10, CD177, PADI2, and TGFBI) were found to be good indicators of the candidate genes that correlate with CRC. From these selected genes, an average of six significant genes were obtained using the PAM method, with an accuracy rate of 95%. The results demonstrate the potential of utilizing a model with the PAM method for data mining. After a detailed review of the published reports, the results confirmed that the screened candidate genes are good indicators for cancer risk analysis using the PAM method.ConclusionsSix genes were selected with 95% accuracy to effectively classify normal and colorectal cancer tissues. We hope that these results will provide the basis for new research projects in clinical practice that aim to rapidly assess colorectal cancer risk using microarray gene expression analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk analysis of colorectal cancer incidence by gene expression analysis

Shangkuan

Lin

Chang

et al. 2017

PeerJ

Self Cite

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“…Cytokines such as IL12A, IL1R1 and IL2RB, which are identified in our gene signature, play a major role as inflammatory mediators and are reported to greatly contribute as one of the factors in CRC development [40]. A study reported a seven-gene expression profile based on 12 gene expression omnibus (GEO) datasets indicated that IL2RB is strongly associated with CRC [41]. Mutations in NKD1 also lead to the activation of Wnt signaling pathways and the promotion of cell proliferation in CRC [33].…”

Section: Discussionmentioning

confidence: 99%

A Nine-Gene Signature for Predicting the Response to Preoperative Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

Park

Yu²,

Mustafa

et al. 2020

Cancers

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Preoperative chemoradiotherapy (PCRT) and subsequent surgery is the standard multimodal treatment for locally advanced rectal cancer (LARC), albeit PCRT response varies among the individuals. This creates a dire necessity to identify a predictive model to forecast treatment response outcomes and identify patients who would benefit from PCRT. In this study, we performed a gene expression study using formalin-fixed paraffin-embedded (FFPE) tumor biopsy samples from 156 LARC patients (training cohort n = 60; validation cohort n = 96); we identified the nine-gene signature (FGFR3, GNA11, H3F3A, IL12A, IL1R1, IL2RB, NKD1, SGK2, and SPRY2) that distinctively differentiated responders from non-responders in the training cohort (accuracy = 86.9%, specificity = 84.8%, sensitivity = 81.5%) as well as in an independent validation cohort (accuracy = 81.0%, specificity = 79.4%, sensitivity = 82.3%). The signature was independent of all pathological and clinical features and was robust in predicting PCRT response. It is readily applicable to the clinical setting using FFPE samples and Food and Drug Administration (FDA) approved hardware and reagents. Predicting the response to PCRT may aid in tailored therapies for respective responders to PCRT and improve the oncologic outcomes for LARC patients.

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“…Expression data of human tumor tissues were downloaded from GSE2109 ( http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2109 ), which consisted of 2158 arrays from solid tumors. When combining datasets from different studies, it is necessary to standardize data to remove batch effect [ 19 , 20 ]. Therefore, we kept genes available on all arrays, and performed the between-study normalization by “ComBat” function in R.…”

Section: Methodsmentioning

confidence: 99%

Integrated Analysis of Transcriptome in Cancer Patient-Derived Xenografts

Zhu

Tang

et al. 2015

PLoS ONE

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Patient-derived xenograft (PDX) tumor model is a powerful technology in evaluating anti-cancer drugs and facilitating personalized medicines. Multiple research centers and commercial companies have put huge efforts into building PDX mouse models. However, PDX models have not been widely available and their molecular features have not been systematically characterized. In this study, we provided a comprehensive survey of PDX transcriptome by integrating analysis of 58 patients involving 8 different tumors. The median correlation coefficient between patients and xenografts is 0.94, which is higher than that between patients and cell line panel or between patients with the same tumor. Major differential gene expressions in PDX occur in the engraftment of human tumor tissue into mice, while gene expressions are relatively stable over passages. 48 genes are frequently differentially expressed in PDX mice of multiple cancers. They are enriched in extracellular matrix and immune response, and some are reported as targets for anticancer drugs. A simulation study showed that expression change between PDX and patient tumor (6%) would result in acceptable change in drug sensitivity (3%). Our findings demonstrate that PDX mice represent the gene-expression and drug-response features of primary tumors effectively, and it is recommended to monitoring the overall expression profiles and drug target genes in clinical application.

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Verification of gene expression profiles for colorectal cancer using 12 internet public microarray datasets

Abstract: A novel gene expression profile was associated with CRC and can potentially be applied to blood-based detection assays.

Cited by 20 publications

References 50 publications

Risk analysis of colorectal cancer incidence by gene expression analysis

Risk analysis of colorectal cancer incidence by gene expression analysis

A Nine-Gene Signature for Predicting the Response to Preoperative Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

Integrated Analysis of Transcriptome in Cancer Patient-Derived Xenografts

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